Meta made an insane number of changes to advertising in 2024, but there is plenty that could be enhanced. This post focuses on specific ways that Meta could further improve the ads product in 2025.

Missing from this list are some of advertisers’ biggest complaints regarding support, ad review, and scams. Those are structural problems without an easy fix.

This list also avoids requests for features that clearly conflict with the current direction of the product. We know that the future of Meta advertising is less control and more automation. The focus here is on finding ways to make that automation better.

In most cases, these are very specific feature requests. I’m no programmer, so I won’t claim to know how easy or difficult it may be to pull them off. But they would improve the product for Meta advertisers.

Let’s get to the list (in no particular order)…

1. Expansion Breakdown

Meta introduced Advantage Detailed Targeting (then called Detailed Targeting Expansion) in 2021. This allowed Meta to expand your audience and reach people beyond the Detailed Targeting inputs if it would result in improved performance.

Many advertisers revolted. I was among this group initially. It was the beginning of our loss of control.

Advantage Detailed Targeting is now on by default and can’t be turned off when the performance goal is to maximize conversions, link clicks, or landing page views. Advantage Lookalike will expand your lookalike audience when maximizing conversions.

Of course, that assumes that you use original audiences. The default targeting approach is now Advantage+ Audience, which treats most of your inputs as suggestions.

While an algorithmic expansion of your audience is not perfect, it is our reality. In 2025, we can expect we’ll further lose targeting control, not gain it back. We need to accept and embrace this.

One way that Meta could improve confidence in audience expansion is by adding a breakdown to reporting. Provide two rows:

• Results from audience that was explicitly targeted from inputs
• Results from audience that was reached beyond targeting inputs

This added transparency can show advertisers how audience expansion is helping them. They may even see that the cost per result is better for the expanded audience. Or not, but this is a necessary breakdown.

Of course, this isn’t a new request. I’ve asked for it from the beginning of audience expansion, and I’ll keep on asking.

2. Audience Segments and Ad Scheduling Availability

Meta added two great new features to manual sales campaigns in 2024…

The addition of Audience Segments was transformative.

It’s because of Audience Segments that I was able to run several tests that changed my opinions about targeting. But, there’s one problem: This feature should be available for all campaign objectives, not just sales.

Another feature added to sales campaigns in 2024 was Ad Scheduling.

Scheduling normally happens on the ad set level, but this allows you to schedule ads individually. This way, you can have ads run within the same ad set based on your promotional schedule.

Once again, it’s a great feature, but it’s only available for sales campaigns. Why?

Both features were originally made available for Advantage+ Shopping Campaigns before rolling out to manual sales campaigns, too. Maybe this is the natural progression and we’ll eventually get access for other objectives.

If not, it feels like an unnecessary restriction. There’s nothing special about sales campaigns that would make these features unique to them. They’d be just as valuable when using any of the other campaign objectives.

Until then, I find creative ways to use the Sales objective even when I don’t optimize for a purchase so that I can get access to Audience Segments. That’s how valuable this feature is.

3. Enhancements to Audience Segments

Audience Segments are awesome. They provide important context to algorithmic targeting by breaking down results into three groups:

• Engaged Audience
• Existing Customers
• New Audience

This helps us see how budget and results are distributed between remarketing and prospecting groups. But it can be improved in three specific ways.

1. Add a layer. Right now, you cannot define Engaged Audience using Facebook Page, Instagram Account, and video view custom audiences.

You can certainly make the argument that these are lower quality than the other custom audiences used to define your Audience Segments. But they do make up your remarketing.

Meta could either add these custom audiences to Engaged Audience or create a new one (“In-App Audience”) to give us additional information about algorithmic remarketing.

2. System generated. Something else Meta could do to make Audience Segments accessible to all advertisers is to auto-generate them initially. Meta has the data to create these without our input.

• Engaged Audience: All pixel activity
• Existing Customers: All purchase events

Customer list custom audiences are more complicated since you may need to segment the purchases from non-purchases, but the pixel gives Meta the initial data to generate these segments for us.

Advertisers could then edit these audience segments as necessary, but an initial definition could help expose more advertisers to the value of this tool.

3. Auto update. It’s not 100% clear if this is an actual problem or if I experienced a bug, but if it’s a problem it needs to be fixed.

I stumbled on an issue where it appears that website custom audiences stop updating if they haven’t been used in targeting recently. This is problematic if Meta wants us to trust algorithmic targeting (not use remarketing audiences) while leveraging audience segments (which rely on those same audiences).

Even if website custom audiences stop updating from a lack of activity (nothing in Meta’s documentation suggests this), it would be a simple fix. Define “activity” to include use in audience segments.

4. Address Advantage+ Audience Weaknesses

I use Advantage+ Audience when optimizing for a purchase, but there is potential for issues with this feature for any other optimization. If Meta can get more of the action that you want by going beyond your suggestions, it will.

That shouldn’t be a problem when optimizing for purchases. If Meta can get you more purchases by ignoring your inputs, that’s a means to an end.

But it can be an issue when optimizing for link clicks, landing page views, ThruPlay, post engagement, and even leads. The issue isn’t that Meta can go beyond your suggestions of custom audiences, lookalike audiences, and detailed targeting. The problem is related to age and gender.

When using Advantage+ Audience, both age maximum and gender are audience suggestions. They are not included in Audience Controls, which act as a tight constraint.

Again, this shouldn’t be an issue when optimizing for purchases. Advertisers can restrict their audience unnecessarily, which drives up costs. You may think that your target audience is women between the ages of 25 and 44, but if a 45-year-old man buys your product (possibly for their partner), that’s a good thing.

But this becomes a problem when optimizing for any other type of action. Let’s assume that you serve female entrepreneurs and 99% of your customers are women. But if you optimize for link clicks, landing page views, or engagement of any kind, your ads will be shown to men. Why? Because they will perform the action that you said you want.

Meta doesn’t care that they won’t eventually buy from you because that’s not the focus of the performance goal you chose. Meta will ignore your audience suggestions of gender and age limit.

I’ve seen this become an issue for leads, too, though it isn’t always. Meta can dedicate a high percentage of my budget on people 55+ because it results in cheap leads. But I’ve also found them to be low quality.

The way around this is to use original audiences, where age maximum and gender are tight constraints. But, this shouldn’t be necessary. Meta could make them part of Audience Controls when using Advantage+ Audience, too — or at least make them available when optimizing for something other than purchases.

5. Address Quality Issues with Optimization

Truthfully, the solution above is a Band-Aid on a much bigger problem. That solution also conflicts with what I said at the top — it’s a request for more control. We’re not going to get that.

The lack of control isn’t the central problem here. The more pressing issue is Meta’s inability to sort out low and high-quality actions. If you optimize for link clicks or landing page views, Meta will do all it can to get as many of them for you as possible. The algorithm is unconcerned about whether they come from accidental clicks or from people who are likely to buy from you.

This is not a new problem (I’ve covered it often), but it is magnified with algorithmic targeting. By removing the guardrails of targeting restrictions, the quality of the actions you get are likely to decrease.

It’s a problem that has solutions, if Meta cares about them…

1. Allow ability to prioritize quality actions. This concept isn’t new to Meta advertising. We already have the option of “maximize number” or “maximize value” when optimizing for purchases.

We could also have options of “maximize number” or “maximize quality” of link clicks, landing page views, post engagement, video views, and more. For example, Meta could prioritize landing page views that resulted in more time spent, return visits, and conversions performed. Quality actions would cost more, but it’s a trade-off most advertisers would take.

2. Change Meta’s signals. Of course, Meta could just update their signals to begin with and prioritize quality actions.

3. Incremental conversions. We know that Incremental Conversions are in testing, and I don’t anticipate that this will apply to many of the actions discussed here. But you could make the argument that Meta could find a way to apply this or a similar concept to top of the funnel actions.

6. Organic Conversion Reporting

It baffles me that this isn’t a thing…

Ads Manager reports on all conversions performed by people you paid to reach. This is logical. But it doesn’t reflect the total impact of your ads.

What happens when someone you paid to reach shares your ad? Someone you did not pay to reach may buy from you. This person would not have converted without the existence of your ad, yet the ad won’t receive any credit.

I’m not suggesting that Meta lump organic in with paid conversions. That would be potentially misleading. But Meta could provide a breakdown of Paid vs. Organic to provide a more complete picture of your ad’s impact.

This could extend beyond Ads Manager, too. If Meta has your pixel and event data, there’s no reason why they couldn’t provide some basic conversion reporting with your organic insights. Instead, all we get is information like impressions and link clicks. Meta could surface the conversions, too.

Once again, this is not a new request. I’ll keep asking it.

7. Address Click Attribution Issue

I stumbled on a troubling discovery in late 2024 that forced me to question what I previously believed to be true: Click attribution doesn’t require a click on an outbound link.

There are two ways Meta can give credit to an ad for a conversion…

Click Attribution: Someone clicked on your ad and converted within seven days.

View Attribution: Someone viewed your ad, did not click, and converted within a day.

Up until very recently, I believed that click attribution required a click on a link to your website. It was logical. If they did not, that could be counted as a view-through conversion.

But since click attribution includes any click, reporting gets fuzzier. Advertisers already have trouble with conversion numbers matching up between Ads Manager and third-party sources. While UTM parameters can help when people click on outbound links, they are worthless for this case.

That’s why I’ve always lumped this type of conversion in with view-through attribution. Your ad may have contributed to the conversion, but the value isn’t as clear as when someone clicks an outbound link and converts.

It took me a decade to realize this, but these lower-quality click conversions are lumped into your click conversions. There’s no way to separate them.

I realize I’m way behind on this request since it’s not a new problem, but there are two things that Meta could do:

1. Move these conversions to view attribution. This solution squares with my initial interpretation of click attribution, so it’s my preference. I believe strongly that these should not be counted among click attributed conversions.

2. Create a separate attribution. This adds complexity, so it’s not ideal. But Meta could conceivably break this type of conversion off into it’s own group: “In-App Click.” That way we could see how many of our click conversions didn’t come from an outbound click. You could even turn it off at the ad set level.

8. A Better, Smarter Event Setup Tool

I can’t tell you exactly when Meta introduced the Event Setup Tool, but it’s been around for several years. It’s also been untouched by enhancements since it’s initial rollout.

It’s still buggy. It’s still painfully limited. But it also holds a ton of promise if Meta chose to focus some development on it.

Meta may need to start over to make this tool more useful, but it would be worth it. It could become the primary way advertisers set up and manage their pixel events.

Right now, Meta relies on third-party integrations for the vast majority of pixel and event management. This creates confusion for advertisers since there isn’t one clear way of managing it. It doesn’t need to be this way.

Meta could develop a smarter, more streamlined and integrated tool that detects events and helps you set them up easily. This also does not need to be limited to standard events — it could help you set up custom events as well.

The Event Setup Tool now is manual, slow, and limited. Instead, Meta could offer an auto-detection of events that you can approve. The technology for auto detection of events already exists.

I’m not a designer or programmer, so forgive me if my vision of how this would work isn’t crystal clear. But this is one of many examples where advertisers are forced to use third-party solutions when it shouldn’t be necessary.

9. Smarter Creative Enhancement By Placement

We’re headed in this direction, but we’re not there yet. It may be the most likely improvement on this list to become a reality.

Currently, advertisers are asked to provide three different versions of ad creative when uploading images and videos.

These different aspect ratios are used for different placements. Of course, this system is imperfect because the sizes Meta requests aren’t always consistent with what is recommended in official documentation.

It would be a whole lot easier if advertisers could upload a single creative that Meta adjusts automatically (and productively) for other placements. While you could approach this level of simplicity with a carefully created 9:16 image or video, this could apply to other dimensions as well.

Submit a square image and Meta uses AI to adjust and generate the background where necessary. This exists now, but it’s in testing and not applied in all situations.

Meta could simplify this by skipping the request for three versions. After submitting a single image or video, Meta then presents the versions that were generated using AI and smart cropping.

While most of my feature requests require new development, this feels more like better presenting and utilizing functionality that already exists.

Your Turn

What other features could Meta develop to improve the ad product in 2025?

Let me know in the comments below!

The post 9 Ways Meta Can Improve Advertising in 2025 appeared first on Jon Loomer Digital.

There was a time when the majority of my ad budget was spent on remarketing in one form or another: Website visitors, email list, followers, post engagement, and more. I’ve abandoned much of this in favor of Meta’s algorithmic targeting, but there are exceptions.

There are times when remarketing continues to make good, smart sense.

Don’t misunderstand my intent. I still think advertisers use remarketing far too often. It’s not only less effective than it once was (and advertisers often misinterpret the effectiveness of their remarketing results), but it’s also often unnecessary.

Let me explain…

Why Remarketing is Mostly Unnecessary

Don’t confuse the message here. Reaching people who are most closely connected to your business remains valuable.

One of the primary reasons that a separate remarketing ad set is mostly unnecessary now is that algorithmic targeting will prioritize these people anyway. When using Advantage+ Audience, Meta prioritizes conversion history, pixel data, and prior engagement with your ads.

You can prove this with the help of audience segments. I’ve seen repeatedly that Meta spends in the range of 25 to 35 percent of my budget on my existing customers and engaged audience (those who are on my email list or have visited my website, but who haven’t yet bought from me).

Here’s an example, using Advantage+ Audience without suggestions…

I’ve also seen this when using original audiences going broad…

Here’s an example using two different ad sets: One using Advantage+ Audience without suggestions and one using only remarketing.

When using Advantage+ Audience without suggestions, Meta spent 45 percent of my budget on the same people that I otherwise targeted specifically in a separate ad set. By giving the algorithm more freedom, I found that it maintained a more reasonable frequency compared to when I only targeted the remarketing group.

Meta now combines remarketing and prospecting to create an optimal balance. It will otherwise be more expensive to reach your remarketing audience (which tends to also be the most likely to perform the action that you want), but the prospecting group is larger and cheaper.

For this reason, general remarketing (where you target broad groups of website visitors, email list, and people who have engaged with your page) is rarely necessary now. It happens automatically.

Misinterpretation of Results

I should also point out that one reason some advertisers continue to swear by remarketing is a misinterpretation or misunderstanding of their results. Whenever I see someone share conversion results or ROAS that seem too good to be true, it’s often because the results are inflated.

To be clear, remarketing results should be good. But they will also be inflated. This is a great opportunity to break down your results and test how good they actually are.

Use the Compare Attribution Settings feature and break down your results by attribution setting. It would also be good to use First Conversion reporting (or at least both First Conversion and All Conversions).

When remarketing, you can expect a disproportionately high concentration in the 1-Day View column. That’s usually because of two different scenarios:

1. You emailed people on the same day they were shown your ad.
2. Regular website visitors happened to visit on the same day they were shown your ad.

It doesn’t necessarily mean that the ad didn’t do anything. In some cases, these customers saw it and it contributed to their buying decision. But a very common scenario is that they didn’t even see your ad. They would have made the purchase anyway.

View-through conversions are much more valuable when they come from new customers. They saw your ad or were impacted by it, but they didn’t click it. They remembered the product or brand and Googled you later. Then they made a purchase.

But when remarketing, at least a decent number of the view-through conversions are fluff.

When Remarketing Makes Sense

While remarketing is often unnecessary, there are some exceptions where it remains relevant.

Here are a few to consider…

1. A specific message for a specific group of people.

The most common example is an abandoned cart scenario. You want to show a different ad to people who have recently added your product to their cart but haven’t purchased. You may want to offer a discount to incentivize the sale.

Of course, it’s debatable whether this is necessary. Meta should prioritize people who have added to cart when determining who will see your ads. It will be more expensive to isolate those people in a separate ad set. It’s worth testing.

I’m actually using a variation of this right now. I have a special offer, but I only want a very specific segment of my email list to see it. While it’s open to the public, my preference for this higher-value offer is people who have bought from me before.

In this case, I am targeting the same people I am emailing about this offer. I even refer to the email in the ad copy.

With this approach, I understand that the ad is only part of the sales pitch. Since it’s a high-dollar commitment, I’m hoping that it will help motivate these people to complete the sale.

I know that my ads will only be partly responsible for the conversions that are reported in Ads Manager. But my hope is to optimize the total number of sign-ups. Since the audience is small, the total amount of ad spend will be reasonably small, too. And since the sticker amount is about $1,000, it’s a low-risk approach that makes sense.

2. Low budget and a challenge to get results.

You’re trying to sell a high-dollar product, but you’ve only been given $50 or less of budget per day. You don’t have the option of building leads and need to go straight to the sale. Remarketing should be an option.

Yes, remarketing will happen naturally if you target more broadly. But maybe the remarketing audience is relatively small. Regardless, you may struggle to achieve meaningful results.

Remarketing doesn’t guarantee results here, but it’s at least a lower-cost option.

3. Top of funnel optimization.

Optimizing for link clicks, landing page views, video views, post engagement, or anything other than a conversion can be problematic. It’s even more so when algorithmic targeting is at play because Meta will do all it can to find you the cheapest action that you want. This is often at the expense of quality. By remarketing, you can limit your audience to people you’ve already determined are higher affinity.

I’ve done this when promoting my blog posts or Reels. I know that I’ll get lots of low-quality clicks or plays if I allow the algorithm to search out anyone to engage with them. But if my goal is to get more of the people who have already proven to engage with my content, I will isolate them with a custom audience.

Beware of Soft Remarketing

While remarketing still has its place, there’s a specific strategy that you should avoid and it goes like this…

1. Run an ad that optimizes for link clicks, landing page views, or video views.

2. Create an audience of the people who engaged with the first ad.

3. Target the people who engaged with the first ad.

The reason this is problematic is the issue we’ve already discussed about top-of-the-funnel optimization. If you optimize for link clicks, landing page views, video views, or just about any other action other than a conversion, you can expect low-quality activity. You are creating a custom audience of low-quality activity. And then you are remarketing to a low-quality audience.

If you’re going to use remarketing, be sure that you’re actually targeting a high-quality group of people. Investigate how that audience was created in the first place. Organically-generated audiences or those built when optimizing for conversions will typically be your best bet.

Let Algorithmic Targeting Do Most of the Work

Remarketing still has its place, but you should allow algorithmic targeting to do the heavy lifting — especially when optimizing for purchases. “Algorithmic targeting” doesn’t only include going broad or using Advantage+ Audience. It includes any situation where your audience is expanded (and that covers a high percentage of our inputs now).

Broader targeting should take up the bulk of your ad spend. While remarketing zeroes in on the people who are already close to you, there’s limited incremental lift. You also want to bring in new people who would have never bought from you if not for your ads.

Remarketing is a good short-term, low-risk play. Broader targeting is a slower, long-term play that will help assure you have a remarketing audience to reach in the future.

Your Turn

Do you still use remarketing strategies? What specific examples of remarketing success or challenges can you share?

Let me know in the comments below!

The post 3 Times You Should Prioritize Remarketing Over Meta’s Algorithmic Ad Targeting appeared first on Jon Loomer Digital.

Complaints about Meta’s algorithmic targeting are mostly misguided. Meta’s ability to find the people who are most willing to perform your desired action is extremely effective. But there is a fatal flaw that impacts optimization for any event that isn’t a purchase event.

Before you come at me about the issues with algorithmic targeting, I get it. I say that it’s “effective” because it’s efficient at doing exactly what it’s supposed to do. The flaw prevents it from being far more valuable.

Some advertisers will spend without seeing it. They see the results and don’t ask questions. Others will reject algorithmic targeting entirely without understanding why they aren’t getting the results that they desire.

There is a problem that is frustratingly difficult, if not impossible, for advertisers to solve. It’s Meta’s problem to fix, and I’ve been complaining about it for years.

I know, I’m being cryptic. It’s not easy to explain in an opening paragraph.

Let’s back up…

Who Sees Your Ads?

First, it’s important to understand that the definition of “targeting” has changed. I’d say that this evolution is part of what confuses advertisers. We don’t know how to communicate what “this” is now.

Not long ago, I asserted that targeting was the most critical factor to the success of your ads. Good ad copy and creative couldn’t recover from a bad targeting pool.

Of course, our inputs are only kinda sorta considered now when it comes to the audience that sees our ads.

1. Advantage+ Shopping Campaigns allow for virtually no targeting inputs at all. No detailed targeting, lookalike audiences, custom audiences, or much of anything.

2. Advantage+ Audience is the default option for defining your audience now. You can define a few things like location and age minimum, but your inputs are otherwise seen as suggestions (and it’s questionable how much they’re considered at all).

3. Original Audiences tend to be the fall-back for advertisers who want to retain targeting control. But, most don’t realize that their audience is usually expanded, especially when optimizing for conversions.

The primary lever that controls who sees your ads is the performance goal.

If you’re able to strictly define your audience (which is rare), Meta will find the people within that audience who are most likely to perform the action that you want.

If your inputs are seen as suggestions, your audience is expanded, or you don’t provide any inputs at all beyond the basics, Meta will find those people within the largest pools of people.

Is this targeting? Not really. It’s providing some initial suggestions and constraints and defining what you want so that Meta can find the people who will lead to results.

Like I said at the top, Meta is actually very good at this. Fewer constraints will almost always lead to more and cheaper results. But, that’s not without some problems.

When Optimization is Most Effective

Meta is best at generating high-quality results with minimal guidance when you are able to clearly articulate what you want. There are three primary examples of this…

1. Maximize Conversions (Purchase Event).

This requires that you’ve set up conversion event tracking and have defined purchase events. Meta will focus on getting you the most purchases within your budget.

2. Maximize Value (Purchase Event).

This requires that you pass value with your purchase events and have a variety of purchase prices. You may get less volume of purchases in this case, but Meta will focus on generating the highest Return on Ad Spend.

3. Maximize Conversion Leads.

Conversion Leads optimization is possible when using instant forms and requires several months of setup to define your funnel. Meta will then optimize to show your ads to people who will most likely become high-quality leads.

It doesn’t mean that you’re guaranteed to get great results when using any of these three approaches (so many factors contribute to that). But these are the times when you and Meta are on the same page regarding what you want.

Where Optimization Struggles

The reason the above three approaches to optimization work is that there is agreement over what a quality result looks like. You’ve defined that you want more purchases, more value, or more conversion leads, and Meta will focus on getting you those things. If successful, there shouldn’t be a dispute about the quality of those results.

Where this goes wrong is when using virtually any other performance goal. It includes some performance goals that are notorious for quality issues:

• Link Clicks
• Landing Page Views
• ThruPlays
• Post Engagement

But it can also include conversions that don’t result in a purchase. If you choose the performance goal to maximize conversions and select Lead or Website Registration as your conversion event, you likely run into a regular battle.

In all of these cases, you’ve only begun to define what you want. But you and Meta aren’t going to be on the same page.

If you choose to maximize link clicks or landing page views, Meta will focus on getting you as many link clicks or landing page views as possible. But you want quality traffic, not just any traffic.

If you maximize ThruPlays, Meta will show your ads to people most likely to watch at least 15 seconds of your video. But, that’s going to include people who are forced to watch your video. You want quality views of people who choose to watch, not just any views.

If you maximize conversions where the focus is on leads, Meta will try to get you as many leads as possible. But you want quality leads who are likely to buy from you, not just any leads.

In each case, Meta doesn’t care at all about quality. The algorithm’s only focus is on getting you as many of the action that you said you want.

This has always been an issue. But it’s less of an issue when you can tightly define your audience. When you can’t, Meta has fewer constraints to find results — and the likelihood for quality issues increases.

Exploited Weaknesses

This is the perfect storm for quality issues.

1. An inability to strictly define your audience.
2. An inability to define a quality action.
3. Weaknesses that can help Meta generate a high volume of the actions that you want

Understand that Meta’s delivery algorithm knows where to look to find the action that you want. This isn’t always good.

This can be as simple as going after people who are likely to act because they’ve visited your website or engaged with your ads. It can also be going after people who have engaged with similar products or businesses.

But, it can also be due to weaknesses that are exploited to get you more results.

1. Placements.

If you choose a performance goal to maximize link clicks or landing page views, expect that a large percentage of your impressions will be focused on Audience Network. Meta knows that it can get clicks there. It’s not clear whether these are from accidental clicks, bots, or click farms (before they’re detected), but you can bet you’ll get lots of low-quality clicks.

If you choose to maximize ThruPlays, a large percentage of your impressions will go to placements where people are forced to watch at least 15 seconds of your video. Audience Network Rewarded Video, which incentivizes people to watch videos in exchange for virtual currency or something else of value, is notorious for this. I’ve had cases where I’ve had more ThruPlays than people reached for this reason.

2. Countries.

If you target multiple countries at once and there’s an imbalance of cost to reach people in those countries, you may then see an imbalance in distribution. Especially if you choose to maximize top-of-the-funnel actions, Meta will try to get you the most actions possible within your budget. While this doesn’t guarantee lower quality results, it can be a contributing factor — particularly when a country is known for bots and low-quality accounts.

3. Ages.

If you aren’t able to restrict by age, this can be a weakness that will be tapped to generate more results. I can only speak from personal experience on this, but it seems that older people are much more likely to click on and engage with ads. But that doesn’t mean that they are a likely customer. If you are generating a high number of low-quality leads, it’s possible that Meta is focusing impressions on older people because it’s leading to more results.

4. Genders.

Let’s say that your business caters to women. In theory, you may not need to limit your audience when maximizing conversions when the conversion event is a purchase. The algorithm will try to get you more purchases and should adjust when men don’t buy.

But that’s not the case if you optimize for link clicks, landing page views, post engagement, or ThruPlays. Even though they may not be your target customer, men may engage at a high rate. And that will lead to low-quality results.

5. Low-Quality Accounts.

This is a big bucket that includes bots (before they’re detected), spam accounts, and real people who want to click on everything. If they perform the action that you’ve defined in your performance goal, these are going to be some of the primary people who see your ads. They’ll get you a bunch of cheap results, but that doesn’t mean those results are the quality that you desire.

NOTE: These five weaknesses aren’t nearly as big of an issue when optimizing for conversions when your conversion event is a purchase. The reason is that if it doesn’t lead to the action that you want (a purchase), the algorithm adjusts. But this is why these weaknesses are so problematic for any other performance goal.

Age and Gender and Advantage+ Audience

One of the primary complaints about Advantage+ Audience is that age maximum and gender aren’t audience controls. You can provide an age maximum and gender, but they are only audience suggestions.

Once again, this should not be a big deal if you can accurately define the action that you want, like a purchase. But it otherwise has the potential to make Advantage+ Audience unusable when using any other performance goal.

Earlier, I mentioned having this challenge with leads. It’s not always a problem, but I’ve found that when I begin to get “surprisingly good results,” it’s usually because a high percentage of my budget is getting spent on an older audience.

There’s unfortunately no easy way around it. I’ve tried an age maximum suggestion, but Meta immediately ignores it because I can get more of the results I “want” by reaching an older audience. You can switch to original audiences and define the age maximum, but that’s not necessarily a great solution either. I don’t necessarily want to cut off all ad spend to an older audience. I just don’t want it to monopolize my budget.

The Fatal Flaw

The fatal flaw in Meta ads targeting and optimization is that, except in rare cases, Meta doesn’t know what we want. We’ve defined what we want in very general terms (link clicks, landing page views, leads, ThruPlays, etc.).

It’s the combination of this weakness in optimization and the growing reliance on algorithmic targeting that makes the problem worse. Meta’s systems are powerfully good at finding people who are willing to perform the action that you want.

Unfortunately, the action that “you want” isn’t necessarily exactly what you’ve defined with the performance goal. And that’s what leads to low-quality results and wasted ad spend.

The Solution: It’s Complicated

To a point, it’s simple. We don’t necessarily need more targeting control. It shouldn’t be necessary to require the ability to restrict by age or gender. The solution also isn’t to eliminate Advantage+ Audience or audience expansion through the various Advantage Audience tools.

The solution hasn’t changed since I first complained about it years ago: We need to be able to more precisely define what we want.

Instead of any old traffic, we want people who are going to spend time on our website, perform several actions, and make return visits.

Instead of any views of our videos, we want people who signal interest (willingly watch without being forced, search out more videos, and provide other engagement).

Instead of any leads, we want people who perform other actions that prove that they are quality leads — even if it’s not an eventual purchase.

I’m not sure how exactly Meta would implement this. It could be by providing a secondary performance goal. Or maybe it would be providing options of “volume” and “quality” actions where other factors are considered.

But the current flaws in optimization are old and primitive. Not only were they unacceptable years ago, they enhance the problem with the development of algorithmic targeting.

This needs to be fixed.

Your Turn

What are your thoughts?

Let me know in the comments below!

The post Meta Ads Targeting and Optimization’s Fatal Flaw appeared first on Jon Loomer Digital.

Today’s targeting is a combination of advertiser inputs and Meta’s algorithmic distribution, with the aim to get you as many of your desired actions as possible within your budget. But advertisers have a common misunderstanding of how much control they actually have.

What I often hear from advertisers who want control is that they switch from Advantage+ Audience to original audiences because they don’t trust Meta’s algorithmic distribution. But more often than not, they’re dealing with algorithmic distribution there, too.

As someone who is focused on educating advertisers on how the systems work, it’s been an incredibly frustrating discussion. While it makes sense to me, it simply does not to most.

That’s why I wrote this post. And, more importantly, why I created the following grid.

Grid Comparison

Are your targeting inputs respected? Or they viewed as merely suggestions? Will your audience be expanded?

The grid above is a summary of how much your targeting inputs matter, depending on the setup. When you use Advantage+ Audience, your inputs are treated the same in all cases, regardless of the performance goal. But there are some contributing factors to how much your inputs matter when using original audiences.

An important point here is that we don’t know how much your audience suggestions matter, though my tests have indicated that they matter very little. We also don’t know how much your audience is expanded when expansion happens with original audiences, though my tests again suggest that it’s similar to when using Advantage+ Audience.

The problem here is that Meta provides little to no transparency on this matter. It’s entirely solvable, of course. I’ve long asked for a breakdown that would generate separate rows of results for our targeting inputs and those who were reached beyond them. Until that exists, we’re left guessing.

Still, we can approach this as if audience suggestions are as impactful to Advantage+ Audience as your targeting inputs that can be expanded when using original audiences. And when we do, we can provide a bit more clarity regarding what we can control and what we cannot.

Advantage+ Audience (Any Performance Goal)

Advantage+ Audience is largely algorithmically driven. That means that regardless of the performance goal, Meta will search out the people who are most likely to perform the action that you want. This freedom can help lower costs and improve results (not without some risk).

Respected Inputs:

Anything entered into Audience Controls within the ad set is a tight constraint that will be respected. Meta will not show ads to people outside of these controls.

When you make customizations here, the following are respected…

Location

I often hear complaints that location isn’t actually respected, but that’s a misunderstanding of how location is controlled from the beginning.

You will reach people who are either “living in or were recently in” your selected location. If a city, that will also include a radius of 10+ miles beyond it. You cannot isolate people who only live in a certain area.

Yes, location targeting is messy. But it doesn’t get messier as a result of using either Advantage+ Audience or original audiences. The same rules apply.

Minimum Age (18-25)

You can set a minimum age, but it can’t be any lower than 18 or higher than 25. How low you can go will depend upon the targeted country.

Note that age maximum is not an audience control option.

Excluded Custom Audiences

You can also exclude people who are within a certain custom audience. An example would be excluding those who bought the specific product that you are promoting.

As is the case with locations, this method is not perfect. Custom audiences are almost never complete for various reasons, and you’re most likely to notice this with exclusions. If you reach a current customer while excluding them with custom audiences, it’s not because of whether you are using Advantage+ Audience or original audiences. These exclusions are treated the same in either case.

Languages

This control is unlikely to be used all that often.

As it says in the tooltip, Meta recommends specifying languages only when they aren’t common to your selected locations.

Audience Suggestions:

You can provide audience suggestions with Advantage+ Audience, but it is purely optional.

Meta says they will “prioritize audiences matching this profile before searching more widely.” So, that means that nothing you provide here is a tight constraint.

That includes settings for:

• Custom Audiences
• Age Range
• Gender
• Detailed Targeting (interests and behaviors)

Note that there is an audience control for age minimum that is respected, but there is also an age range that is only a suggestion. In other words, the range here (minimum and maximum) will only be seen as a suggestion and your ads can be shown to people outside of it if Meta believes it will lead to more of the actions that you want.

The age minimum within audience controls will be respected. But it doesn’t necessarily need to be the same setting as what is in audience suggestions. If you do set an age minimum in audience controls, you won’t be able to set a suggested range below it.

For example, when setting the audience control age minimum at 25, you can’t set the suggested minimum range below 25.

A key takeaway here is that there are no audience controls for age maximum or gender.

Original Audiences (Conversions Performance Goal)

If you switch to original audiences while using the performance goal to optimize for conversions or value, algorithmic expansion will be significant. This is when distribution is likely to be most similar to what you get when using Advantage+ Audience.

Understand that this has nothing to do with your campaign objective. For example, you can use the Sales objective but select the performance goal to Maximize Impressions. The factor that impacts these differences is the performance goal.

Respected Inputs:

• Minimum Age
• Maximum Age
• Gender
• Location
• Custom Audience Exclusions
• Language

Audience Expanded:

• Lookalike Audiences
• Detailed Targeting

This is where I’ve found advertisers are most surprised. When optimizing for conversions or value and you provide a lookalike audience for targeting, Advantage Lookalike is automatically turned on and cannot be turned off.

The same is the case for detailed targeting. If you provide detailed targeting, Advantage Detailed Targeting is automatically turned on and cannot be turned off.

In theory, your audience will only be expanded if it will lead to more or better results. But all indications I’ve had is that your audience expands significantly in these cases.

It Depends:

You can provide custom audiences with original audiences, but whether your audience expands will depend upon whether you leave the box for Advantage Custom Audience checked. It will be checked by default.

If it’s unchecked, you can run remarketing ads that only target people in your selected custom audiences. If you check that box, you’ll reach people well beyond that group. Based on my tests, that expansion is similar to what happens when providing custom audiences as suggestions with Advantage+ Audience.

Original Audiences (Link Clicks/Landing Page Views)

Of course, what is expanded and what isn’t by default — and whether you can turn that expansion off — varies depending on your performance goal. If you select a performance goal to maximize link clicks or landing page views, things are slightly different.

Respected Inputs:

• Minimum Age
• Maximum Age
• Gender
• Location
• Custom Audience Exclusions
• Language

Audience Expanded:

Here, only Advantage Detailed Targeting is on by default without an option to turn it off.

This was a change that rolled out in early 2024.

It Depends:

When using original audiences, you will always have the option of turning Advantage Custom Audience off (assuming you remember to uncheck the box). When optimizing for link clicks or landing page views, you will also have the option of turning off Advantage Lookalike to focus on your selected lookalike audiences.

Original Audiences (Any Other Performance Goal)

For any other performance goal (Reach, Impressions, Post Engagement, ThruPlays, etc.), you’ll have slightly more control over whether your audience is expanded when using original audiences.

Respected Inputs:

• Minimum Age
• Maximum Age
• Gender
• Location
• Custom Audience Exclusions
• Language

Audience Expanded:

Nothing is expanded by default.

It Depends:

In this case, Advantage Detailed Targeting can be turned on if you so desire.

Advantage Custom Audience and Advantage Lookalike are both optional.

What Should You Do?

So now you should understand that algorithmic distribution beyond your targeting inputs is likely to happen regardless of your decision to use Advantage+ Audience or original audiences. There are times when original audiences do give you more control. But that added control isn’t always required, or even beneficial.

There isn’t a one-size-fits-all approach to this. But here is how I approach it…

1. When Using the Conversions Performance Goal and Purchase Conversion Event

Keep in mind that you can select conversion events other than Purchase. But when using Purchase as your goal conversion event, I recommend using Advantage+ Audience (if not Advantage+ Shopping). The algorithm will adjust in real-time to show your ads to people most likely to purchase. That flexibility should only help you.

Even if your clients are primarily women and you can’t set gender as an audience control, the algorithm should adjust when Purchase is your goal event. Meta doesn’t want to waste money on people who don’t lead to that action (this could be an issue for other types of optimization).

2. When Using the Conversions Performance Goal and Other Conversion Events

If you select a conversion event other than Purchase, I’d still recommend that you use Advantage+ Audience. However, you should monitor it closely to make sure that the algorithm doesn’t exploit weaknesses that may lead to low-quality results.

Once again, understand that the algorithm’s focus is getting you as many of the goal action that you want within your budget. That’s not an issue when the goal event is a purchase. You’re not in danger of getting low-quality purchases this way. But that could be an issue for leads or other actions.

But I emphasize the word “could.” Don’t assume it. I’ve actually seen it go both ways. I’ve used Advantage+ Audience to generate leads at a lower cost that are also at a high quality. And I’ve also seen the algorithm suddenly favor the highest age bracket, resulting in low-quality leads. And the issue, of course, is that we can’t set an audience control for age maximum.

3. When Using Any Other Performance Goal

This is a bit of a loaded hypothetical because I don’t recommend using other performance goals generally since there is always the potential for low-quality results. The reason is that the algorithm will always look to exploit weaknesses in placements or the user pool to get you as many of the action you want. That can be a big problem when optimizing for clicks or engagement.

The truth is that switching from Advantage+ Audience doesn’t solve this problem. But you can at least limit your audience pool by age maximum or gender, if that is important. And this is where it can be an issue if your business serves primarily women or a specific age group.

Why is it a problem? If you want post engagement or video views, Meta’s delivery algorithm only cares about getting you more post engagement or video views. It doesn’t care whether potential clients see your ads. If men click on your ads or watch your videos, Meta will take that as a signal that more men should see your ads.

Your Turn

How do you approach audience inputs and expansion?

Let me know in the comments below!

The post When Do Targeting Inputs Matter? appeared first on Jon Loomer Digital.

To suggest that my perspective on Meta ads targeting has changed during the past year is an understatement. It’s completely transformed. This evolution wasn’t immediate and was reinforced through a series of tests.

Understand that it wasn’t easy to get here. It’s reasonable to say that my prior advertising strategy could have been boiled down to targeting. It was the most important step. Great ad copy and creative couldn’t overcome bad targeting.

It’s not that I don’t care about reaching a relevant audience now. It’s that the levers we pull to get there are no longer the same.

I’m getting ahead of myself. This post will help explain how I got here. I’ve run a series of tests during the past year that have opened my eyes to just how much things have changed. They’ve helped me understand how I should change, too.

In this post, we’ll discuss the following tests:

• Test 1: How Much Do Audiences Expand?
• Test 2: How Much Remarketing Happens When Going Broad?
• Test 3: Do Audience Suggestions Matter When Using Advantage+ Audience?
• Test 4: Comparing Performance and Quality of Results
• Test 5: Understanding the Contribution of Randomness to Results

Let’s get to it…

Test 1: How Much Do Audiences Expand?

One of my primary complaints ever since Advantage Detailed Targeting (then Detailed Targeting Expansion) was introduced is the lack of transparency.

We know that Meta can expand your audience beyond the initial targeting inputs, but will this always happen? Will your audience expand a little or a lot? We have no idea. I’ve long asked for a breakdown that would solve this problem, but I don’t anticipate getting that feature anytime soon.

The same questions about how much your audience expands also apply to Advantage Lookalike and Advantage Custom Audience. It’s a mystery.

This is important because we can’t always avoid expansion. If your performance goal aims to maximize conversions, value, link clicks, or landing page views while using original audiences, Advantage Detailed Targeting is automatically on and it can’t be turned off.

The same is true for Advantage Lookalike when your performance goal maximizes conversions or value.

Are we able to clear up this mystery with a test?

The Test

I don’t believe that there’s any way to prove how much our audience is expanded when Advantage Detailed Targeting or Advantage Lookalike are applied. But, there is a way to test this with Advantage Custom Audience. While it won’t definitively prove how our audience is expanded with the other two methods, it could provide a roadmap.

This test is possible thanks to the availability of Audience Segments for all sales campaigns. Once you define your Audience Segments, you can run a breakdown of your results to view the distribution of ad spend and other metrics between three different groups:

• Engaged Audience
• Existing Customers
• New Audience

For the purpose of this test, this breakdown can help us understand how much our audience is expanded. All we need to do is create an ad set using original audiences where we explicitly target the same custom audiences that are used to define our Audience Segments.

So, I did just that, and I turned on Advantage Custom Audience.

I used the Sales objective so that the necessary breakdown would be available.

The Results

My only focus with this test was to uncover how my budget was distributed. Performance didn’t matter.

In this case, 26% of my budget was spent between my Engaged Audience and Existing Customers.

Since the custom audiences I used for targeting matched how I defined my Audience Segments, we can state definitively that, in this case, Meta spent 74% of my budget reaching people outside of my targeting inputs.

What I Learned

This was groundbreaking for my understanding of audience expansion. Up until this point, whether or not Meta expanded my audience — and by how much — was a mystery. This test lifted the curtain.

These results don’t mean that the 74/26 split would apply in all situations universally. Many factors likely contribute to the distribution that I saw here, not limited to…

• Performance goal
• Conversion event
• Budget
• Size of remarketing audiences

We also don’t know if a similar split happens when applying Advantage Detailed Targeting or Advantage Lookalike. While we don’t know, this at least gives us a point of reference rather than having to make a blind guess.

Read More

Check out the following post and video to learn more about this test:

How Much Do Audiences Expand Using Advantage Custom Audience?

Test 2: How Much Remarketing Happens When Going Broad?

Even before we had Advantage+ Shopping Campaigns and Advantage+ Audience, some advertisers swore by using original audiences to “go broad” (no inputs for custom audiences, lookalike audiences, or detailed targeting). While unique, this approach was largely based on gut feel, with limited ways to prove how ads were getting distributed. They could only provide results as evidence that it was effective.

The addition of Audience Segments to all sales campaigns would allow us to provide a bit more insight into what is happening when going broad.

The Test

I created a campaign with the following settings…

• Campaign Objective: Sales
• Performance Goal: Maximize Conversions
• Conversion Event: Complete Registrations
• Targeting: Original Audiences using only location and custom audience exclusions
• Placements: All

The Results

Recall that we already had a remarketing distribution benchmark with the prior test. In that case, we explicitly defined the custom audiences we wanted to reach within targeting. In this case, I didn’t provide any such inputs.

And yet…

Even though no inputs were provided, Meta spent 25% of my budget on reaching prior website visitors and people who were on my email list (both paid customers and not).

What I Learned

I found this to be absolutely fascinating. While we will struggle to get any insight into who the people are that Meta reached outside of remarketing, the fact that 25% of my budget was spent on website visitors and email subscribers is important. It shows that Meta is prioritizing showing my ads to people most likely to convert.

This realization helped improve my confidence in a hands-off approach. If the percentage were closer to 0, it may show disorder. It could suggest that the broad targeting approach is based in smoke and mirrors and your inputs are necessary to help steer the algorithm.

What was most shocking to me is that the remarketing distribution was nearly identical, whether I used Advantage Custom Audience and defined my target or went completely broad. This was a whole new realization.

While the first test helped me understand how much Meta expands my targeting inputs, the second made me question whether those inputs were necessary at all. I’d spend about the exact same amount reaching that desired group in each case.

Read More

Check out the following post and video to learn more about this test:

25 Percent of My Budget Was Spent on Remarketing While Going Broad

Test 3: Do Audience Suggestions Matter When Using Advantage+ Audience?

While you have the option to switch to original audiences, the default these days is Advantage+ Audience. Meta strongly encourages you to take this route, warning that switching to original audiences can lead to a drop in performance.

When using Advantage+ Audience, you leverage Meta’s AI-driven algorithmic targeting. You have the option to provide audience suggestions, but it’s not required.

Meta says that even if you don’t provide suggestions, they will prioritize things like conversion history, pixel data, and prior engagement with your ads.

But, is this true? And how pronounced is it?

The Test

We could test this by again leveraging a manual sales campaign with Audience Segments. I created two ad sets:

• Advantage+ Audience without suggestions
• Advantage+ Audience with suggestions that match my Audience Segments

Since I can use custom audiences that exactly match the custom audiences used to define my Audience Segments, we can get a better idea of just how much (if at all) these audience suggestions impact delivery.

A reasonable hypothesis would be that while Advantage+ Audience without suggestions will result in remarketing (potentially in the 25% range, as we discovered when going broad). But, it’s likely to make up a smaller percentage of ad spend than when providing suggestions that match my Audience Segments.

But, that didn’t play out…

The Results

Once again, quite shocking.

The ad set that used custom audiences that match those used to define my Audience Segments resulted in 32% of my budget spent on that group.

By itself, this seems meaningful. More is spent on remarketing in this case than when going broad or even using Advantage Custom Audience (wow!).

But, check out the results when not providing any suggestions at all…

Your eyes aren’t deceiving you. When I used Advantage+ Audience without suggestions, 35% of my budget was spent on remarketing.

What I Learned

Every test surprised me. This one shook me.

When I provided audience suggestions, I reached the people matching those suggestions less than when I didn’t provide any suggestions at all. Providing suggestions was not a benefit. It didn’t seem to impact what the algorithm chose to do. That same group was prioritized either way, with or without suggesting them.

It’s not clear if this would be the case for other types of suggestions (lookalike audiences, detailed targeting, age maximum, and gender). But, the results of this test imply that while audience suggestions can’t hurt, it’s debatable whether they do anything.

As is the case in every test, there are several factors that will contribute to my results. Budget and the size of my remarketing audience are certainly part of that. And it’s also quite possible that I won’t always see these same results if I were to run the test multiple times.

It remains eye-opening. Not only is Advantage+ Audience without suggestions so powerful that it will prioritize my remarketing audience, it’s possible that Meta doesn’t need any suggestions at all.

Read More

Check out the following post and video to learn more about this test:

Audience Suggestions May Not Always Be Necessary

Test 4: Comparing Performance and Quality of Results

I’ve encouraged advertisers to prioritize Advantage+ Audience for much of the past year. It’s not that it’s always better, but it should be your first option. Instead, it seems that many advertisers find every excuse to distrust it and switch to original audiences.

Advertisers tell me that they get better results with detailed targeting or lookalike audiences. And even if they could get more conversions from Advantage+ Audience, they’re lower quality.

Is this the case for me? I decided to test it…

The Test

I created an A/B test of three ad sets where everything was the same, beyond the targeting. Here are the settings…

• Objective: Sales
• Performance Goal: Maximize Conversions
• Conversion Event: Complete Registration
• Attribution Setting: 1-Day Click
• Placements: All

The three ad sets took three different approaches to targeting:

• Advantage+ Audience without suggestions
• Original audiences using detailed targeting (Advantage Detailed Targeting)
• Original audiences using lookalike audiences (Advantage Lookalike)

Since the performance goal is to maximize conversions, Advantage Detailed Targeting and Advantage Lookalike would automatically be applied for the respective ad set, and it could not be turned off. The audience is expanded regardless.

The ads were the same in all cases, promoting a beginner advertiser subscription.

The Results

In terms of pure conversions, Advantage+ Audience led to the most, besting Advantage Detailed Targeting by 5% and Advantage Lookalike by 25%.

Recall that this was an A/B test, and Meta had 61% confidence that Advantage+ Audience would win if the test were run again. Maybe as important, a less than 5% confidence that Advantage Lookalike would win.

But, one of the complaints about Advantage+ Audience relates to quality. Are these empty subscriptions run by bots and people who will die on my email list?

Well, I tracked that. I created a separate landing page for each ad that utilized a unique form. Once subscribed, these people received a unique tag so that I could keep track of which audience they were in. The easiest way to measure quality was to tag the people who clicked on a link in my emails after subscribing.

Once again, Advantage+ Audience generated the most quality subscribers.

Is this because Advantage+ Audience leaned heavily into remarketing? We can find out with a breakdown by Audience Segments!

Nope! More was actually spent on remarketing for the Advantage Detailed Targeting ad set. Advantage+ Audience actually generated the fewest conversions from remarketing (though it was close to Advantage Lookalike).

What I Learned

This test was different than the others because the focus was on results and quality of those results, rather than on how my ads were distributed. And, amazingly, Advantage+ Audience without suggestions was again the winner.

Of course, we’re not dealing with enormous sample sizes here ($2,250 total spent on this test). It’s possible that Advantage Detailed Targeting would overtake Advantage+ Audience in a separate test. But, what’s clear here is that the difference is negligible.

There just doesn’t appear to be a benefit to spending the time and effort required to switch to original audiences and provide detailed targeting or lookalike audiences. I’m getting just as good results (even better) letting the algorithm do it all for me.

As always, many factors contribute. I may get better results with Advantage+ Audience because I have extensive history on my ad account. But, as mentioned in the results section, it’s not as if it led to more results from remarketing.

The fact that Advantage+ Audience won here isn’t even necessarily the main takeaway. There could be some randomness baked into these results (more on that in a minute). But, this test further increased my confidence in letting the algorithm do it’s thing with Advantage+ Audience.

Read More

Check out the following post to learn more about this test:

Test Results: Advantage+ Audience vs. Detailed Targeting and Lookalikes

Test 5: Understanding the Contribution of Randomness to Results

There was something about that last test — and really all of these tests — that was nagging at me. Yes, Advantage+ Audience without suggestions kept coming out on top. But, I was quick to remind you that these tests aren’t perfect or universal. The results may be different if I were to run the tests again.

That got me thinking about randomness…

What percentage of our results are completely random? What I mean by that is that people aren’t robots. They aren’t 100% predictable when it comes to whether they will act on a certain ad. Many factors contribute to what they end up doing, and much of that is random.

If there’s a split test and the same person would be in all three audiences, which audience do they get picked for? How many of those random selections would have converted regardless of the ad set? How many converted because of the perfect conditions that day?

It might be crazy, but I felt like we could make an example of randomness with a test.

The Test

I created an A/B test of three ad sets. We don’t need to spend a whole lot of time talking about them because they were all identical. Everything in the ad sets was the same. They all promoted identical ads to generate registrations for my Beginners subscription.

I think it’s rather obvious that we wouldn’t get identical results between these three ad sets. But, how different would they be? And what might that say about the inferences we make from other tests?

The Results

Wow. Yes, there was a noticeable difference.

One ad set generated 25% more than the lowest performer. If that percentage sounds familiar, it’s because it was the exact same difference between the top and bottom performer in the last test. But in that case, that difference “felt” more meaningful.

In this case, we know there’s nothing meaningfully different about the ad sets that led to the variance in performance. And yet, Meta had a 59% confidence level (nearly the same as the level of confidence in the winner in the previous test) that the winning ad set would win if the test were run again.

What I Learned

Randomness is important! Yet, most advertisers completely discount it. They test every detail and make changes based on differences in performance that are even narrower than what we saw here.

Think about all of the things that advertisers test. They create multiple ad sets to test targeting. They try to isolate the best performing ad copy, creative, and combination of the two.

This test taught me that most of these tests are based in a flawed understanding of the results. Unless you can generate meaningful volume (usually because you’re spending a lot), it’s not worth your time.

Your “optimizing” may not be making any difference at all. You may be acting on differences that would flip if you tested again — or if you let the test run longer or spent more money.

It’s even reasonable to think that too much testing will hurt your results. You’re running competing campaigns and ad sets that drive up ad costs due to audience fragmentation and auction overlap — all for a perceived benefit that may not exist.

I’m not saying that you should never test anything to optimize your results. But be very aware of the contributions of randomness.

Read More

Check out the following post to learn more about this test:

Results: Identical Ad Sets, a Split Test, and Chaos

My Approach Now

You’re smart. If you’ve read this far, you can infer how these tests have altered my approach. My strategy is drastically simplified from it once was.

I lean heavily on Advantage+ Audience without suggestions, especially when optimizing for conversions. Of course, Advantage+ Audience isn’t perfect. If I need to add guardrails, I will switch to original audiences. But when I do, I typically go broad. I rarely ever use detailed targeting or lookalikes now.

I also rarely use remarketing now, which is insane considering it once made up the majority of my ad spend. Since remarketing is baked in, there are few reasons to create separate remarketing and prospecting ad sets now. Especially when I’d normally use general remarketing (all website visitors and email subscribers) because I felt these people would be most likely to convert.

This also means far fewer ad sets. Unless I’m running one of these tests, I almost always have a single ad set in a campaign.

It doesn’t mean I’m complacent in this approach. It means that the results of these tests have raised my confidence that no targeting inputs will not only perform just as well, but oftentimes better. And I know that there are exceptions and factors that contribute to my results.

Maybe things will change. But, I no longer feel the need to micromanage my targeting. Based on the results of these tests — and of my results generally — it’s no longer a priority or a factor that I worry about.

And that, my friends, is quite the evolution from where I was not long ago.

Run Your Own Tests

I’m always quick to point out that my results are at least partially unique to me. Whether you’re curious or skeptical, I encourage you to run your own tests.

But, do so with an open mind. Don’t run these tests hoping that your current approach will prevail. Spend enough to get meaningful results.

Maybe you’ll see something different. If you do, that’s fine! The main point is that we shouldn’t get stuck in our ways or force a strategy simply because it worked at one time and we want it to work now.

Replicate what I did. Then report back!

Your Turn

Have you run tests like these before? What results did you see?

Let me know in the comments below!

The post 5 Meta Ads Tests that Transformed My Perspective on Targeting appeared first on Jon Loomer Digital.

Advertisers often complain about paying to reach people they believe should have been explicitly excluded using custom audiences. The assumption is that Meta has chosen to ignore exclusions. But, the effectiveness of these exclusions is mostly within our control.

We most often hear this related to existing customers. There are two primary scenarios where this comes into play:

1. Advantage+ Shopping using an Existing Customer Budget Cap.

Advantage+ Shopping Campaigns allow you to set a cap on how much you will spend on existing customers.

This approach relies on the definition of your Existing Customers audience segment within your Ad Account Settings.

2. Manual campaign with a custom audience exclusion.

You can also run a manual campaign and exclude your existing customers by listing out the custom audiences that reflect that group.

If you use Advantage+ Audience, that would be within the Audience Controls.

If using original audiences, you can exclude custom audiences.

But, even if you use these settings, you will probably still reach some of your custom audiences. Why?

Here are the three most likely reasons (along with a myth about audience expansion)…

1. Completeness and Accuracy of Data Provided

In order to exclude every existing customer, you must first completely and accurately define your customers with custom audiences so that Meta can do just that. But, this is far more difficult than it sounds, approaching unreasonable.

Here’s an example of how I’ve defined my existing customers…

It’s a mixture of data file custom audiences and website custom audiences. But, I guarantee it’s incomplete.

To troubleshoot, ask yourself these questions…

Do your excluded custom audiences actually include existing customers?

It may seem like a silly question, but one of the first mistakes that advertisers make in this area is that they mess up the parameters that define a group of people. Look no further than inflated conversion reporting happening because the Purchase event is firing for the wrong stage.

Do your excluded custom audiences exclude all customers or only some?

When creating a custom audience based on your email list, have you confirmed that you’ve included every customer for every product? All customers historically, or only a specified period of time?

I should also point out that, depending on how you interpret Meta’s Custom Audience Terms of Service, you may be required to remove customers who have opted out of your list. So, there may be paying customers who you can’t include in the custom audience.

This may be pointing out the obvious, but website custom audiences are capped at 180 days. If you exclude your existing customers using this approach and your business is more than six months old, the audience will be incomplete.

And of course, there’s a long list of potential issues with website custom audiences and completeness. The most obvious is iOS opt-outs. Meta specifically said that the result of opt-outs would be smaller custom audiences.

That will create holes in your exclusions.

2. Meta’s Ability to Match the Audience

This mostly applies to data file custom audiences, where you provided a customer list to create a custom audience. Just because you uploaded a customer list that includes a specific person doesn’t mean that Meta will be able to match that customer’s details to a Facebook profile.

If you only include a list of email addresses, they need to be matched to Facebook users who provided those same addresses in their profiles. Facebook profiles may be old and outdated. Maybe your customer used a business email address that isn’t associated with their profile.

The more columns of data you provide for first name, last name, email address, phone number, and physical address, the higher the match rate will be. But, you can guarantee you won’t get a 100% match rate.

It’s anecdotal, but advertisers tend to see anywhere from 20 to 70% match rates from customer lists. The ability to match is only as good as the completeness and accuracy of the data. But even then, it’s not guaranteed to match a Facebook profile that’s used for exclusions.

You could also make the argument to include website custom audiences here. If a user is blocking cookies, browsing incognito, or using other privacy settings that impact the data that can be sent back to Meta (not to mention iOS opt-outs), Meta’s ability to match and exclude users is impeded.

3. Meta’s Ability to Actually Exclude Them

This is more theory than reality, and it assumes that the source of the problem isn’t #1 or #2 above. Essentially, it would mean that despite accurately and thoroughly defining your existing customer custom audiences, you are still paying to reach the people you shouldn’t. Meta knows that a specific person falls within your exclusions, but you reach them anyway.

Maybe it’s due to a bug. Maybe it’s because Meta doesn’t care about your stinking exclusions.

I’m not saying that this is impossible. But, of the three possible explanations, it’s the least likely. It’s also very difficult, if not impossible, to prove.

By “least likely,” I don’t mean that bugs rarely happen or that Meta is always trustworthy. I mean that there are so many obvious reasons for holes in exclusions, we don’t really need to resort to conspiracy theories to explain them.

The Expansion Myth

I’ve seen the theory that audience expansion doesn’t respect your custom audience exclusions. Specifically, this is related to using original audiences when Advantage Detailed Targeting or Advantage Lookalike are turned on.

The way I understand it, the source of the theory is this passage in Meta’s documentation related to Advantage Detailed Targeting…

And a similar passage from Meta’s documentation related to Advantage Lookalike…

For Advantage Detailed Targeting, Meta says that you can still exclude “targeting selections outside of detailed targeting (such as age, gender, location and language).” For Advantage Lookalike, “you can add targeting selections as exclusions if you don’t want our system to consider certain demographics such as Locations, Age, Gender etc.” Meta didn’t mention custom audiences!

But, is this an intentional omission? In both cases, it’s clear that Meta isn’t providing an exhaustive list. “Such as” language when listing out what can be excluded from Advantage Detailed Targeting and an important “etc.” to wrap up exclusions for Advantage Lookalike could suggest, maybe, that custom audience exclusions aren’t respected.

I’m not buying this argument. You can still exclude custom audiences in either case. It’s far from definitive that the reason you can still reach some of these people is due to expansion.

According to this theory, the proof is that if you optimize for a top of funnel action that doesn’t require expansion, third-party reporting tools show that you reach fewer existing customers as a result. But, this is less a function of the incredibly low quality results you get from top of funnel optimization than any proof that the exclusion works in this case.

If you’re still not convinced, look no further than Advantage+ Audience. Audience Controls are where you set the specific parameters that Meta will respect. These are not suggestions, but tight constraints.

One of those controls is excluded custom audiences.

If you believe that your custom audience exclusions aren’t respected when using original audiences when expansion is on, then maybe you should use Advantage+ Audience instead. This seems backwards, though, since the entire benefit of Advantage+ Audience is that the algorithm has more freedom to reach people who are likely to convert than when using original audiences. It would be odd if it were Advantage+ Audience that would respect your exclusions while they may not be with original audiences.

But, again, I’m confident that the belief that exclusions aren’t respected with expanded audiences is a misinterpretation. When in doubt, go with the most likely explanation. And there are lots of them.

Your Turn

What are your feelings about the causes behind reaching excluded existing customers?

Let me know in the comments below!

The post 3 Holes in Existing Customers Exclusion for Meta Ads appeared first on Jon Loomer Digital.

We should always test our assumptions. We may think that something works, or maybe it worked at one time, but it’s important to verify that it remains the path forward.

Testing our targeting strategies was the focus of a recent blog post, and I ran a test of my own as an example. This post will highlight the setup and results of the test.

I tested using the following three targeting strategies:

1. Advantage+ Audience without suggestions
2. Detailed Targeting with Advantage Detailed Targeting
3. Lookalike Audiences with Advantage Lookalike

It’s important to understand that the results of this test are not universal. I will address some of the potential contributing factors at the end of this post.

Here’s what we’ll cover:

• Campaign Basics
• Targeting
• A/B Test Setup
• Surface Level Data
• Conversion Results
• Quality
• Remarketing and Prospecting Distribution
• Potential Contributing Factors
• What it Means

My goal isn’t to convince you that your approach is right or wrong. My hope is that my test inspires you to run a similar one of your own so that you can validate or invalidate your assumptions.

Let’s begin…

Campaign Basics

I created a campaign using the Sales objective.

Within that campaign, I created three ad sets. Each used the following settings…

1. Performance Goal: Maximize conversions with Complete Registration conversion event.

My goal is to get registrations on a lead magnet. The reason I’m using the Sales objective is to get access to Audience Segments data (I’ll address that later).

2. Attribution Setting: 1-day click.

I recommend using a 1-day click attribution setting for most non-purchase events.

3. Budget: $25/day per ad set ($750 per ad set overall)

The total spent on the test was about $2,250.

4. Locations: United States, Canada, and Australia.

I would normally include the United Kingdom, but it is no longer allowed for split testing.

5. Placements: Advantage+ Placements.

6. Ads: 1 static and one using Flexible Ad Format. The Flexible version utilized four different images.

Each ad sent people to a different landing page with a unique form. All three landing pages and forms appear identical to the user. This was done so that I could confirm results in my CRM — not just the number of registrations using each form, but what these people did once they subscribed.

Targeting

Each ad set utilized a different targeting approach.

1. Advantage+ Audience without suggestions.

There isn’t much to show here. This allows the algorithm to do whatever it wants.

2. Detailed Targeting with Advantage Detailed Targeting.

I used Original Audiences and selected the following detailed targeting options:

• Digital Marketing Strategist
• Advertising agency (marketing)
• Jon Loomer Digital (website)
• Digital marketing (marketing)
• Online advertising (marketing)
• Social media marketing (marketing)

Because I’m optimizing for conversions, Advantage Detailed Targeting is automatically turned on. I cannot prevent the audience from expanding.

3. Lookalike Audiences with Advantage Lookalike.

I selected lookalike audiences based on the following sources:

• Customer List
• Power Hitters Club – Elite (Active Member)
• All Purchases – JonLoomer.com – 180 Days

Because I’m optimizing for conversions, Advantage Lookalike is automatically turned on and can’t be turned off.

A/B Test Setup

I ran an A/B test of these three ad sets in Experiments. The key metric for finding a winner was Cost Per Result. That “result” was a registration.

I ran the test for 30 days and chose not to have it end early if Meta found a winner.

I’m glad I did it this way because Meta’s confidence in the winner wasn’t particularly high and it changed the projected winner a couple of times. This allowed the test to play out until the end.

Surface Level Data

Before we get to the results, I found this interesting. Beyond testing how these three would perform, I was curious if the cost for delivery would be much different. This, of course, could have an impact on overall performance.

The difference in CPM is minor, but it could be impactful. It was $.68 cheaper to deliver ads using Advantage+ Audience than Lookalikes. The difference in CPM between Advantage+ Audience and Detailed Targeting was $.89.

While this may not seem like much (it’s not), that resulted in the delivery of between 1,500 and 2,000 more impressions when using Advantage+ Audience. It doesn’t mean that a lower CPM will lead to more results, but we should bookmark this metric for later.

Conversion Results

According to Ads Manager, Advantage+ Audience led to 9 more registrations than Detailed Targeting and 36 more than Lookalikes.

The overall costs for these results weren’t great, but that’s also consistent with what I’ve seen when running split tests. Because these tests prevent overlap, delivery will be less efficient. Of course, “good results” weren’t the goal here.

The difference between Advantage+ Audience and Detailed Targeting may not be statistically significant, but the difference between the two and Lookalikes certainly was. The A/B test results support this assumption.

It’s possible that if the test were run again, Detailed Targeting would come out ahead (Meta estimates a 36% chance of that happening). But, it’s very unlikely (under 5%) that Lookalikes would come out on top.

Recall that each ad sent people to a different landing page that utilized a different form. This way, registrants were given a unique tag so that I knew which audience they were in. These landing pages and forms were only used for the test.

Keep in mind that the results in Ads Manager reflect all registrations, and this can include registrations for other lead magnets. This could happen if someone who subscribes to the lead magnet I’m promoting then subscribes to another (I email about other lead magnets in my nurture sequence).

The numbers from my CRM aren’t much different, but they are different.

The disparity is greater when looking at the “true” results. Advantage+ Audience led to 14 more registrations than Detailed Targeting and 43 more than Lookalikes.

At least some of this difference might be related to the slight difference in CPMs. But, keep in mind that Lookalikes had the second lowest CPM of the three targeting strategies, but it performed the worst.

Quality

One of the first arguments I hear from advertisers when it comes to leveraging Advantage+ Audience over old school targeting approaches is that it’s more likely to lead to low-quality results. Was that the case here?

I was prepared to measure this. It’s one of the reasons that I used unique forms for each ad set. It allowed me to get a deeper understanding of whether these registrants did anything else.

I’d consider my funnel atypical when it comes to most businesses who collect registrations. I don’t have an expectation that many of them will buy from me within 30 days. I look at it as more of a long-tail impact, and many of the people who buy from me do so years later.

Because of that, we can’t make any reasonable assessment of registration quality based on sales at this stage. While two purchases came in via Advantage+ Audience and two from Detailed Targeting so far, these are hardly statistically significant. And it could change dramatically in a matter of months or years (and I don’t want to wait until then to publish this post).

But, there is another way to assess quality, and I first applied this when comparing lead quality from instant forms vs. website forms. Have these registrants performed a funnel event by clicking specific links in my emails?

Once again, the count of “quality clicks” is incomplete, but we can make some initial evaluations. Here’s where we stand at this moment…

While Advantage+ Audience led to a higher volume of registrations, it was not at the expense of quality. It generated 17% more quality registrants than Detailed Targeting and 54% more than Lookalikes.

These numbers are imperfect and incomplete since, like I said, a true evaluation of whether or not the registrations were “quality” can’t be made for quite some time. But, it at least shows the difference in engagement. If someone hasn’t engaged with my emails, they are less likely to be an eventual customer.

Remarketing and Prospecting Distribution

I promised I’d get back to this when I explained using the Sales objective at the top. I could have used the Leads objective (or even Engagement), but I chose Sales for one reason: Access to data using Audience Segments.

When running a Sales campaign (Advantage+ Shopping or manual), some advertisers have access to Audience Segments for reporting.

Once you define your Engaged Audience and Existing Customers, you can use breakdowns to see how your budget and results are distributed between remarketing (Engaged Audience and Existing Customers) and prospecting (New Audience).

This is something that isn’t necessarily incredibly meaningful, but I find it interesting. It gives us an idea of how Meta finds the people who are likely to perform our goal event. I used this as the primary way to compare distribution using four different targeting approaches in another test.

Within that test, I saw remarketing take up 25 to 35% of my budget, regardless of the targeting approach. In that case, I ran each ad set concurrently and didn’t run an A/B test. This test could be different since it’s a true A/B test.

Here are the breakdowns…

It’s a lot of numbers, but the distribution between remarketing and prospecting is very similar in all three cases.

• Advantage+ Audience: 9.2% remarketing, 90.8% prospecting
• Detailed Targeting: 10.1% remarketing, 89.9% prospecting
• Lookalikes: 8.7% remarketing, 91.3% prospecting

More remarketing happened with Detailed Targeting, though I wouldn’t consider that statistically significant. The type of remarketing was a bit more significant, however. Advantage+ Audience spent $10 on existing customers, whereas the other two approaches spent around $5 or under. Not a lot, obviously.

Maybe somewhat surprising is that more remarketing registrations came from using Detailed Targeting (25 vs. 16 for Lookalikes and 14 for Advantage+ Audience). While that creates a seemingly significant percentage difference, we’re also dealing with very small sample sizes now that may be impacted by randomness.

My primary takeaway is that distribution to remarketing and prospecting is about the same for all three approaches. My theory regarding why it’s so much less than when I ran my other three tests is that an A/B test splits a finite (and comparatively smaller) remarketing audience into three. There isn’t as much remarketing to go around.

Potential Contributing Factors

It’s important to understand that my results are unique. They are impacted by factors that are unique to my situation and you may see different results.

1. The Detailed Targeting selected.

Some advertisers swear by detailed targeting. Maybe they have certain options that are much more precise and make using them an advantage. Maybe I would have seen different results had I used a different selection of interests and behaviors.

These things are all true. But, you should also remember that no matter what our selections, the audience is expanded when optimizing for conversions. This is why I have my doubts regarding the impact of using specific detailed targeting options.

2. The Lookalike Audiences selected.

The lookalike audiences that I selected are based on sources that are important to my business. They include both prior registrants and paying customers. But, this was also my worst performing ad set. Maybe different lookalike audiences would have changed things.

Once again, I’m not wholly convinced of this because of the fact that lookalike audiences are expanded when optimizing for conversions. I have doubts regarding whether any of my lookalike audiences are that different that the algorithm wouldn’t eventually find itself showing my ads to the same people once expanded.

But, I can’t ignore the possibility. I was surprised that lookalikes performed so much worse than the other two, and the ones I selected could have contributed to those results.

3. Activity and history on my account.

This one is based primarily on theory because Meta isn’t particularly clear about it. We know that if audience suggestions aren’t provided when using Advantage+ Audience, Meta will prioritize conversion history, pixel data, and prior engagement with your ads.

It’s possible that I’m at an advantage because I have extensive history on my account. My website drives more than 100,000 visitors per month. There is a history of about a decade of pixel data.

Yes, this is possible. We just don’t know that for sure. Many advertisers jump into a new account and automatically assume that Advantage+ Audience won’t be effective without that history. Test it before making that assumption.

4. Industry.

It’s entirely possible that how each of these three approaches performs will differ based on the industry. Maybe some industries have detailed targeting that clearly makes a difference. That doesn’t seem to be the case for me, even though there are detailed targeting options that clearly fit my potential customer.

And… once again, we can’t ignore that your detailed targeting inputs will be expanded when optimizing for conversions.

5. Location.

Some of the responses I’ve received from advertisers regarding the viability of Advantage+ Audience refer specifically to their location. They say that Advantage+ Audience does not work where they are. Maybe that’s the case. I can’t say for sure.

6. Randomness.

One of the biggest mistakes that advertisers make is that they fail to account for randomness. Especially when results are close, do not ignore the potential impact of random distribution. The more data we have, the less it becomes a factor.

One of the tests on my list is to compare the results of three ad sets with identical targeting. What will happen? I’m not sure. But, a piece of me is hoping for chaos.

What it Means

As I said at the top, my goal with this test wasn’t to prove anything universally. My primary goal was to validate or invalidate my assumptions. I’ve been using Advantage+ Audience for a while now. I haven’t used detailed targeting or lookalikes for quite some time. But, these results validate that my approach is working for me.

Another goal for publishing these results is to inspire advertisers to create similar tests. Whether you use Advantage+ Audience, detailed targeting, lookalike audiences, or something else, validate or invalidate your assumptions.

A far too common response that I get from advertisers about why they don’t use Advantage+ Audience is something along the lines of, “This will never work for me because…” It’s based on an assumption.

That assumption could be because of an inability to restrict gender and age with Advantage+ Audience. But, as I’ve discussed, you should test that assumption as well — especially when optimizing for purchases.

Bottom line: These results mean that Advantage+ Audience without suggestions can be just as effective as, if not more effective than, detailed targeting and lookalikes. If that’s the case, you can save a lot of time and energy worrying about your targeting.

Test this yourself and report back.

Your Turn

Have you run a similar A/B test of targeting strategies? What did you learn?

Let me know in the comments below!

The post Test Results: Advantage+ Audience vs. Detailed Targeting and Lookalikes appeared first on Jon Loomer Digital.

I’ve been a Facebook-then-Meta advertiser since close to the beginning. This site exists (for 13 years strong now) because of my passion and deep understanding of how everything works. It’s been my pleasure sharing tips over the years to help keep you ahead of the curve.

That’s why the current path of Meta ads targeting pains me. My only goal is to help you understand where things are now and where they are heading so that you are best prepared. I’ve published several videos and posts to help explain what’s happening with targeting. The most common response I’m receiving is disbelief, if not outright defiance.

I am not trying to convince you that Advantage+ Audience is always effective or that you should go targeting-free with Advantage+ Shopping Campaigns. I want you to understand that your targeting inputs matter less than they ever did before. Knowledge of this is power because it helps advertisers better understand their role and where they can be most impactful.

Some of the things I’ve said and will repeat here aren’t up for debate. It’s how things work now. Too many advertisers simply don’t have a full understanding of how targeting works in the current environment. They are tweaking things and turning dials that have little or no connection to results.

But, the defensiveness runs deep, and I understand this. If you believe that the value you add as an advertiser is found, partially or entirely, within your targeting strategy, you will hate everything that I’m saying on the topic. It’s an attack on your way of life, and that’s scary.

This post may not fix that. It took me longer than I care to admit to accept it, and I was surely angry and defensive at first. But, I hope that this at least sends you in the direction of understanding.

Interests, Behaviors, and Detailed Targeting

First, Interests and Behaviors is the same category of targeting as Detailed Targeting. I include them all here because advertisers often misunderstand what Detailed Targeting means and lump it in with remarketing, lookalike audiences, and demographic adjustments.

This is the oldest method of targeting. It was a big deal when advertisers were given the ability to target people based on their interests and behaviors. It allowed us to isolate people based on specific interests that were related to what we were promoting.

It allows me, for example, to target people who may be interested in online-advertising content and products.

This was powerful since it would give me confidence that my ads were being shown to people who cared about, and were more likely to respond favorably to, my ad.

But, the current environment is not the same as that of 2014. The value of these inputs is not the same.

1. Inaccuracies.

I encourage you to take the time to go through the interests and behaviors that can be used to target you. Some of it is accurate. Some of it is outdated. And some of it is straight-up random.

I was originally going to list out all of the most random ways that advertisers can waste their money targeting me, but I honestly don’t know where to start. There are a lot of them. I wrote about this four years ago.

North Carolina State University ran a study in 2022 that estimated 30% of interests and behaviors used for targeting are inaccurate or irrelevant. These categories are far from perfect. We should treat them accordingly.

We assume that when we use detailed targeting that our ads will reach people who have an interest or experience directly related to that thing, but it’s not that simple. Meta seems to make inferences from random engagements that are far less meaningful.

2. Expansion.

This is a big one. It’s not new. But, advertisers continue to act surprised by or completely oblivious to this.

If you optimize for conversions, link clicks, or landing page views and you provide detailed targeting inputs, Advantage Detailed Targeting is automatically turned on. It can’t be turned off.

This means that your ads will reach people beyond those interests and behaviors if it can improve results. Your audience is expanded.

We don’t know how much your audience is expanded. We don’t know how much of your budget will be spent on the interests you listed and on people beyond those groups. But, this uncertainty matters.

There’s a very wide range of possibilities here. Maybe only a small percentage of your budget is spent on reaching people beyond your intended interests. Maybe most was spent on people you didn’t plan to target.

You should have concerns regarding the accuracy of detailed targeting inputs. You should also assume that there’s a distinct possibility that the results you get have more to do with the expansion of your audience than the inputs you provided.

While we can’t say definitively that interest targeting doesn’t matter at all, the amount of positive impact they can make is certainly in question.

Bottom line: My point isn’t that you can’t get good results while using detailed targeting. A common response I get from advertisers is that they get good results when they use interests. The point is that it’s questionable how much your selections of interests and behaviors impacted your results.

Lookalike Audiences

Like interest targeting, lookalike audiences are not new. When they were announced, lookalikes presented an enhancement from using interests only. Instead of guessing about what your customer was interested in, you could have Meta find people who were most similar to your customers.

While they made sense at one time, it’s questionable whether they remain relevant today. At the very least, they’re certainly less useful than they once were.

1. Expansion.

Once again, there’s a bit of fuzziness about the parameters you’re providing. When optimizing for conversions, Advantage Lookalike is automatically turned on and it can’t be turned off.

This means that you may reach people beyond the percentage of lookalike that you selected. We won’t know how much this is expanded or how much of your budget is spent on this expansion versus your selected audience.

2. Algorithmic Targeting.

I generally find it curious that advertisers will favor lookalike audiences over Advantage+ Audience (which we’ll cover in more detail shortly). Lookalike audiences are algorithmically driven. Meta will search for people similar to those in your source audience and compile an audience that is much, much larger.

Instead of using a lookalike audience based on your current customers, let’s instead assume you use Advantage+ Audience without suggestions. By definition, Meta will use signals like pixel activity, conversion data, and prior engagement with your ads to determine who should be in your audience.

It seems odd to be okay with Meta’s development of lookalike audiences but not with algorithmic targeting. There are very obvious similarities between the two.

How much impact do the lookalike audiences that you provide have on your results? Due to expansion, we don’t know. And why should we prefer it over Meta’s more recent algorithmic targeting developments?

Targeting Inputs are Deprioritized

You may not like it, but it’s clear what Meta is doing. If you use original audiences and optimize for conversions, your detailed targeting and lookalike audiences will be expanded. Those inputs are less important than they once were.

Of course, Meta doesn’t want you to use those approaches anyway. Meta wants you to use Advantage+ Audience.

While you can provide targeting inputs, it’s pretty darn obvious that Meta doesn’t think this is necessary. Otherwise, those inputs would be immediately available.

If you provide custom audiences, lookalike audiences, detailed targeting, age maximum, or gender, they will be used as audience suggestions.

This is the default way to impact targeting. While the option to provide targeting inputs using original audiences still exists, Meta works hard to discourage you. When you click to use original audiences, you’ll get an alert asking if you’re sure.

Meta’s tests show that you can improve your results by up to 33% if you use Advantage+ Audience over original audiences. It’s in Meta’s best interests that you get those superior results.

When it comes down to it, Meta may not even prefer that you use Advantage+ Audience. When creating a sales campaign, you are defaulted to Advantage+ Shopping Campaigns.

You still have the option of creating a manual sales campaign, but Meta clearly wants you to go this route.

Advantage+ Shopping Campaigns take algorithmic targeting even further. Your targeting inputs are virtually non-existent.

It’s not that you will always get better results using Advantage+ Audience or Advantage+ Shopping Campaigns. But, Meta has found that advertisers do get better results with these methods, on average. And your impact on targeting in either case is minimal.

Remarketing

I still remember how excited I was when advertisers were given the ability to target website visitors. It changed the entire industry.

You don’t need to convince me of the value of reaching people who are deeply connected to us. I lived primarily off of remarketing for a very long time. The question is whether much of the remarketing that we once did is still necessary.

Audience Segments for sales campaigns opened my eyes to this possibility. Once you define your Engaged Audience and Existing Customers (essentially your remarketing audiences), you can see how much of your budget is spent on remarketing while not even trying.

In my tests, it doesn’t matter whether I use Advantage+ Audience (with or without suggestions) or original audiences. I regularly see a similar distribution between remarketing and prospecting.

If Meta is going to prioritize your remarketing audience anyway, why is it necessary to create separate ad sets to reach your remarketing audience — especially a general remarketing audience (all website visitors, for example)?

The primary argument for remarketing now is if you have a unique message for a very specific group of people that would only be relevant to them. Minus such a message, it just doesn’t feel necessary.

Exceptions and Caveats

I’ve been careful to specify that the situations when detailed targeting and lookalike audiences are least impactful are when those audiences are expanded. The end result is likely more like Advantage+ Audience than you think.

But, there are times when you can turn expansion off — and it may even be recommended. If your performance goal is post engagement, ThruPlay, or just about anything other than a conversion (or link clicks and landing page views for detailed targeting), Advantage Detailed Targeting and Advantage Lookalike are options that can be turned on or off.

I’m not suggesting that turning off expansion will give you better results. Instead, your inputs obviously mean more if targeting is restricted to what you provide.

There are also times when using original audiences instead of Advantage+ Audience may be preferred, especially when optimizing for top-of-the-funnel actions. Not only do you get more control over detailed targeting and lookalike audiences, but age maximums and gender become tight constraints. If you’ve seen that your budget is wasted outside of your demographic preferences when using Advantage+ Audience, this is always an option.

That said, this still doesn’t have anything to do with your detailed targeting and lookalike audience selections.

How Much Does It Matter?

If I’m successful at nothing else with this post, I hope that you at least walk away with a new skepticism about your impact on targeting.

I said it before, but it requires repeating: This isn’t about whether Advantage+ Audience is superior to using interests and lookalikes. It’s that any difference between the three approaches has the potential of being completely random.

If you’re getting great results using a certain group of interests, it may be partially due to the interests you’re using. It may be mostly due to the expanded audience. We don’t know. The main thing is that the inputs you provided aren’t likely to be the main or only reason you’re getting those results.

Results from test after test are showing me this. Surface level metrics are nearly the same. Distribution between remarketing and prospecting are nearly the same. Results are nearly the same.

And when there’s a wider difference, it’s a disparity that often can’t be replicated when I recreate the test. It was random.

That’s why I want you to obsess less over these things. It’s not that I demand you stop using original audiences with interests and lookalikes. I just want you to stop obsessing over them. It’s unlikely that you found the perfect combination of targeting inputs.

Advertisers are superstitious creatures. Even if we know that something we’re doing isn’t why we’re getting great results, we don’t want to rock the boat. And that’s perfectly fine.

But, I encourage you to resist the need to over test your targeting. If you continue to create multiple ad sets for different groups of people, hoping to isolate the best performing selection of targeting inputs, you are likely doing more harm than good.

It’s also a potentially colossal waste of time that could be better spent on things that matter, like your ad copy, creative, landing page, and attribution.

The Direction We’re Heading

This should be obvious…

1. In a very limited number of situations, you can avoid having your detailed targeting and lookalike audiences expanded. In those that remain, they may be expanded by default, but you can turn it off. Meta wants you to turn it on.

2. When optimizing for conversions (and sometimes link clicks or landing page views), your ads can be delivered to people outside of the interests and lookalikes that you provide.

3. The default approach to targeting is Advantage+ Audience. Meta doesn’t want you to use original audiences and tries to discourage you from using them.

4. Meta doesn’t even seem to care if you provide any targeting at all with Advantage+ Audience. When you do, it’s merely a suggestion.

5. If you’re creating a sales campaign, it defaults to Advantage+ Shopping, which allows for virtually no targeting inputs at all. This is what Meta wants you to do.

Your targeting inputs matter far less than they ever did before. More importantly, Meta doesn’t seem to want or even need them. And the trend line is towards eliminating them entirely.

You can be upset about this, but I simply ask that you acknowledge it. Repeat after me:

“My targeting inputs mean less than ever before. Meta doesn’t want or need my targeting inputs. One day, I will likely lose all ability to control these things.”

Once you accept it, you can prepare.

How to Impact Who Sees Your Ads

This may seem like you’re placed in a helpless situation, but you’re not. Your targeting inputs may not matter much, but you can still impact who sees your ads.

1. Performance Goal. Think about it. This might be the most impactful control of all. Whether your audience is expanded or you’re using Advantage+ Audience, the algorithm is driven by finding people who will perform the action that you want, as defined by the performance goal. This includes the conversion event that you choose when optimizing for conversions.

What you define as your goal will drastically alter who sees your ad. Meta’s focus will be on helping get you that action.

2. Ad Copy, Creative, and Offer. A common claim is that the ad does the targeting now, and I don’t know that this is literally true. I haven’t seen Meta specify that the algorithm scans your copy for keywords to determine who sees your ad. But, it’s mostly semantics.

Your initial audience is likely determined by pixel activity, conversion data, and prior engagement with your ads. After that, it learns from who performs the action that you want. So, you want your ad copy, creative, and offer to attract your ideal audience.

You don’t want to attract a general audience. You want to attract very specific people. In a sense, you want your ad to repel people who aren’t your ideal customer.

These aren’t small things. Crafting effective copy, creative, and offers isn’t easy to do. Don’t feel as though a light-touch approach to targeting is somehow the easy way out. You still have work to do.

Your Turn

What’s your approach to reaching your ideal audience? Has it evolved?

Let me know in the comments below!

The post Meta Ads Targeting and an Advertiser’s Role, Explained appeared first on Jon Loomer Digital.

Are you still running Meta ads strategies that you used years ago? Do you ignore Meta’s best practices and recommendations because you swear that they don’t work?

My view of ad strategies isn’t absolute. There isn’t one approach that will always work for everybody in all situations. If you’ve found what works for you, great. Even if it’s inconsistent with what works most often, there are exceptions.

But, you also shouldn’t do this blindly. Don’t be stubborn about it. Don’t take an approach based on gut feel, a lack of trust in automation, or because something did or didn’t work a few years ago.

If you’ve been taking the same approach for the past year or longer, it’s important that you test your assumptions about what works and what doesn’t. And when you do, make sure it’s a scientific test that will provide meaningful results.

Running these tests can only be productive. It could reinforce what you believed to be true. Or the results may make you question whether what you’re doing is actually effective. You may see an alternative approach in a new light.

My advertising approach has changed dramatically over the years. I did not immediately embrace an evolving set of best practices. I was stubborn. But, my own tests have helped me understand that I was wrong. They also helped improve my confidence in another approach.

In this post, we’ll cover a handful of old school advertising targeting strategies and how you should test them against a more modern approach. Once you’ve tested, you can decide whether your stubbornness was right all along.

Testing Basics

Before we get to the old school strategies, it’s important to provide a framework for testing.

1. Use A/B Test.

I prefer to create A/B Tests in Experiments. Create the campaigns or ad sets that you want to compare first. Then go to Experiments and click to create an A/B Test.

Select the campaigns or ad sets that you want to compare. I ask you to test ad sets in two of the three examples below. In the third, you’d compare campaigns.

2. Focus on a Single Variable.

Everything about the two campaigns or ad sets should be identical except for a single variable. Since this post is about testing targeting strategies, everything beyond targeting should be the same. Make sure that there aren’t any other variables like placements or ad copy and creative that could result in differences in performance.

3. Your Key Metric

The Key Metric is what determines which campaign or ad set “wins” in an A/B test.

Make sure that this metric isn’t frivolous. What ultimately determines which ad set was better? If your goal is sales, then the key metric should be Cost Per Purchase. Do not use secondary metrics like CTR or CPC.

If your key metric is Cost Per Lead, you may want to take steps to measure the quality of those leads. Make sure that you send these leads to different forms so that you can keep track of them in your CRM.

4. Strive for meaningful results.

Your goal isn’t to find a winner quickly, it’s to find convincing results that actually mean something. Make sure that the budget dedicated to each competing campaign or ad set, combined with the length of the test, are enough to produce the volume that you need.

The longest you can run a test is a month. This would be my preference for a test that will help define your strategy going forward. Do not end the test early if a winner is found.

If the results become more convincing with time, that’s a good thing.

1. Interests and Lookalikes

There was a time when the ability to target people by interest, behavior, or lookalike audience was revolutionary. It gave advertisers targeting control and your ads were more likely to reach a relevant audience.

That isn’t always the case now. If you use Advantage+ Audience, any inputs you provide for detailed targeting or lookalike audiences will be suggestions.

This is why many advertisers have resorted to using original audiences. Targeting inputs in that case are more than suggestions — or we assume.

But, the reality is that even when using original audiences, your targeting inputs are rarely tight constraints. If you’re optimizing for conversions, link clicks, or landing page views, Advantage Detailed Targeting is automatically on.

If you optimize for conversions, Advantage Lookalike is automatically on.

In other words, we have no idea how much your selection of those interests and lookalike audiences actually matter. And based on my tests, they matter very little — if at all.

It’s not even clear that your audience suggestions matter when using Advantage+ Audience. They may actually be a detriment. This is why I recommend testing your current strategy with interests and lookalike audiences versus Advantage+ Audience without any suggestions at all.

Compare:

• Version 1: Original Audiences using Detailed Targeting or Lookalike Audiences
• Version 2: Advantage+ Audience without Suggestions

Key Metric: Cost Per Conversion (whichever event is most relevant)

Are you actually better off using original audiences to target interests or lookalikes? Maybe. But, prove it.

2. Gender and Age Control

One of the complaints I’ve heard from advertisers about Advantage+ Audience is the lack of control over age and gender.

You are only able to provide an age minimum within Audience Controls when using Advantage+ Audience.

Any age maximum or gender inputs you provide are audience suggestions. If Meta can get you more or better results by delivering your ads outside of those ranges, it will.

As a result, advertisers who feel these inputs are critical have favored original audiences. In that case, age and gender are tight constraints that will be respected.

Let’s assume that your customer is predominantly women aged 25-49. If Advantage+ Audience works the way that it should, whether or not ads are delivered to men or people outside of those age ranges will depend upon whether you can get your optimized actions from those other groups.

I’ve seen examples where businesses that serve women used Advantage+ Audience and 99% of the budget was spent on reaching women — even though gender is only a suggestion.

The key, though, is that you should optimize for conversions for this to be effective — preferably purchases. If reaching people who fall outside of expected gender and age range won’t lead to conversions, you’ll likely spend very little there.

Can you trust Advantage+ Audience without these controls? It’s worth testing for any type of conversion, especially purchases. Leads can be problematic since it’s possible you may get cheaper and lower quality leads this way — but, it’s worth testing. Engagement optimization is likely to go off the rails using Advantage+ Audience without those controls, but top-of-the-funnel optimization is problematic at its core.

Compare:

• Version 1: Original Audiences with Age and Gender Restrictions
• Version 2: Advantage+ Audience with Age and Gender Suggestions (if at all)

Key Metric: Cost Per Conversion (whichever event is most relevant)

Is it critical that you only reach people within your preferred demographic? Is it possible that Advantage+ Audience will waste money by reaching people outside of those groups? Maybe. But, prove it.

3. Remarketing

Look, my whole thing years ago was remarketing. I was generating a high volume of daily organic traffic, and ads allowed me to leverage this with highly relevant targeting.

But, things have changed. You can still target remarketing audiences. Those groups of people are surely just as relevant as they were years ago. What changed is the cost.

Targeting small groups of people is much more expensive than targeting large groups. Even though your website visitors may be three times more likely to convert, it may cost three (or five or 10) times more to reach them.

The other development is that Meta’s ad delivery algorithm has improved. Even if you use Advantage+ Audience without suggestions or go broad with original audiences, the algorithm will almost always prioritize a percentage of your budget to remarketing. We now know this due to Audience Segments.

When running Advantage+ Shopping Campaigns (or any Sales campaigns, if you have the update), you can breakdown your results by Audience Segments. I’ve often seen that between 25 and 35% of my budget is spent on people who have engaged with me (visited my website or subscribed to my email list) or bought from me.

Many advertisers continue to create campaigns with separate ad sets for prospecting and remarketing. But, since these two things happen at once without us even realizing it, is it still necessary?

For this test, we’ll need to compare campaigns since the old school approach is to use two ad sets. I would also use an attribution setting that is click only to prevent the remarketing ad set from inflating results with view-through conversions.

Also make sure that the combined budget of each campaign is the same. In other words, Version 2 using Advantage+ Audience should be the same as the sum of the two ad sets in Version 1.

Compare:

• Version 1: Campaign with Remarketing and Prospecting Ad Sets
• Version 2: Campaign with one Ad Set using Advantage+ Audience

Key Metric: Cost Per Conversion (whichever event is most relevant)

In addition to comparing the Cost Per Conversion, use your Breakdown by Audience Segments to see how your spend and results from remarketing compare.

Test Your Assumptions

I want you to test these because what I’ve seen from my own tests is quite clear. I’ve seen that…

1. Detailed targeting and lookalike audiences are rarely beneficial. Advantage+ Audience almost always gives me the same or better results.

2. Gender and age restrictions are rarely necessary. Especially when optimizing for purchases, the algorithm figures it out.

3. Remarketing is not the advantage it once was. It’s expensive to run stand-alone remarketing ad sets. Remarketing and prospecting happen together in the most optimal way now.

There are always exceptions, and I’ve even mentioned some of those cases in this post. But, if you are still utilizing some of these old school targeting strategies, I encourage you to run these tests yourself and allow for the possibility that more modern approaches may be more beneficial.

Your Turn

These are the types of tests that I often run to challenge my own assumptions. Once you’ve run these tests, I’d love to see your results.

Let me know in the comments below!

The post How to Test Meta Advertising Targeting Strategies appeared first on Jon Loomer Digital.

One of the challenges resulting from audience expansion and algorithmic targeting is the lack of visibility into who sees our ads. When the advertiser loses control over defining the target audience, how can we trust that our ads are shown to the right people?

Audience Segments can help.

Let’s take a closer look at this incredibly valuable tool…

What Are They?

When running Advantage+ Shopping Campaigns, advertisers have very little impact on who sees their ads.

When using Advantage+ Audience, most targeting inputs are used as audience suggestions only.

Even when using original audiences, an advertiser’s targeting inputs are often expanded — either manually or automatically.

The resultant mystery about who sees your ads can be frustrating. A benefit of Audience Segments is that advertisers can get visibility into how budget and performance are distributed between important groups.

Audience Segments allow advertisers to define people who are connected to their business:

• Engaged Audience: People who have interacted with your business but have not made a purchase
• Existing Customers: People who have bought a product or signed up for your services

Anyone who falls outside of these groups will be considered a New Audience. You will then be able to see how much of your budget was spent on each group — as well as how performance varied between them.

Audience Segments were first made available for Advantage+ Shopping Campaigns. In the beginning, advertisers were only able to define Existing Customers. Not only did this allow them to view breakdowns of results by customers and non-customers, but it could be used to set an Existing Customer Budget Cap for those campaigns.

The Engaged Audience segment would soon follow. Meta eventually rolled out Audience Segments for all sales campaigns — whether manual or Advantage+ Shopping. While it doesn’t allow for an Existing Customer Budget Cap on manual campaigns, these Audience Segments are enormously useful for reporting.

NOTE: You’ll know that you can leverage Audience Segments for manual Sales campaigns if you see this reporting section when creating a campaign.

Define Them

You won’t be able to leverage Audience Segments until you define them. To define your your Audience Segments, go to Advertising Settings in the All Tools menu. You may need to go to Ad Account Settings first.

Click the section for Audience Segments.

It will look like this…

1. Define Engaged Audience.

If you haven’t yet defined your Engaged Audience, expand this section and it will look like this…

You can select from existing custom audiences or create new custom audiences to define this group. You’ll want to use every method possible to help define people who have engaged with you. That includes Website, Customer List, App Activity, Offline Activity, Catalog, Lead Form, and Shopping.

Note that this will not include certain types of custom audiences like Page Engagement, Instagram Account Engagement, and Video View Engagement.

There will be overlap — not only between custom audiences within Engaged Audience, but between your Engaged Audience and Existing Customers. Do not worry about excluding people to prevent that overlap. People will only be counted once. If someone is shown your ad who exists in both the Engaged Audience and Existing Customers, they will only be counted as an Existing Customer.

Define this Audience Segment as throughly as possible. Here’s what mine looks like…

2. Define Existing Customers.

If you haven’t defined your Existing Customers, expand it and it will look like this…

You will want to define this based on people who have bought from you. There is a bit of confusion in Meta’s definition since it also includes “people signed up for your services.” I do not interpret that as being anyone who is on your email list (these people would be part of your Engaged Audience). A purchase needs to be made.

The most common ways to define this will be a segmented customer list of people who have made a purchase. I’ve created several Customer List Custom Audiences based on specific purchases as well as one that captures all purchases. I also use a Website Custom Audience based on the Purchase standard event that fired during the past 180 days.

Here’s what my Existing Customers Audience Segment looks like…

Depending on your business, you could certainly use Shopping, App Activity, Offline Activity, and and Catalog Custom Audiences, too.

Leverage with Breakdowns

Once your Audience Segments have been defined, you can leverage them for Ads Manager breakdowns going forward. How long will it take until it’s available? It could be as quick as a few minutes, or it could take longer.

Then click the Breakdowns dropdown menu in Ads Manager (between Columns and Reports).

You will have two different breakdowns that you can use.

1. Breakdown by Audience Segments.

This is found under “By Demographics.” When your Audience Segments are defined, your results will be broken down to include three separate rows:

• Engaged Audience
• Existing Customers
• New Audience

Here’s an example…

You may also see Uncategorized or Unknown. “Uncategorized” will appear when viewing campaigns that don’t qualify (not a Sales campaign). Keep in mind that not everyone has this for manual Sales campaigns.

“Unknown” may reflect that ads were delivered to people while your Audience Segments weren’t yet defined. I’ve also seen some results for Unknown temporarily before they eventually move to one of the two Audience Segments.

2. Breakdown by Country and Audience Segments.

This is found under “By Geography.” You will then get breakdowns by Audience Segments for each country where people were shown your ads. Here’s an example…

Examples of How I Use Them

I’ve had lots of fun using Audience Segments with both Advantage+ Shopping Campaigns and manual Sales campaigns. This feature was central to a test I ran to determine how much our audience inputs matter (and how much remarketing happens) when using various targeting approaches.

1. Advantage+ Audience without Suggestions.

I was curious how much of my budget would be spent on remarketing without providing any suggestions at all. It was a lot!

2. Advantage+ Audience with Suggestions.

If that much remarketing happens without providing suggestions, what happens when I provide suggestions that match my Audience Segments exactly? Maybe surprisingly, the distribution was virtually unchanged.

3. Original Audiences Using Custom Audiences with Advantage Custom Audience Turned On.

If I use Original Audiences, would Meta respect my inputs more before going broader? Once again, the custom audiences I used matched my Audience Segments. Distribution was again about the same.

4. Original Audiences Going Broad.

What about using Original Audiences and going broad? Well, still lots of remarketing!

5. Advantage+ Shopping Campaign Optimizing for a Complete Registration.

And finally, I created an Advantage+ Shopping Campaign that optimizes for the Complete Registration event to be consistent with what I did in the other four tests. So far, this is looking a lot like Advantage+ Audience without suggestions.

Here are my main takeaways from these tests:

1. Algorithmic targeting and audience expansion do indeed result in remarketing. This is a good thing!

2. It is unclear how much our targeting inputs matter when audience expansion and algorithmic targeting are at play. The distribution of my budget and results were all within a similar range for each approach.

Feel free to use Audience Segments for your own tests!

Your Turn

Have you started using Audience Segments? What have you learned from using them?

Let me know in the comments below!

The post A Guide to Audience Segments appeared first on Jon Loomer Digital.

There was a time when asking the question in today’s title would have been considered ridiculous. But, we can no longer ignore the trends. As much as we may want to fight it, targeting doesn’t matter nearly as much as it once did.

The way I phrased that was a soft landing. It left some wiggle room. Maybe it still matters, but not as much as before, right? But, let’s take it further.

It’s possible that targeting no longer matters at all.

Whoa, don’t come at me like that! I know. There are so many advertisers who still use the same strategy they’ve used for years. They insist it still works — and that it’s necessary. I know because I hear from them often.

I was one of those advertisers. If you were to go back far enough on this website, you’d begin to find quotes from me like this:

There are many factors that lead to success or failure of your Facebook ad campaign. But spoiler alert: Nothing is more important than targeting.

Or this:

Facebook ad targeting is one of the primary reasons why ads fail or succeed. You could have the perfectly crafted ad, but you shouldn’t expect it to work if it’s targeting the wrong people.

Things have changed. Our inputs mean less due to the move towards audience expansion and algorithmic targeting. In most cases, you can provide audience suggestions or inputs, but it’s questionable how much those inputs impact delivery.

And now that we have audience segments for sales campaigns, we can start to get more visibility into whether what we change matters at all.

Through my tests during the past month using four different ad strategies, it’s pretty clear: The strategy I chose did not seem to make any noticeable difference.

Allow me to explain…

Targeting Before

My quotes in the section above came from 2017, and I stand by them as being applicable at that point in time. Our targeting inputs absolutely did matter.

We defined the precise pool of people who should see our ads based on location, age, gender, custom audiences, lookalike audiences, and detailed targeting. Meta’s ad delivery optimization would then show ads to people within that pool who were most likely to convert.

If that initial targeting pool was flawed, we would not get results. Our inputs were important.

They were so important that Meta’s algorithm couldn’t fix broken targeting for us. It didn’t look at our inputs as suggestions. It didn’t prioritize our inputs initially before expanding to help us get better results.

We defined who could see our ads, and performance relied heavily on it.

Targeting Now

Of course, that’s no longer the case. The introduction of features like Advantage audience expansion and Advantage+ Audience means that we have various levels of control when it comes to who sees our ads:

• Things we definitively control
• Things we sorta control
• Things that have inconclusive impact and may not matter at all

The point of this article isn’t to say that today’s algorithmic targeting is better — or even that the control of the past was superior. Rather that it is what it is, and it’s possible that we’re paying far too much attention to factors we have little control over.

As we discuss control, I am going to focus only on cases when we’re optimizing for conversions. Otherwise, the factors that contribute to control will vary.

But, truthfully, if you’re able to optimize for conversions (and have conversion data available via the pixel or Conversions API), conversion optimization should be your priority. Top-of-funnel optimization is largely flawed, whether you control the audience or not.

What We Definitively Control

For each of these sections, we should differentiate between whether we’re using Advantage+ Audience or Original Audiences.

Advantage+ Audience: Factors We Control

• Minimum Age
• Languages
• Excluded Custom Audiences

These are within your Audience Controls. Meta will not serve ads to people under your set minimum age, within excluded custom audiences, or who don’t speak your selected language.

You may assume that location should fall here, too, but I omitted it intentionally. We’ll get to it.

Original Audiences: Factors We Control

• Minimum and Maximum Age
• Gender
• Languages
• Excluded Custom Audiences
• Custom Audiences (if Advantage Custom Audience is turned off)

There’s a bit more control here, but it’s minimal. When using Original Audiences, you can set a tight control on age range (both minimum and maximum) as well as gender. When using Advantage+ Audience, gender and custom audiences are suggestions (we’ll get to that). But when using Original Audiences, they are tight constraints.

What We Sorta Control

There’s one item that we omitted from above that we sorta control, and this applies to both Advantage+ Audience and Original Audiences: Location.

Way back in 2023, Meta changed our control over location targeting. Originally, we had four options:

• People living in or recently in a location
• People living in a location
• People recently in a location
• People traveling in a location

But, now it’s only “living in or recently in.” That means that you can’t isolate locals or travelers. This is why location is something we only sorta control.

No, Meta will not deliver your ads to people who don’t either live in or were recently in your selected location. You have control over that.

But, that doesn’t mean you have full control. If you want to only reach locals or travelers, you can’t. And that’s been a major frustration for advertisers.

What We Do That Has Inconclusive Impact

There’s a growing list of targeting inputs that we provide that may not matter at all. Or maybe they do. But, it’s not entirely clear whether they matter a lot, very little, or somewhere in between.

Advantage+ Audience: Inconclusive Impact

• Custom audiences
• Lookalike audiences
• Age maximum
• Gender
• Detailed targeting

All of these things are audience suggestions. It is entirely unclear whether they matter. Maybe they help give the algorithm initial direction. Maybe these suggestions are completely inconsequential.

Ultimately, your ads will be delivered to people who are likely to perform the action that you defined with your performance goal.

Original Audiences: Inconclusive Impact

• Custom audiences (if Advantage Custom Audience is turned on)
• Lookalike audiences
• Detailed targeting

When optimizing for conversions, Advantage Lookalike and Advantage Detailed Targeting are on by default and can’t be turned off. This means that your audience will be expanded and ads can be shown to people beyond those audiences.

In the case of custom audiences, you have the option of turning on Advantage Custom Audience.

Does that mean that if you get great results from your targeting inputs, expansion won’t happen? Does it mean that expansion always happens? Or does it mean that your inputs are no more than suggestions, like with Advantage+ Audience — this is simply a softer repackaging?

We don’t know. But, it’s entirely possible that inputs for audiences that can be expanded have minimal impact on delivery.

Here’s why I think that…

Look At This! (Targeting Test)

The image above is a summary of a test that I ran recently. It’s a sales campaign with four separate ad sets using a different targeting strategy:

1. Advantage+ Audience without suggestions
2. Advantage+ Audience with suggestions
3. Original Audiences using custom audiences with Advantage Custom Audience turned on
4. Original Audiences with no targeting inputs beyond country (going broad)

This test wasn’t about comparing performance (Cost Per Conversion) because too many factors impact that. But, if you’re curious, those results were almost exactly the same.

I was more concerned about whether my ads were delivered differently. I used audience segments to get an idea of how much of my budget was spent reaching my engaged audience and existing customers (in other words: Remarketing).

The difference was negligible and could be due to randomness, rather than the specific strategy.

Without getting too in the weeds of that test, my inputs or targeting strategy didn’t seem to have any impact on the distribution of my budget between remarketing and prospecting. At the very least, there’s strong evidence that at least 25% of my budget will go to remarketing, no matter what my approach.

The question we can’t answer is whether my strategy or targeting inputs impacted the prospecting audience. Since results are essentially the same, it would be logical to assume the difference is minimal. But, there’s no way to say for sure.

Does Remarketing Matter?

There was a time when remarketing made up a very large percentage of my advertising efforts. But, that’s no longer the case. More accurately, I no longer create ad sets that isolate custom audiences.

In the section about targeting we definitively control, custom audiences is listed under Original Audiences (assuming you turned Advantage Custom Audience off). You can still run remarketing campaigns. But, the question is whether you should.

As you can see in my pie charts above, between 25 and 35% of my budget was spent on remarketing using all four strategies. This includes using Advantage+ Audience without suggestions and Original Audiences while going broad.

I should also mention that it’s possible, if not extremely likely, that even more than that is spent on remarketing. Audience segments for engaged audience and existing customers do not include engagement custom audiences. So, we don’t know how much of my budget is spent on people who engaged with my ads, but didn’t click to my website, make a purchase, or join my email list.

While I don’t explicitly run remarketing campaigns, I’m still remarketing. And that’s kind of the beauty of how Meta is distributing my budget. Prospecting and remarketing happens all within a single ad set.

What This Could Mean

If what I’ve found in this limited test scales and isn’t a random blip, it should make you think about how you run ads.

It may not matter whether you use suggestions with Advantage+ Audience.

It may not matter whether you use Advantage+ Audience or Original Audiences and go broad.

It may not matter if you use Original Audiences with one of the targeting options that expands your inputs.

It’s quite possible that in all cases, Meta’s ad delivery algorithm will dedicate a similar percentage of your budget to remarketing and the rest to prospecting.

When I discovered this possibility, it was freeing. When you realize that none of your inputs make that much of a difference, you stop obsessing over how you do it. It allows you to focus more of your time on ad copy and creative.

But, just as importantly, you realize that all of those separate ad sets to segment your targeting were probably completely unnecessary. Because each ad set, assuming the audience was impacted by expansion, likely reached a very similar group of people. You’re better off consolidating your budget.

My takeaway is that Advantage+ Audience without suggestions is likely sufficient for me. And there’s no reason to run multiple ad sets in one campaign at the same time for the purpose of segmenting targeting groups.

The main exception to this could be if you need to tightly control the ads that are shown to individual audience segments, but that should not be the norm for most advertisers. And ultimately, you could hurt your performance by forcing such control.

So… Does Targeting Still Matter?

I don’t have a definitive answer for you. There’s still too much we do not know about the impact of our inputs and how our ads are delivered.

At the very least, our targeting inputs certainly mean far less than they did before. Remarketing isn’t necessary, in many cases. It’s possible that you only need to use Advantage+ Audience without suggestions now, assuming you’re optimizing for conversions.

I’ve seen enough to decide that these inputs are no longer impactful enough (if at all) to be all that concerned about them. Because it seems that no matter what approach I take, my ads get delivered in a similar manner.

Summary Grid

I put together a grid to summarize the level of audience control advertisers have over targeting, broken down by approach. I’ve been told that people like summary grids. So, here you go…

Your Turn

What’s your feeling about targeting these days? Does it still matter?

Let me know in the comments below!

The post Does Targeting Still Matter? appeared first on Jon Loomer Digital.

A long-running mystery in the era of algorithmic Meta ad delivery can finally be answered: How much do our targeting inputs matter?

I’ve run a test that reveals how much Meta distributes ad delivery between my remarketing audiences and prospecting while relying on algorithmic targeting and expansion. The results are surprising, encouraging, and enlightening.

This post is a bit of a rabbit hole, but it’s worth it. Let’s get to it…

Background and Historical Context

Years ago, targeting was simple. We made a series of selections using custom audiences, lookalike audiences, detailed targeting, and demographics. We then expected that our ads would reach people within those groups.

But, that all began to change with the introduction of Advantage audience expansion. At first, it was an option. Then expansion became the default for detailed targeting and lookalike audiences with certain objectives. And finally, Meta introduced the next level of hands-off, algorithmic delivery: Advantage+ Shopping Campaigns and Advantage+ Audience.

Luckily, Meta made audience segments available to provide important visibility into how Advantage+ Shopping Campaigns were delivered. We could then see how much of our budget went to our engaged audience, existing customers, and new audience (or prospecting). This was critical since these campaigns didn’t allow for any of the audience inputs we typically expected.

Meanwhile, advertisers confronted with the unknown of how Advantage+ Audiences delivered their ads often chose the greater control found with original audiences. But even then, audiences often expanded. The mystery went unanswered.

And then Meta expanded access to audience segments for all campaigns that utilize the Sales objective (this feature is still rolling out). While this includes Advantage+ Shopping Campaigns, it also applies to any manual campaign that utilizes the Sales objective. And this doesn’t require optimizing for a purchase.

This new option opened up a world of possibilities for testing and transparency. I recently wrote a blog post about the test I was starting. And now I’m ready to share my initial results.

My Test

The basis of this test was simple. I wanted to use audience segments to get a better sense of how my ads were delivered when using the following targeting setups:

1. Advantage+ Audience without suggestions
2. Advantage+ Audience with suggestions using custom audiences that match my audience segments
3. Original Audiences using custom audiences that match my audience segments — with Advantage Custom Audience turned on

This was all part of a single campaign that utilized the Sales objective and a website conversion location.

Since the purchase conversion event isn’t required for this objective, I used this test to promote a lead magnet that utilizes the Complete Registration standard event.

In terms of demographics, I used all ages in the countries of the United States, Canada, United Kingdom, and Australia. These are the four countries that make up the largest percentages of my customer base.

I initially started running the ad sets concurrently before I quickly switched gears and ran one at a time without distraction. I spent a modest $270 (or so, not exact) for each ad set.

I contend a large budget wasn’t necessary for this test since my questions were answered rather quickly. My focus wasn’t on whether any of these ad sets were “successful” in terms of generating conversions. Far too many factors impact Cost Per Conversion (the ad, the offer, the landing page), and that just wasn’t a concern here.

Granted, spending thousands of dollars would give me more confidence in these results. And I’ll certainly be monitoring whether what happened here continues with my advertising in the future. But, there were very clear learnings here, even with a modest budget.

My primary concern was simple:

1. How will ads get delivered?
2. How will my budget get spent?
3. How will it be distributed between my engaged audience, existing customers, and new audience?

We have answers.

Defining My Audience Segments

A critical piece to this test is how I’ve defined my audience segments. This is done within your ad account settings.

1. Engaged Audience. These are people who have engaged with my business but have not made a purchase. I’ve used a website custom audience for all visitors during the past 180 days and a data file of all of my newsletter subscribers.

2. Existing Customers. These are people who have made a purchase. I used website custom audiences and data file custom audiences for those who have bought from me before.

There will be overlap between these two groups, of course. A Meta representative confirmed that if anyone is in both groups, they will only be counted as an existing customer.

Once these are defined, we’ll be able to use breakdowns by audience segments in Ads Manager to see results of sales campaigns for each group.

Test Group 1: Advantage+ Audience Without Suggestions

This may have been the biggest mystery of all. When you use Advantage+ Audience without suggestions, who will see your ads?

Meta gave us some clues in their documentation, indicating that remarketing was likely a big part of where delivery starts.

But this passage isn’t definitive, and I wanted to prove this actually happens — or doesn’t. Well, it happens. Boy, does it happen.

I didn’t provide any audience suggestions, yet a very large chunk of my budget was spent on remarketing to my defined audience segments. More specifically, percentages dedicated to my engaged audience and existing customers…

1. 35.4% of amount spent
2. 23.7% of impressions

That’s incredible. I never would have expected the percentages to be that high. Note that the percentage of impressions is lower because the CPM to reach my audience segments is nearly twice as high as that for the new audience.

This is a relief. While I’ve trusted in Advantage+ Audience up until now, I generally provide audience suggestions because of that small amount of doubt in the back of my mind. But, this proves that Advantage+ Audience doesn’t require suggestions to reach a highly relevant audience.

Test Group 2: Advantage+ Audience With Suggestions

This got me thinking. If Advantage+ Audience without suggestions results in spending 35.4% of my budget on remarketing to my audience segments, what would happen if I provided suggestions? More accurately, what if I provided suggestions that were custom audiences that exactly match the definitions of my audience segments?

It’s reasonable to assume that even more of my budget would be dedicated to these groups. Once again, if we were to take Meta’s explanation of how Advantage+ Audience works, that’s a reasonable explanation. Meta says that if you provide an audience suggestion, they will “prioritize audiences matching your suggestions, before searching more broadly.”

Well, here’s what happened…

So that you don’t have to do any math, here are the percentages dedicated to my engaged audience and existing customers when using audience suggestions that matched those audience segments…

1. 32.4% of amount spent
2. 29.0% of impressions

By comparison, here are the percentages when not providing any suggestions:

1. 35.4% of amount spent
2. 23.7% of impressions

So, a higher percentage (by 3%, but still higher) of my budget was spent on reaching my audience segments when not providing suggestions than when using custom audiences that matched those audience segments as suggestions. While the percentage of impressions dedicated to those groups was higher, that’s because the CPM to reach my new audience was higher with this approach.

If we hadn’t first tested Advantage+ Audience without suggestions, we’d say that this test proved that Meta did in fact prioritize reaching the audience suggestions before going broader. But, since at best there was no difference in prioritization when not providing any suggestions at all, it’s inconclusive.

My take: Audience suggestions are optional, and in some cases they are unnecessary. If you have an established ad account with extensive conversion and pixel history like I do, you probably don’t need it. In fact, it may even be (slightly) detrimental.

Test Group 3: Original Audiences Using Advantage Custom Audience

Many advertisers have chosen to use original audiences instead of Advantage+ Audience because they don’t trust the lack of transparency of algorithmic targeting. So, I wanted to test one more thing that could be proven with audience segments.

Audience segments won’t help us with better understanding ad distribution with Advantage Detailed Targeting or Advantage Lookalike. While they will help us understand how many of the people reached were already connected to us, it won’t answer questions about how much the audience is expanded — and how that compares to using Advantage+ Audience with or without suggestions.

But, we can learn a lot from how expansion works with Advantage Custom Audience. In that case, Meta should prioritize the custom audiences we provide before expanding and going broader. Technically, it may not have to go broader, and we don’t know how much broader it goes when it does.

So, I ran a test that was similar to the one where I used Advantage+ Audience with suggestions. In this case, I used original audiences and provided the custom audiences that match my audience segments. And then I turned Advantage Custom Audience on.

Here are the results…

Here’s how that breaks down by budget spent and impressions towards the original custom audiences…

1. 26.4% of amount spent
2. 24.1% of impressions

Interesting! In this case, we’d assume that the audience would expand the least and a higher percentage of the budget would be spent on the custom audiences. But, this approach actually resulted in the lowest percentage of budget spent on those groups. The percentage of impressions dedicated to those groups is about the same as when using Advantage+ Audience without suggestions.

Another point to note is that the overall CPM was highest with this approach, though it’s not much higher than when using suggestions. That’s largely driven by a higher CPM to reach the new audience.

The Results: Overall Evaluation

To recap, here are each of those ad sets in one view…

There’s no reason to split hairs here about which approach led to spending more or reaching more of my audience segments. It’s within a margin of error related to randomness that could flip if we tested again — or continued the test.

The main takeaway is this: The overall breakdown in distribution between my remarketing audience segments and new/prospecting audiences was virtually the same for each approach. It made very little difference when using Advantage+ Audience without suggestions, Advantage+ Audience with suggestions, or original audiences using custom audiences and Advantage Custom Audience turned on.

This provides strong evidence that Advantage+ Audience does exactly what Meta says it does. At least in my case, there’s strong evidence that using suggestions is completely unnecessary — or marginally impactful.

I’m also a bit surprised that using the original audiences approach resulted in as much expansion as it did. I expected delivery to hold closer to the custom audiences that I provided — at least in comparison to using Advantage+ Audience.

I didn’t want Cost Per Conversion results to be a distraction in this test because they were not a priority when evaluating distribution. But in case you’re wondering, those results followed very similar trends. Each ad set generated virtually the same number of conversions (within a range of randomness). But, Advantage+ Audience without suggestions provided the most conversions, followed very closely by the other two approaches.

Contributing Factors

It’s important to remember that while these results are generally reflective of how algorithmic ad delivery distributes our ads, they are also unique to me and how this test was set up. There are several factors that may have contributed to what I saw, and you may get very different results.

1. Budget. As I’ve said before, a lower budget still gives us meaningful information here. But, it’s reasonable to expect that the more money I spend, the less will be spent on my audience segments, audience suggestions, or custom audiences. Those audiences will become exhausted and more would likely be spent on the new audience.

2. Audience segment sizes. Very closely related to budget, but this clearly impacts the volume of results I can see from remarketing to these groups. The total sizes of these groups for my test are roughly over 200,000, but closer to 100,000 when limited by the four countries I targeted. The smaller this pool, the less can be spent there.

3. Time elapsed. It’s reasonable to assume that the greatest distribution to these audience segments and custom audiences will happen in the beginning, prior to growing expansion to new audiences. This is again related to the sizes of the audiences and the rate of exhausting them. None of these ad sets ran for a full week, so those percentages would likely drop with time.

4. Conversion event. Since I’m still in the very early stages of analyzing results using audience segments, it’s not clear how much the conversion event used for optimization impacts distribution. We know it does — Meta will make algorithmic changes to find people willing to perform the action that you want. But, it’s not clear how much the event impacts distribution to audience segments, if at all. I used Complete Registration for the conversion event here. Distribution may be different for purchases or custom events.

5. Ad account history. There’s a strong argument that can be made that I should use Advantage+ Audience and there’s no reason to provide audience suggestions. But, that doesn’t mean that’s the case for everyone. It’s possible this is viable for me because of an extensive ad account history with pixel and conversion data to pull from. New accounts, new pixels, or websites that get minimal activity may not have the same advantage. They may see much different results here.

6. Campaign construction. I went back and forth on how to run this. I didn’t run this as an A/B test because I wanted to evaluate natural distribution, rather than forcing delivery without overlap. I also chose to run these ad sets at separate times, rather than concurrently. Even though they ran separately, overlapping delivery was likely (some people may have seen the same ad from multiple ad sets). These decisions likely impacted my results.

Overall, this has been a fun test, but it’s also incomplete. These are numbers I will continue to monitor with my ads going forward to see how it plays out in the future.

Your Turn

Have you run a similar test of manual sales campaigns to see how ads are distributed for you? What did you learn?

Let me know in the comments below!

The post How Meta’s Algorithmic Audience Targeting Impacts Ad Distribution: A Test appeared first on Jon Loomer Digital.

Now that audience segments are available for manual Sales campaigns, it opens up all kinds of fun opportunities for testing and learning. That’s what this post is all about.

If you’re not familiar with audience segments, they’re set within your Ad Account Settings. You can define your Engaged Audience and Existing Customers.

This information can then be used to provide greater insight into your reporting. Using breakdowns, you can generate separate rows for each of these groups — as well as “New Audience” (those who qualify for neither group).

This transparency was helpful, even necessary, for Advantage+ Shopping Campaigns, which are driven almost entirely by algorithmic targeting without the advertiser’s input. These audience segments help answer questions about who is seeing our ads.

Now that audience segments are available for manual Sales campaigns, we can use this to answer some similar questions that have gone unanswered — until now.

Here are three ways you can leverage audience segments to get greater insights into your manual Sales campaign reporting. At the end, I’ll also provide some surprising results of what I’m seeing…

1. Advantage+ Audience without Suggestions

When you create an ad set that uses Advantage+ Audience, you have the option of providing an audience suggestion.

If you don’t, ad delivery will be entirely algorithmic. Meta says that their “AI uses lots of information to find your audience” — like past conversions, pixel data, and prior engagement with your ads.

Here’s a screenshot of that explanation…

That sounds a whole lot like remarketing, right? In other words, even if you don’t provide an audience suggestion, Meta’s AI should — in theory — prioritize showing ads to people you’d otherwise select to target.

I’ve long wondered whether using an audience suggestion mattered. I’ve decided that while it may not make a difference, it can’t hurt.

But, what actually happens? Does Meta’s AI prioritize remarketing audiences like their documentation claims?

Thanks to audience segments, we can test this. Define your audience segments as thoroughly as possible.

This is how I defined my Engaged Audience…

And my Existing Customers…

Next, create a Sales campaign with Advantage+ Audience without providing an audience suggestion. You will then be able to use Breakdowns by Audience Segment to see how many of the people you reached fall within Engaged Audience, Existing Customers, or New Audience.

2. Advantage+ Audience with Suggestions

We can also use audience segments to help answer our questions about whether providing audience suggestions makes a difference.

As I said in the prior section, I tend to use audience suggestions. It’s partly out of habit. But it’s also out of a belief that, at best, it can help the algorithm get started. At worst, it shouldn’t hurt.

Back to Meta’s documentation. If you provide an audience suggestion, Meta says that they will “prioritize audiences matching your suggestions, before searching more broadly.”

Again, let’s screenshot this for emphasis…

In theory, if we were to provide suggestions matching our Engaged Audience and Existing Customers, we should see Meta’s explanation above reflected in our breakdown by audience segments.

So, let’s do that! Create a Sales campaign using Advantage+ Audience. Provide audience suggestions that match your definitions of Engaged Audience and Existing Customers exactly.

The reason for this approach is simple. There’s no reason to provide detailed targeting or lookalike audiences as suggestions since we can’t use those to define our audience segments. Because of that, we’ll never know for sure whether people in those audiences saw our ads.

Since we’re told that Meta AI will prioritize our audience suggestions before going broader, we can prove that one way or another by using the exact custom audiences for suggestions that we used to define our audience segments. When we breakdown our results, we should see that reflected.

In theory, of course.

3. Original Audiences with Advantage Custom Audience

I’ve mostly abandoned original audiences (and Advantage expansion tools that go with them) since the rollout of Advantage+ Audience.

Original audiences feel like old strategies, and we should use Meta’s new and improved tools. Advantage+ Audience works in much the same way that Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience, but Meta says that Advantage+ Audience is better and more advanced.

Back to Meta’s own documentation on Advantage+ Audience, this is spelled out:

Meta’s original audience options, including Advantage options (Advantage detailed targeting, Advantage custom audience and Advantage lookalike), can limit the potential of Meta’s AI which can be less effective.

Based on Meta’s own words, we assume that these work similarly, but Advantage+ Audience has the ability to go much broader (and lead to better results). So, the assumption is that if you turn on audience expansion with original audiences, the audience will expand — but your original inputs may be more respected.

Once again, we need to stick with the topic of custom audiences since these are what can be verified with audience segments. If we provide all of the same custom audiences that were used in our audience segments and turn on Advantage Custom Audience, what would happen?

How many of the people reached would be from our custom audiences? How many would be from expansion? And how does this compare to when using Advantage+ Audience?

We can test this! Once this is set up, use the breakdown by audience segment to see how your ads are distributed.

Initial Learning

I actually started part of this test already. The early results represent a small sample size, and in some cases they have been surprising.

It’s not clear how much the conversion event matters. Will your Engaged Audience and Existing Customers be used differently depending on whether you optimize for a purchase, lead, or something else?

Other factors like the sizes of the audience segments, sizes of the custom audiences used for suggestions, budget, and time may all contribute.

My initial test used a custom event for 60 second website views as the conversion event. The results were staggering. When providing audience suggestions, less than 1% of my budget was spent on them. When providing no suggestions, it was only slightly better.

But, I started a new test and the results have (thankfully) adjusted. Distribution to my Engaged Audience and Existing Customers has increased significantly, regardless of which approach I’m taking. These results have increased my faith in Meta’s claims that remarketing happens, regardless of whether you provide audience suggestions.

I’ll hold off on sharing specifics until I’m done. Until then, I encourage you to test this, too.

A Note on “Sales” Campaigns

Something that flies a bit below the radar is that you don’t technically need to optimize for purchases when running a Sales campaign. Because of that, you could run tests like I describe in this post while optimizing for any website conversion event (leads, registrations, custom events, and whatever else you use).

Sales is simply how you defined your campaign objective.

It doesn’t determine how your ads are optimized. This is defined by your performance goal and conversion event.

This is the case with Advantage+ Shopping Campaigns, too. Yes, it’s super confusing. You don’t need to optimize for SALES when running Advantage+ Shopping or manual Sales campaigns.

Your Turn

Have you run a test like this? What have you seen?

Let me know in the comments below!

The post Leverage Audience Segments for Manual Sales Campaigns appeared first on Jon Loomer Digital.

CPM (Cost Per 1,000 Impressions) may be the most impactful and frustratingly erratic metric when it comes to your Meta ads performance. How much it costs to reach people can override the greatness — or terribleness — of your ads.

Some of the choices you make will contribute to CPM in a positive or negative way. But, other factors can result in temporary or random CPM spikes that can lead to a misunderstanding of performance.

I’ve been guilty of this, too, and it led to this blog post. I want you to avoid this mistake while understanding how this sometimes invisible force can impact performance — and the perception of performance.

Example

I ran a lead generation campaign for two months to promote my Beginner Advertiser lead magnet that was reasonably efficient at $2.24 per lead. The problem was that the lead magnet it promoted wasn’t for my optimal audience.

I offer a Beginner Advertiser email sequence because it’s something to get people started. But given the choice, I’d prefer to promote one of several other lead magnets that appeal to the intermediate to experienced advertiser.

Unfortunately, I’ve struggled to get the same results lately with these other lead magnets. Leads often cost at least twice as much as those for the Beginner offering. Knowing that, I often abandon these campaigns rather quickly and go back to the one I know works.

The latest example is a lead magnet for my Cornerstone Advertising Tips. What I love about this lead magnet is that it is a much longer commitment. While the Beginner offering is over after eight quick emails, Cornerstone is a weekly tip that will go for several months. It’s also much more advanced than the option available for beginners.

I’ve spent more than $200 to promote Cornerstone so far, and the results just aren’t close. I’m spending $5.41 for these leads, which is more than twice what I pay for beginners.

The assumption was that the offer for Cornerstone just isn’t as appealing to a wide-ranging audience the way Beginners is. I’d likely need to spend more time on the ad copy and creative to improve it, but that CPA gap may be too much to overcome.

But, once I started scratching below the surface, it became a bit less discouraging. It didn’t take long to realize that there was nothing wrong with this lead magnet. The cost discrepancy could be traced almost entirely to CPM.

As you can see in the example above, the difference in costs isn’t due to the offer or conversion rate. An impression for the Cornerstone offering is more likely to produce a lead (.59%) than an impression for Beginners (.41%). In each case, about 1% of those reached became a lead.

The biggest difference here is CPM. It costs 3.2 times more to reach people when promoting Cornerstone than when promoting Beginners.

Note that both ad sets utilize nearly identical settings:

• Optimized for Facebook Leads from Instant Forms
• Advantage+ Audience with a few custom audience suggestions
• United States, Canada, United Kingdom, Australia
• Advantage+ Placements

So, what’s causing this? I have a theory, but I’ll get to that later.

First, let’s dig a bit deeper into the various factors that contribute to CPM — both within and outside of our control.

Are You Driving Up CPM?

First of all, know that there are several ways that you can drive up CPM and make things more difficult for yourself.

1. Targeting Restrictions.

If you choose to forgo Advantage+ Audience in favor of original audiences, you’ll have the option of further limiting your potential audience.

When custom audiences are provided with Advantage+ Audience, they are only used as an audience suggestion.

But, if you provide a custom audience when using original audiences, you can choose to limit targeting to those people only.

Additionally, detailed targeting and lookalike audiences are used only as suggestions with Advantage+ Audience. In some cases, you can limit targeting to those inputs when using original audiences (in other cases, the audience may be expanded).

Of course, those who choose to use original audiences do this so that the audience can be restricted. But, that restriction also limits the algorithm, which often drives up CPM.

2. Demographics

When using Advantage+ Audience, only an age minimum set in Audience Controls is considered a hard constraint. Otherwise, gender and age ranges are considered audience suggestions and the algorithm can go where it needs to go to find your desired action.

If you switch to original audiences, you can prevent ad delivery to people outside of your age range and gender inputs. While this provides more control, it applies a restriction to delivery.

There are cases when limiting demographics can make sense. But, many advertisers assume it’s necessary when it’s not. That assumption can drive up CPM.

3. Geography.

There’s no secret that some countries are much more expensive than others to reach due to advertiser competition. I primarily target the United States, Canada, United Kingdom, and Australia knowing that it will cost more to reach people there, but there tends to be an acceptable tradeoff. You may not have a choice here, but the countries you target will impact CPM.

Some geographical decisions are avoidable, though. If you choose to limit your audience to certain regions, states, or cities, that limitation restricts your potential audience size. While you might have a good reason for this, expect a higher CPM.

4. Placements.

Meta wants you to use Advantage+ Placements, which makes all placements available for delivery. If you want, you can manually remove placements, which will limit potential options for the algorithm.

Sometimes it makes sense to remove placements to prevent the algorithm from finding low-quality actions that can happen in specific locations. But in other cases, you may do this unnecessarily and hurt performance.

5. Estimated Action Rate.

One of the factors that impact your performance in Meta’s ad auction is the Estimated Action Rate. This is the estimate of the probability that a person will engage with your ad. A high Estimated Action Rate could help you win the auction with a lower bid. A low Estimated Action Rate could have the opposite effect.

Essentially, this is all about creating ads that inspire the action that you want. If you don’t do this well, you can drive up CPM.

6. Low-Quality Ads.

Another factor that contributes to auction performance is ad quality. This has nothing to do with Estimated Action Rate. Instead, Meta uses signals from users to detect click bait, engagement bait, and other signs of low-quality ads that push the lines of the ad policy. Low-quality ads will lead to higher CPM costs.

Uncontrollable Factors

While your micromanagement of an ad campaign can drive up CPM costs, there are other factors that are completely outside of your control. While you could conceivably include industry in the mix here, I want to focus on things that are variable from day to day or week to week (your industry tends to be static).

1. Competition.

The more money in the system looking to target the same audience you want to reach, the higher your costs can go. This can be due to seasonal competition, and tends to be reflected in spikes beginning before Black Friday and dropping after the new year.

But, there can also be completely random competition increases as well since you don’t control what other brands and advertisers choose to do.

2. Learning Phase.

Ad delivery and performance are least stable during the Learning Phase. You’ll often see this reflected in an inflated CPM during this time. Even when my ads never enter learning, I’ve found that CPM tends to be higher during the initial days of the ad set.

3. Randomness.

Sometimes you just can’t explain it. CPM costs can rise and fall for no particular reason. More accurately, there’s certainly a complicated reason that combines several factors that mostly happen behind the scenes, but you won’t always have a clear reason to explain it.

In other words, you shouldn’t obsess over CPM since there’s always going to be a randomness to it that is unpredictable and can’t be controlled.

CPM is a Secondary Metric

CPM is an important metric, we can’t deny that. As you saw with my example at the beginning of this post, you can have everything else go right, but an inflated CPM can drastically alter your perception of a campaign. The opposite can happen, too. Maybe your campaign and ads are nothing special, but a low CPM can get you great results.

All this said, we can’t treat CPM as a primary metric. It’s not a Key Performance Indicator (KPI). In most cases, don’t make drastic changes to your advertising in an effort to lower your CPM.

The exception, of course, would be if you are otherwise restricting ad delivery in ways that you shouldn’t, and your micromanagement is driving up CPM. If you’re limiting ad delivery by demographics or placements, or using original audiences over Advantage+ Audience, it’s worth trying a more hands-off approach.

But, changing your performance goal or targeting the cheapest countries in an effort to get your CPM down is unlikely to get you better results. A lower CPM does not guarantee an acceptable Cost Per Action. It will be up and down, and it’s mostly best to understand that it’s a factor that is mostly outside of your control.

My Theory

Back to my example at the top. The one benefit of looking at CPM in that case is that it reassured me that I wasn’t necessarily doing anything wrong. It wasn’t a matter of people preferring the Beginner offer over Cornerstone. People were telling me (through their action rate) that they liked it just fine.

As noted, the setup of the Beginner and Cornerstone ad sets were nearly identical. Both used Advantage+ Audience with similar audience suggestions. Both used the same performance goal and left Advantage+ Placements on.

My theory is that because all performance indicators are positive, I just need to be patient. I’ve started, stopped, and tried again with two different ad sets so far that totaled four days and $200 in ad spend. While those early results seemed bad on the surface (which led me to make that first decision to turn it off), I need to let it keep going.

Strangely, the Learning Phase does not apply here. I don’t believe it was ever on. But, that doesn’t necessarily mean that my ad set will immediately deliver optimally.

I decided to go back and look at results at several of my ad sets during the first week compared to its overall average, and I found a common trend: CPM almost always starts high and trends downward after the first few days or week.

The other possibility is that the build-up to Memorial Day Weekend contributed to increased competition. I think it’s possible this is a minor factor, but I have my doubts that it’s the primary driver.

Your Turn

Have you seen that CPM impacts ad performance? What do you do about it?

Let me know in the comments below!

The post Meta Ads Performance and the Impact of CPM appeared first on Jon Loomer Digital.

It’s a common problem. Meta advertisers, in search of the perfect combination of advertising strategies, overcomplicate things and make it worse. Several factors contribute to this problem.

First, we assume that “complicated” means “better” and “sophisticated.”

How could an Advantage+ Shopping Campaign with no targeting inputs and one ad set perform better than my execution of a 10-step strategy with multiple campaigns, ad sets, and audience segmentations?

Second, our answer to fixing a campaign that isn’t working as we’d like is to tweak, add, and add some more. Duplicate the campaign, create a new ad set, try a new optimization, or target a new group.

It’s not that the simplest strategy is always the best. But, less complicated strategies provide more clarity. By simplifying, you aren’t driving up your costs by competing against yourself or restricting the algorithm. If something isn’t working as well as you’d like, the focus is on ad copy and creative, your offer, and your website.

Look, I was once the king of complicated strategies. My favorite thing to do was create complex Evergreen Campaigns that used 10 ad sets to move a very small number of people through an ad funnel. But, things have changed.

Some advertisers are stuck in the past. Others are frustrated with their results and are trying everything. And we listen to far too many “gurus” with complicated strategies that sound good because of their complexity.

Limit Unnecessary Complexity

Don’t take every recommendation in this post as gospel. There are too many factors that contribute to performance to decide that one human’s advice is best for everyone.

I’m not always right. Sometimes there’s nuance. And even my advice below should be taken as a general approach. I don’t want you to always do what I suggest. I just want you to think about these things.

Your main takeaway should be this: Make a conscious effort to limit unnecessary complexity.

“Complex” is rarely helpful. By adding more variables, you make it more difficult to understand what is working and what isn’t. You’re watering down your results. It’s difficult to know what you need to change to get things back on the right track.

Complexity can be created by adding campaigns or ad sets. It can result from micromanaging targeting or placements. It can even be found in testing your ad creative.

It’s not that you should never do these things. But, before you do, ask yourself whether it’s necessary. Is it truly helpful?

There are always exceptions to what I recommend below. It could be due to large budgets, specific company goals, or unique circumstances. I get it. Sometimes you can’t avoid it. But, understand why “simple” is often better.

Let’s discuss the main things that you can simplify…

1. Campaigns

I host regular one-on-ones and help clients think through their advertising. One of the first things I often see is an Ads Manager cluttered with a whole bunch of campaigns.

Campaigns for sales, leads, traffic, engagement, and awareness. Multiple campaigns for a single objective.

There are only two objectives that I would recommend are required for virtually every business:

1. Sales
2. Leads

If we really want to simplify things, an Advantage+ Shopping Campaign is often the best way to run a sales campaign. No targeting and one ad set. All of the focus is on your ads.

Regardless, focus on sales and leads — or on conversions of some kind. Everything else is extra and needs a good business reason for doing it. Top of funnel objectives are rarely worth the money because Meta doesn’t have a way to optimize for quality traffic or engagement. Instead, you’ll typically get a bunch of empty clicks.

Save the money you were going to use on those top of funnel campaigns and push them towards leads or sales. You will build awareness, engagement, and traffic incidentally with those campaigns.

Also make sure you actually need the multiple sales or leads campaigns before you create them. If you have specific business goals, multiple campaigns can be difficult to get around.

Just remember that the more campaigns you create, the more ad sets you create. And that can eventually become problematic, in the form of Auction Overlap, which can drive up your costs.

2. Ad Sets

This is connected to limiting your number of campaigns, but also not. If you create 20 campaigns, that’s at least 20 ad sets. That, by itself, could be a problem.

But, you could also have two campaigns that each house 10 ad sets. Maybe this is an exaggeration, but advertisers do it. In most cases, it’s completely unnecessary.

If you create multiple ad sets to segment your audience, for example, you are contributing to Audience Fragmentation. This makes your ad spend less efficient.

You can’t always avoid creating that extra ad set. But, whenever possible, aim to consolidate.

3. Targeting

Since targeting is the primary motivator for advertisers who create multiple ad sets, this is a good transition.

Targeting may be the best example of how advertisers overcomplicate things. While it made sense in the past, it almost never does now.

If you’re optimizing for some sort of conversion, you should use Advantage+ Audience (assuming you haven’t created an Advantage+ Shopping Campaign). Provide some audience suggestions and allow the algorithm to do its thing.

There’s no need to create multiple ad sets to test the use of different audience suggestions. Those suggestions are unlikely to be all that impactful anyway. They’re just a starting point. Once the audience expands, those multiple ad sets will be nothing but overlap.

If you prefer original audiences over Advantage+ Audience due to the perception of additional control, keep in mind that your targeting inputs are often expanded:

• Advantage Detailed Targeting and Advantage Lookalike are automatically on and can’t be turned off when optimizing for conversions
• Advantage Detailed Targeting is automatically on and can’t be turned off when optimizing for link clicks or landing page views

The audience is often expanding anyway.

Also, don’t assume that expansion is bad and needs to be avoided. While eliminating expansion can lead to good temporary results, it’s not scalable. You can’t, for example, keep targeting your email list and website visitors while spending $100 per day and expect to get good results beyond a short window.

Remarketing is mostly dead. First, the algorithm is smart enough now that it will automatically target people based on your conversion history, pixel data, and prior engagement with your ads. This is even the case when you don’t provide targeting inputs with Advantage+ Shopping or Advantage+ Audience.

I will still use my remarketing custom audiences as suggestions for Advantage+ Audience. Even then, I don’t know how much it matters. But, it gives me peace of mind that it’s at least starting with that group.

Simplify your targeting. Embrace the fact that your targeting inputs are far less impactful than they were in the past. Stop obsessing over isolating the perfect combination of demographics, detailed targeting, and lookalike audiences. Ditch creating multiple ad sets for the purpose of audience segmentation.

Don’t lose any sleep over it. This is a good thing because it allows you to focus on your copy and creative.

4. Budget

All of these things are related.

The vast majority of advertisers have a finite budget. You can’t spend more than a certain amount per day or month.

And yet, you’re spreading that budget across a cluttered list of campaigns and ad sets — many of which are unnecessary. You complain about bad results and your inability to exit the learning phase. And the whole time, this problem is easily solvable.

Create fewer campaigns. Create fewer ad sets. But spend the same amount. Consolidate your budget that was spread across campaigns and ad sets into fewer targets.

This will give you the best chance of spending enough to help the algorithm learn and generate optimal results.

5. Performance Goals

Your performance goal may be the most important part of the campaign creation process.

I know, the ad copy and creative are incredibly important. But, great copy and creative may not overcome the wrong performance goal. If you use the right performance goal, mediocre ad copy and creative could still get you acceptable results.

The performance goal is exactly that: It defines what you are trying to accomplish. This helps Meta know how to deliver your ads and who should see them. It helps determine whether your ad set is working or underperforming and something needs to be corrected.

What’s crazy to me is that this shouldn’t be complicated, but advertisers love to complicate it.

As discussed earlier, your priority should be to optimize for conversions of some kind. You can set a performance goal to Maximize Conversions or Maximize Value of Conversions.

And then define what exact conversion type is most important to you. It could be purchases, leads, complete registrations, or potentially something else.

The algorithm will then focus on getting you those conversions. It wants to make you happy.

But, don’t get cute.

If you optimize for link clicks or landing page views, the algorithm will be focused on getting you link clicks or landing page views. They could be accidental clicks, bots (before detected), or people who click on everything. But, these people may not have any interest in your ad or your product.

If you optimize for ThruPlay, the algorithm will find ways to get people to watch at least 15 seconds of your video. That includes prioritizing placements where users are forced to watch video ads and can’t skip them. You assume these people cared about your video, so you create remarketing campaigns to target them. But, many didn’t care.

Keep it simple: Set a performance goal that defines exactly what you want.

This is the only way that you and Meta’s ad delivery algorithm will be on the same page. You can’t complain about low-quality traffic if you didn’t define you wanted high-quality traffic. You can’t complain about not getting purchases if you told the algorithm you wanted add to carts.

6. Bidding

The ad auction is dependent on three things:

1. Your bid
2. The likelihood that someone will engage with your ad
3. Ad quality

Unlike the typical auction, your bid isn’t everything. The highest bidder doesn’t necessarily get the impression. And really, you don’t want that to be why you win the auction anyway.

If you don’t touch anything, Meta bids for you. In most cases, it’s using the Highest Volume bid strategy. Meta’s focus will be to get you the highest volume of optimized actions within your budget. If you optimize for Value, the Highest Value bid strategy is default.

Otherwise, you can use a Cost Per Result Goal, ROAS Goal, or Bid Cap.

But, in most cases, don’t bother. You’re usually going to be disappointed. You’re not going to get magical results because you set a Cost Per Result Goal of $.01 and Meta unearths people willing to buy your product at a penny per purchase.

More often than not, your manual bidding will lead to spending less of your budget and getting fewer or worse results. It’s not that you should never try manual bidding. But, it should mostly be used as a last resort when you can’t get anything else to work.

7. Placements

If your only active ad sets are optimized for some sort of conversion, this is the easiest step possible. Do nothing. Keep your hands off and use Advantage+ Placements.

It’s not that there aren’t low-value placements. Audience Network is notorious for generating low-quality clicks and video views. But, if you’re optimizing for conversions, the algorithm knows about these pitfalls, too. You can bet that very little, if any, of your budget will be spent there.

Not, of course, unless that placement leads to conversions. And to be clear, impressions that don’t lead to a direct conversion can have value, too. One user may see three or five ads before finally converting. Some of those lower-performing (and lower-priced) placements may contribute.

Where you need to be careful is when optimizing for anything other than conversions. As we know, Audience Network leads to low-quality clicks. And since you can’t set a performance goal of high quality link clicks or landing page views, Meta will fill your results with those clicks if you set a performance goal of link clicks or landing page views.

A similar problem is found in Audience Network Rewarded Video when optimizing for ThruPlay. Third-party apps monetize themselves with Meta ads for this placement. People can watch videos in exchange for virtual currency that is used in the app. These people don’t care about your video.

Of course, there are other examples. But, this is another reason why optimizing for anything other than conversions is a complicated game of whack-a-mole. You need to do all you can to control quality, and that includes removing problematic placements.

But, again, that’s not an issue when optimizing for conversions. Keep it simple and use Advantage+ Placements.

8. Ad Copy and Creative

Ad copy and creative are super important. If they aren’t the most important part of your advertising, they’re at least in the discussion.

But, you don’t need to overdo this.

Meta says that there’s no benefit to creating more than six ads for a single ad set. And if your budget is low, even those six will chop up your budget to the point of making results mostly meaningless.

As I’m sure you know, the algorithm will pick one or a handful of those ads rather quickly and run with them. This isn’t because those ads were clearly more effective at a high level of certainty, it’s because the difference is negligible and the algorithm had to run with something.

Create multiple ads if you have multiple ad ideas. But, don’t feel you need to create six. And don’t obsess over the results and what they mean from small sample sizes.

Sometimes, it’s best to create two or three ads and run with them. Not getting great results? Fine. Create two or three more. It doesn’t matter that you restart the learning phase because you weren’t satisfied with your results anyway.

9. Testing

I don’t want to completely minimize testing because it can be helpful. But, I also see advertisers stuck in a constant cycle of A/B tests that barely move the needle.

Over-testing happens when you don’t trust anything that Meta does automatically. You feel the need to scientifically define absolute winning ads and optimizations.

But, the testing itself costs money. Performance is almost always worse when you force the algorithm to A/B split the audience. And you’re not guaranteed to get results that are statistically significant that would have made the test more productive than simply running the ads the old fashioned way.

Again, there are exceptions. If you’re going to run a long-term campaign, testing ads makes sense. And if you have big budgets, knock yourself out.

But, these low-budget tests to find winning creative are virtually meaningless. Just run the ads. Let the algorithm sort it out.

You can “test” without always needing to find a winner. Give the system multiple ads to work with. Utilize Dynamic Creative or the text variations feature.

Once again, complicating things with a test isn’t always the best path to profitability. Sometimes the simplest approach is the answer.

10. Reporting and Interpretation of Results

What’s a good CPC for this industry? Is this an acceptable CTR? Why is my CPM so high? How can I get it down?

Just stop…

Meta offers limitless metrics that can distract you. Many of them provide some value. But don’t obsess over the secondary metrics.

Keep it simple. Focus most on your goal action and the cost per goal action.

Not getting the cost per goal action that you’re wanting? The secondary metrics can help tell that story. Maybe your conversion rate is great, but the CPM is going up due to competition. Or maybe the CTR is lower than normal, indicating that you need to improve your offer to get people to click. Or those secondary metrics are all solid, but you aren’t getting conversions — so you shift your focus to the landing page.

Stop freaking out about every metric. They’re all part of the story. But, only a couple truly matter. The rest are window dressing.

Your Turn

How have you overcomplicated your ad strategy?

Let me know in the comments below!

The post A Simplified Meta Ads Strategy for Optimal Results appeared first on Jon Loomer Digital.

The process of Meta ad targeting and audience selection has evolved significantly during the past few years. Advertisers have been pulled through audience expansion and into complete algorithmic targeting, largely against their will.

But, there are very good reasons why these changes, which come at the expense of advertiser control, were necessary. It started with regulatory pressure on Meta related to the misuse of targeting distinct groups in order to manipulate elections or discriminate. You could also point to a loss of reliable data due to changes in allowable tracking used for targeting.

And finally, there’s a matter of Meta’s own investment in AI and machine learning. There are times when Meta may be better at finding your ideal audience automatically than you could be manually.

These days, we’re given options. Advantage+ Audience is the default method for audience selection, but you are able to switch to Original Audiences.

Not without repeated warnings, of course…

It would be a lot easier if I could tell you to either always use Advantage+ Audience or always avoid it. It’s not that simple.

There are times when Advantage+ Audience makes the most sense. There are times when it’s probably a bad idea. But, most advertisers misunderstand when to take each approach.

It’s understandable why there’s so much confusion. Several variables apply. While Advantage+ Audience is rather straightforward, Original Audiences behave differently depending on your performance goal.

Once you better understand how each of these work, the strengths and weaknesses will begin to clarify. By the end of this post, I hope that you’ll have a better plan for when you should use Advantage+ Audience and when you should revert to the old ways.

How Advantage+ Audience Works

For each approach, let’s focus on what you can restrict, the inputs you can provide, and when Meta can expand targeting beyond your initial inputs.

Restrictions:

Audience Controls provide limited restrictions regarding who can see your ads.

Your ads will not be shown to people outside of your selected locations, minimum age, languages, or excluded custom audiences.

Note that there is not an Audience Control for maximum age or gender. Your ads will be shown to anyone who is likely to perform your goal action.

Your Inputs:

Your inputs are Audience Suggestions and they are not required. Suggestions can include custom audiences, lookalike audiences, age range, gender, and detailed targeting.

Note that these are all suggestions and not restrictive. Ads can be shown to people outside of your selected custom audiences, age range (assuming it’s within the age minimum Audience Control), gender, and detailed targeting.

If you don’t provide suggestions, Meta will begin with your pixel data, conversion history, and prior engagement with your ads while searching for people most likely to perform your goal action.

Expansion:

I don’t know if defining what Meta does here as “expansion” is accurate, but it’s a way to compare Advantage+ Audience with what can happen using Original Audiences.

Meta will initially prioritize your audience suggestions before going much broader. Ultimately, the algorithm will show your ad to anyone (assuming this is allowed by Audience Controls) if they are likely to lead to more of the action you want, as defined by the performance goal.

How Original Audiences Work

Original Audiences allow you to use targeting the way you “used to” use it — but not the way you did it several years ago. It just provides more control than Advantage+ Audience, though there are several variables that alter how it works.

Restrictions:

Meta will not deliver your ads to people outside of your selected locations, age range, gender, exclusions (custom audience or detailed targeting), or languages.

There are signs that detailed targeting exclusions may be going away, but Meta is currently saying that there are no immediate plans for such a change.

Your Inputs:

In addition to the audience inputs listed above in Restrictions, advertisers can provide custom audiences, lookalike audiences, and detailed targeting.

Expansion:

This gets somewhat complicated with Original Audiences. In some cases, Meta can serve your ads beyond your selected detailed targeting or lookalike audiences, and you can’t turn it off. Sometimes you have the option. And whether or not you have an option may be different, depending on your version of Ads Manager.

Advantage Custom Audience: When you provide a custom audience or group of custom audiences, you have the option to turn on Advantage Custom Audience. When on, your ads can be delivered to people beyond your selected custom audiences if it will lead to better results.

There is always an option to turn Advantage Custom Audience off when using Original Audiences, regardless of the performance goal.

Advantage Lookalike: Lookalike audiences allow you to create a pool of people who are similar to those who are connected to you in some way. When creating these lookalike audiences, you can focus on those who are within the top 1 to 10% of those most similar within a given country or group of countries.

When you provide a lookalike audience for targeting, Advantage Lookalike allows Meta to show your ads to people outside of your selected percentage if it will improve performance.

This cannot be turned off when optimizing for conversions.

Advantage Detailed Targeting: Advertisers can target people based on interests and behaviors on and off of the Meta family of apps using detailed targeting. Advantage Detailed Targeting allows Meta to reach people beyond those inputs if it will improve performance.

Similar to Advantage Lookalike, Advantage Detailed Targeting is on by default and cannot be turned off when optimizing for conversions.

While it appears to be Meta’s plan to make this the default when optimizing for link clicks and landing page views, it’s not currently the case for all advertisers.

A Summary of Control

There are multiple reasons to favor one approach over the other. For many advertisers, it’s a matter of control, even though that complaint isn’t always justified as a harm.

Let’s summarize the level of control for each approach…

Advantage+ Audience

Controlled:

• Locations
• Minimum Age
• Languages
• Excluded Custom Audiences

Audience Suggestions:

• Custom audiences
• Lookalike audiences
• Age range
• Gender
• Detailed targeting

Original Audiences

Controlled:

• Locations
• Age Range
• Gender
• Languages
• Excluded custom audiences

Optional Expansion:

• Advantage Custom Audience
• Advantage Detailed Targeting (all but for conversions, link clicks, and landing page views)
• Advantage Lookalike (all but for conversions)

Forced Expansion:

• Advantage Detailed Targeting (for conversions, link clicks, and landing page views)
• Advantage Lookalike (for conversions)

As a reminder, not all versions of Ads Manager have forced audience expansion when optimizing for link clicks and landing page views, but Meta announced this as a change.

When to Use Advantage+ Audience

Advantage+ Audience leverages algorithmic targeting, putting minimal limits on whom can be reached in an effort to get you the most desired actions at the lowest cost.

The assertion that Advantage+ Audience leads to lower costs is difficult to dispute (or prove false). The question is related to quality.

When you should use Advantage+ Audience can be summarized like this…

1. When optimizing for purchases. You could make the argument that you should instead use Advantage+ Shopping Campaigns, but Advantage+ Audience is a good option as well. The algorithm can’t be misled by low-quality purchases, since this isn’t a thing. It will do what it can to get you the most purchases at the lowest cost. If you desire higher value, you can optimize for Value instead.

2. When optimizing for other types of conversions. There is a caveat here since quality is something to monitor. But, I’ve found Advantage+ Audience to be plenty effective for running lead campaigns. When quality is a concern, you can also assess your lead forms or optimize for Conversion Leads instead.

When to Use Original Audiences

Choosing to use Original Audiences is less about leveraging a unique strength of this approach and more about avoiding a potential weakness associated with Advantage+ Audience. But, let’s be clear: Original Audiences merely help limit the issues associated with certain types of optimization.

1. Top of Funnel Optimization. Whenever possible, you should select a performance goal that is near the bottom of the funnel (conversions or leads). The algorithm’s focus is getting you as many of those actions as possible. But, if you optimize for link clicks, landing page views, post engagement, ThruPlay, or some other type of top of funnel action (and you have no choice), you should use Original Audiences.

Top of funnel optimization is already problematic because the algorithm does not care about generating quality link clicks, landing page views, post engagement, or ThruPlays. Its only focus is getting you that thing, regardless of who is performing the action. This is why Advantage+ Audience can make what is already a problem even worse.

Original Audiences allow you to put some guardrails on your targeting. You can isolate gender, age ranges, and even lookalike audiences and detailed targeting. Of course, if you have the update that forces audience expansion for link click and landing page view optimization, it’s less restrictive.

Keep in mind that having customers who are primarily a certain gender or within a specific age group isn’t enough to require Original Audiences. If you optimize for purchases, use Advantage+ Audience — the algorithm will focus on those most likely to purchase. But, this customer focus is more reason to switch to Original Audiences for the top of the funnel (though you should have made that switch anyway).

2. Remarketing. If you want to run ads that only reach people within a custom audience, Advantage+ Audience is not the method for you. The custom audience you provide will only be used as an audience suggestion. You should instead use Original Audiences.

The question is whether you need to run a “true” remarketing campaign. Some advertisers run general remarketing campaigns to their email list, website visitors, or people who engage with their ads because they assume these people are more likely to act on their ads. If that’s the case (and you’re optimizing for conversions), I still recommend using Advantage+ Audience and listing custom audiences as suggestions.

The only time when using Original Audiences for remarketing would be necessary is if you have a unique message that only people in that audience should see. Original Audiences will allow you to isolate that group.

Have a Reasoned Approach

I hope this post provides some clarity on how these two approaches work and when you might use both. Find what works for you. But, I ask that you make sure that your reasons for doing what you do are backed in facts and not assumptions.

If you assume that your targeting inputs are critical to the performance of your ads, you will likely prefer using Original Audiences in most cases. But, I encourage you to challenge that assumption. Experiment more thoroughly with Advantage+ Audience. Remember that your audiences are often expanded anyway when using Original Audiences.

Make sure that your reasoning for abandoning Advantage+ Audience is backed by a known weakness. If you primarily serve women, do not assume that if you optimize for purchases and use Advantage+ Audience that your ads will be shown to men. More than likely, it will be the opposite.

Your Turn

How do you approach when to use Advantage+ Audience or Original Audiences?

Let me know in the comments below!

The post Advantage+ Audience vs. Original Audiences appeared first on Jon Loomer Digital.

There is no single aspect of Meta advertising that has been impacted more during the past year than targeting. The change has been dramatic.

The problem is that advertisers aren’t so quick to move with these changes. They resist, and they’re mostly determined to apply their tried and true targeting strategies from years past.

In some cases, these strategies still work — or work well enough. In others, they fail miserably, and advertisers who misunderstand the current environment can’t figure out why.

During the course of the past year, I’ve published about 30 pieces of content related to the evolution of Meta ads targeting. I highlight the 20 cornerstone pieces below.

Consider this post a deep resource to help walk you through how to approach targeting now and in the future.

On a Macro Level

Let’s begin with a group of posts related to targeting, where we were, where we are now, and what we can expect in the future.

Where we’ve been…

1. The Ultimate Guide to Meta Ads Targeting: Some targeting methods are new and others have been around forever. This post provides a thorough overview of how you can control who sees your ads with targeting inputs.

Where we are now…

2. 6 Targeting Mistakes Advertisers Make: When ads fail due to the decisions made by advertisers related to who should see their ads, it’s usually due to one of these six things.

3. The Evolution of Who Sees Your Ads: So much has changed during the past few years. It’s important that we reframe how we look at “targeting.” It’s now more about who sees our ads, and that’s not always something we determine with targeting inputs.

4. Your Targeting Matters Less Now: This is something that advertisers need to understand and embrace. It’s not that targeting doesn’t matter at all. It’s not that going broad or audience expansion will always work better. It’s that, quite simply, our targeting inputs make less of an impact now than ever before.

5. How to Approach Meta Ads Targeting Now: Accept that things are different now. You can’t approach targeting the same way that you once did. You need a new strategy.

Where we’re going…

6. The Future of Meta Ads Targeting: I don’t have a crystal ball, but I do think I’m a good judge of where we’re going based on trends. And it’s pretty darn obvious.

7. Meta’s Removal of Detailed Targeting is a Reminder of What’s to Come: Meta rarely adds new targeting options. Instead, the news is almost always that targeting options have been taken away. This should be a sign of what is to come.

8. Targeting Will Get More Difficult: If you struggle to embrace the new world of ad targeting and your role in it, there is no relief in sight. If you don’t adjust, it will only get tougher.

9. Will Meta Remove All Interest Targeting? It’s not a crazy question. After all, Advantage+ Shopping strips away virtually all targeting inputs. Interest targeting also gets Meta in trouble at times due to misuse. So, might interest targeting eventually disappear?

Audience Expansion

The biggest changes to targeting during the past year and more are related to the expansion of who sees our ads beyond our targeting inputs — if we provide them at all.

10. Advantage Targeting: How Meta Audience Expansion Products Work: It all started here. I’ll candidly admit that I resisted. I didn’t like the idea that Meta could expand beyond my targeting inputs. Here’s how Advantage audience expansion works in three different forms.

11. Advantage Detailed Targeting Updates: Audience expansion isn’t perfect, and there are times when you should avoid it. Unfortunately, Meta is starting to limit how often you can avoid it.

12. Meta is Forcing Expanded Audiences for Top of Funnel Optimization: In this post, I make the case for why this is a bad change and what Meta needs to do to make audience expansion for top-of-funnel optimization viable.

13. Advantage+ Audience Best Practices Guide: Advantage expansion products may eventually be a thing of the past, replaced by Advantage+ Audience. This approach applies to any objective and uses your targeting inputs — if you provide any — as mere suggestions. This post outlines how this works and how you should approach it.

Today’s Strategies

Most people misunderstand my feelings about Advantage+ Audience and audience expansion. While I truly believe you should embrace and use it in specific situations, it’s also counterproductive in others. It’s a matter of understanding how these things work, what makes them powerful, and when they might fail.

14. Are Audience Suggestions Necessary?: We’ve reached an interesting point where it may make sense, based on on Advantage+ Audience works, not to provide any audience suggestions at all.

15. Should You Restrict by Demographic Group?: Some of these tools force us to confront our approach to targeting. This is a primary example. In the past, it’s been a must to restrict by demographic group to help the algorithm. But there are specific cases now when that may no longer be the case — but others when that restricted is necessary.

16. Ads Reaching the Wrong People?: Advertisers often make claims about ads reaching the wrong people based on comments, but that’s not a good gauge of whether you actually paid to reach them. Here’s what you should do instead.

17. No Gender in Audience Controls: One interesting quirk of Advantage+ Audience is that there are no controls for gender. You can provide gender as an audience suggestions but not as an Audience Control. In some cases, this actually isn’t a big deal. But there are exceptions.

18. When Broad Targeting Fails: While I’m generally looking forward and an advocate for the advancements in Meta ads targeting, there are specific cases when it’s still not ready for prime time. There are reasons it fails, and it’s something that Meta could fix.

19. Going Broad Isn’t Always the Answer: Whenever I write about the newer targeting options, it does not fail. I’ll get comments from people telling me how going broad, using Advantage+ Audience, or turning on audience expansion doesn’t work for them. That might just be the case!

20. When to Switch to Original Audiences: There are times when it does not make sense to use Advantage+ Audience due to weaknesses in how it works. Be aware of these examples because you may find yourself throwing money away. The expansion of your audience will only make what was already a problem much worse.

Your Turn

How have you adjusted to the evolution of Meta ads targeting?

Let me know in the comments below!

The post A Comprehensive Guide to Meta Ads Targeting: 20 Resources appeared first on Jon Loomer Digital.

Meta has begun to roll out an update to ad sets utilizing performance goals for link clicks and landing page views, which was originally announced in January. When using original audiences, Advantage Detailed Targeting is automatically applied.

If you missed that Advantage Detailed Targeting was turned on, you’d be forgiven. This design variation is not at all obvious. If you miss the new label, you won’t see that the audience may expand unless you hover over one of the tooltips.

This is a departure from the primary design I’ve seen when using conversion performance goals. In those cases, a message is highlighted in gray.

But, let’s back up. There’s plenty to unpack here. The signs are all around us that we’re headed towards a future of less targeting control, regardless of the performance goal. And that could be a problem, unless Meta makes some much-needed improvements.

In this post, let’s discuss:

• The current state of audience expansion
• Where expansion is effective
• Where expansion fails
• Where this is headed
• What Meta needs to do

At the bottom of this post, I’ve also recorded a video that summarizes what is going on.

Current State of Audience Expansion

Meta first unveiled audience expansion in 2021 with a suite of products that would eventually fall under the “Advantage” line. Here is how they work…

Advantage Detailed Targeting

Advertisers provide detailed targeting inputs that Meta prioritizes. Your audience can be expanded to reach people beyond that group if better results can be found.

Not long after its initial rollout, Advantage Detailed Targeting became a fixed default for any conversion-related performance goal. Otherwise, advertisers had the option of turning it on or off.

That, of course, changed with this latest rollout of Advantage Detailed Targeting for ad sets utilizing link clicks or landing page views performance goals.

Advantage Lookalike

The second of the Advantage expansion family, Advantage Lookalike works in a similar manner as Advantage Detailed Targeting. If Meta detects that better results can be found beyond the selected percentage of your lookalike audience, the percentage can be expanded.

For example, if you use a 1% lookalike, the audience could be expanded to anywhere from 2 to 10%.

Like Advantage Detailed Targeting, Advantage Lookalike is turned on by default for conversion performance goals and cannot be turned off.

The latest update to link click and landing page view performance goals has not been applied to lookalike audiences. Advertisers still have the option of turning this on or off in that case (for now).

Advantage Custom Audience

Next, Meta rolled out the ability to expand custom audiences if better results can be found. Unlike the first two features, there is always an option to turn Advantage Custom Audience on or off. There isn’t currently a case where it’s on by default (though this may change).

Of course, Meta then took things even further…

Advantage+ Audience

Beginning in August of 2023, Advantage+ Audience became the default way of selecting an audience in the ad set. Advertisers still have the ability to switch back to original audiences, where the three Advantage expansion tools may be applied.

When using Advantage+ Audience, any targeting inputs provided are seen as mere suggestions. You will reach people beyond that initial group, and providing suggestions is optional. If you don’t provide them, Meta will automatically begin with your pixel data, conversion history, and prior engagement with ads as a guide.

Advantage+ Audience is the initial default for all campaign objectives, regardless of the performance goal. When used, any targeting inputs — custom audiences, lookalike audiences, detailed targeting, and even gender and age maximum — are seen as audience suggestions, and your ads may reach people beyond those groups.

Where Expansion is Effective

While I initially resisted audience expansion (“I only want to reach the people I’m targeting!”), I’ve come around to it. But, it’s most effective for a unique set of circumstances.

Audience expansion (any of the Advantage expansion tools or Advantage+ Audience) can work because the algorithm is hyper-focused on finding your desired action, as defined by the performance goal. Your targeting constraints could conceivably restrict the algorithm from getting more of those actions.

This is especially true when optimizing for purchases.

Success is defined by getting more purchases within your budget. If your targeting can be expanded to find more purchases, that’s a good thing.

There’s no better example of this in action than Advantage+ Shopping Campaigns. Targeting inputs are virtually nonexistent, and yet Meta says that they lead to a 17% improvement in cost per acquisition and a 32% increase in return on ad spend.

Where Expansion Fails

For the same reason that audience expansion can be effective for purchase optimization, it often fails for anything else — especially when using a performance goal that represents a top-of-the-funnel action (link clicks, landing page views, ThruPlay, post engagement, and more).

The audience will expand beyond your inputs if more of the actions defined by the performance goal can be found.

This isn’t a problem when optimizing for purchases because getting the purchase is the ultimate determinant of success. The algorithm makes adjustments based on whether it can get you more purchases.

It’s a problem for everything else because quality then matters…

Your audience is expanded to get more link clicks or landing page views. But did these people do anything else after clicking? Were they bots? Where they accidental clicks? Were they people who click everything? The algorithm doesn’t care.

Your audience is expanded to get more people to engage with your post. But is this positive or negative engagement? Do they fit your typical customer profile? Is there any chance that they’d ever buy from you? The algorithm doesn’t care.

Your audience is expanded to get more leads. But were the email addresses provided valid? Are these people reachable? Will they open their messages and engage? Is there any chance they’d ever buy from you? The algorithm doesn’t care (unless you optimize for conversion leads, which isn’t reasonable for everyone).

In each case, you care. And that’s the problem. Audience expansion fails when there’s no control for quality. Your targeting inputs were the only remaining constraints to focus only on potential customers.

Where This is Headed

Look to the most recent developments to predict where this is heading…

1. Advantage+ Shopping Campaigns don’t allow for any targeting inputs.

2. Advantage+ Audience is now the default, and you’re discouraged from switching to original audiences.

3. Advantage Detailed Targeting is now on and can’t be turned off when using link click and landing page view performance goals.

Every new update puts less importance on your targeting inputs. More ways to expand the audience. Fewer controls to be able to target an exact group.

Given that Advantage+ Audience is the default for all objectives and performance goals now, I’m actually surprised that Meta would make this update to Advantage Detailed Targeting related to link clicks and landing page views.

My assumption is that the ability to switch back to original audiences (and utilize Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience) will eventually be eliminated. But, maybe this is a sign that such a move is further off in the future than I expect.

The bottom line is that Meta isn’t going to stop expanding audiences beyond your targeting inputs any time soon. We’re likely to see this forced for more objectives and performance goals in the future, even if you’ll be able to continue switching back to original audiences.

What Meta Needs to Do

I am not a fan of this latest update to Advantage Detailed Targeting. The reason can be found within the section about when audience expansion fails.

Optimizing for top-of-funnel actions is already problematic. But if Meta removes or de-emphasizes targeting constraints, we lose all checks on quality. It no longer matters who these people are. Meta only cares that they’ll perform the action that we want.

The solution isn’t that complicated, and it’s been needed for years. The evolution of audience expansion only makes it more imperative that Meta act on it.

There must be a way to optimize for quality top-of-funnel actions.

I’d be much more willing to use the link click or landing page view performance goals to promote my blog posts if I could require the algorithm to optimize for quality traffic — not just any traffic. This could be defined by time spent on the website, scroll depth, other conversions, and return visits.

I’d be much more willing to use performance goals related to post engagement if I could require the algorithm to optimize for quality engagement — not just any engagement. I want people who are likely to share my posts, provide thoughtful comments, and return to my content later.

This “quality” element could be an option when setting a performance goal. Do you care more about getting a high volume of actions? Leave it at the default. Do you care about quality? Check this box and expect to spend more.

If that were possible, the expansion of your audience becomes less problematic. The algorithm would expand to get more of the quality actions that you are wanting — and that is ultimately what would guide ad delivery.

This would seem like a natural solution that is good for everyone. Most importantly, advertisers would be willing to spend much more on actions other than conversions if there were an increased confidence in the quality.

Watch Video

I recorded a video about this, too, and you can watch it below…

Your Turn

Do you run ad sets optimized for link clicks and landing page views? What do you think about this update?

Let me know in the comments below!

The post Meta is Forcing Expanded Audiences for Top of Funnel Optimization appeared first on Jon Loomer Digital.

Meta launched Advantage+ Audience in August of 2023, but the vast majority of advertisers still struggle to leverage it properly. Most either resist it whenever possible or use it without knowing how it works.

Both are wrong.

Advantage+ Audience is a powerful improvement over original targeting methods. That said, it also shouldn’t be used in all situations. Additionally, you should reconsider campaign construction strategies due to how it works.

There are strengths and weaknesses to consider. With this post, I hope to provide clarity on when you should use Advantage+ Audience, when you shouldn’t, and how it impacts your overall approach.

The Basics

When you create an ad set, the default targeting method is Advantage+ Audience.

This is meant to streamline targeting, leveraging Meta’s ad technology to automatically find your audience for you. You can optionally provide an audience suggestion, and Meta will prioritize it before going broader.

Audience Controls

Audience Controls are your tight constraints. While the algorithm has mostly free rein to find your audience, these controls set a few strict guardrails.

Note that Audience Controls only consist of the following:

• Locations (people living in or recently in)
• Minimum age
• Excluded custom audiences
• Language (if it isn’t common to your selected location)

It’s important to note that there is no Audience Control for a maximum age or gender. This allows the algorithm more ability to find people who are likely to perform your desired action.

Of course, that could be an issue in specific circumstances. We’ll get to that.

Audience Suggestion

This is a unique approach, so advertisers may be inclined to provide an audience suggestion. While it can’t hurt, you should understand how Meta finds your audience if you don’t provide one.

Meta uses AI to find your audience, evolving as it learns. That audience may be based on:

• Past conversions
• Pixel data
• Interactions with previous ads

These are many of the sources you’d provide for an audience suggestion anyway. In other words, Meta’s AI should prioritize what is essentially a remarketing audience before going broader (there is evidence of this).

Another critical aspect of how Meta finds your audience will be the performance goal (and conversion event, if applicable). Understand that this is how performance is measured, so what you select will impact delivery.

But, maybe you want to provide an audience suggestion.

Remember that the Audience Controls act as a tight constraint that Meta won’t deliver beyond, but your audience suggestion is just that — a suggestion. Meta can deliver ads to people who aren’t part of the custom audiences, lookalike audiences, or detailed targeting inputs that you provide here.

The age ranges and gender are also merely suggestions. Meta won’t show your ads to people who are younger than the age minimum that you provide in Audience Controls, but ads may be shown beyond your age maximum since it’s a suggestion. And since there isn’t a control for gender, ads may be shown to people beyond your gender suggestion.

Switching Back

If you don’t want to use Advantage+ Audience, you can switch back to original audience options. There’s a link at the bottom of Advantage+ Audience to do this.

Meta discourages this. In fact, you’ll get a warning message that requires you to confirm that you want to switch back.

Here, Meta highlights the 33% lower cost per result based on an experiment run from March to June 2023.

Within Meta’s documentation, they also highlight the following stats:

• 13% lower median cost per product catalog sale
• 7% lower median cost per website conversions
• 28% lower average cost per click, lead or landing page view

The first two are the most meaningful. The third is actually a potential red flag, depending on your performance goal. We’ll address that when discussion when to consider using the original audience options later in this post.

If you switch back to original audiences, you’ll have all of the old options you’re used to.

Similarities and Differences

Trying to differentiate between Advantage+ Audience and the original audience options can be a challenge, especially when audience expansion using the original method is in play. But, let’s break it down…

Similarities

Going Broader. Whether you’re using Advantage+ Audience or the original audience options, targeting may be expanded beyond your targeting inputs.

When using the original audience options, Advantage Detailed Targeting audience expansion is automatically turned on and can’t be turned off when optimizing for conversions, link clicks, or landing page views. Advantage Lookalike is automatically on when optimizing for conversions.

If you don’t like the fact that your inputs are only suggestions for Advantage+ Audience, just know that your audience is often expanded using the prior methods, too.

Differences

Expansion Exceptions. As noted, Advantage+ Audience will apply to any objective or optimization, unless you switch back to the original audience options. When using that original targeting, you will have the option to turn on Advantage expansion depending on the optimization — and in some cases, you won’t have the option to turn it on.

Custom Audiences. Your custom audience inputs will always be an audience suggestion when using Advantage+ Audience. When using the original audience options, you’ll always have the option of turning expansion off.

Tight Constraints. When using original audience options, your inputs for age (minimum and maximum), gender, locations, and languages are all tight constraints. When using Advantage+ Audience, you can only provide Audience Controls for age minimum, locations, and languages. Any inputs you provide for age maximum or gender are only suggestions.

Going EVEN Broader. It’s easy to miss the differences between Advantage+ Audience and the original audience options, especially when optimizing for conversions. But, Meta says that “Advantage+ Audience creates the broadest possible audience” and that the original audience options (including Advantage expansion options) “can limit the potential of Meta’s AI which can be less effective.”

When You Should Use It

If you’re optimizing for conversions — especially purchases — you should use Advantage+ Audience over the original audience options.

The objections to Advantage+ Audience don’t hold much water in this case.

1. Going broader. Whether you use Advantage+ Audience or the original audience options, targeting will be expanded beyond your detailed targeting and lookalike audience options when optimizing for conversions. While you don’t have to expand beyond your custom audience when using the original options, the typically small sizes of custom audiences aren’t ideal for conversion optimization anyway.

2. Tight constraints. You can’t set gender or maximum age as an Audience Control when using Advantage+ Audience, but that shouldn’t be an issue when optimizing for most conversions (again, especially purchases). The algorithm learns and will adjust based on who is performing these actions and who isn’t.

Advantage+ Audience provides less control but fewer restrictions on the algorithm to help find more of the actions that you want.

When You Should Use Original Audience

There are a few cases when you should consider using the original audience options due to potential weaknesses in Advantage+ Audience.

1. Top-of-Funnel Optimization. Keep in mind that top-of-funnel optimization (link clicks, landing page views, post engagement, ThruPlay, etc.) can already be problematic due to quality concerns. The algorithm’s primary focus is getting you as many of that action within your budget, and there’s no concern for whether these people do anything else.

You can limit this, to a point, with tighter targeting constraints. Using original audience options, you can define your targeting audience with more specificity — and without turning on audience expansion. This could help assure that anyone who sees your ads will at least be in your target group (even if limiting expansion might increase costs).

2. Gender and Age Focus. This can especially be an issue if your customer is only a specific gender or age group. Women serving women entrepreneurs is an example. If optimizing for a purchase, the algorithm should sort out that your paying customers are only (or primarily) women and adjust delivery when using Advantage+ Audience. But if you optimize for something top-of-funnel, there’s little preventing men from engaging and commenting, which will only convince the algorithm that men should see your ads.

This can also be an issue with lead quality and it’s something worth monitoring. It’s not that Advantage+ Audience is especially susceptible to low-quality leads. This is a potential issue, regardless of your approach. But if you find that you’re getting low-quality leads, and especially if they fall outside of your target age and gender demo, you may consider switching back.

Should You Provide an Audience Suggestion?

This is something you should test and find what works for you. But based on my experience, there’s little to no risk in providing an audience suggestion. It’s just a matter of whether it’s necessary.

As discussed earlier, Meta will automatically find your audience based on a combination of your performance goal, conversion history, pixel data, and prior engagement with your ads if you don’t provide a suggestion. These are all things you’d likely focus on when entering that suggestion.

But here are a couple of situations to consider…

1. New Pixel or Ad Account. If you lack that historical data that Meta can leverage to find your audience, it will likely help to provide some suggestions as a starting point.

2. Different Demo. Maybe your content serves several distinct groups or there are various categories of customers. That history would theoretically be lumped together when Meta builds your initial audience. If you want to be sure that Meta focuses on a unique group that you serve, it may make sense to start with a suggestion.

How It Impacts Campaign Construction

Most advertisers miss this, and it’s a behavior I’m determined to help change.

The old school approach to campaign construction involved multiple (sometimes several) ad sets for cold targeting. You can make the argument (and I do) that this isn’t ideal even when using the original audience options when expansion is on. But it definitely doesn’t make sense when using Advantage+ Audience.

Assuming you are using the same optimization and ad creative, what would differentiate each ad set? While you can provide unique audience suggestions for each one, this is only the starting point of targeting before going much broader.

Even if these ad sets generate distinct audiences from your suggestions, that uniqueness disappears when Advantage+ Audience goes broader. In each case, the algorithm will attempt to get you more of the actions that you want. That original suggestion no longer matters (or likely matters very little).

The result: The overlap between audiences once Meta has moved beyond the suggestions will be significant. This auction overlap will unnecessarily drive up your costs.

It’s simply inefficient. Not only can you expect your costs to go up due to auction overlap, but creating separate ad sets can also prevent you from exiting the learning phase.

The eventual audience leads to the same place. Combine these ad sets for better results.

Bottom Line

There’s a lot to digest here, but keep it simple…

1. If you’re optimizing for any type of conversion, you should prioritize using Advantage+ Audience.

2. You may not need an audience suggestion, but feel free to experiment with them. They shouldn’t hurt you.

3. Advantage+ Audience isn’t ideal for top-of-funnel optimization (link clicks, landing page views, post engagement, ThruPlay, etc.), especially if your primary demo is limited by age or gender. This could even be an issue when optimizing for leads.

4. Reconsider your tried and true campaign construction strategies when using Advantage+ Audience. In most cases, only one ad set per campaign is necessary, otherwise you’re bound to generate overlap that will negatively impact results.

Don’t be afraid of Advantage+ Audience. It’s powerful and can help improve results. But be aware of both its strengths and weaknesses.

Watch Video

I recorded a video about this, too. Watch it below…

Your Turn

How do you use Advantage+ Audience?

Let me know in the comments below!

The post Advantage+ Audience Best Practices Guide appeared first on Jon Loomer Digital.

While pondering the changes related to targeting and optimization over the years, it struck we why so many advertisers struggle with understanding how their role has evolved with targeting: There’s a messaging problem.

I know there’s a messaging problem because I was dealing with it myself while I was trying to communicate it. The processes of targeting and optimization are beginning to blend into one. I think I found the solution.

We need to shift our focus to who sees our ads.

Sometimes people see your ads because they were included in your targeting inputs. Sometimes it’s due to Meta’s optimization for delivery. Both have been important. But the importance of both are changing.

Targeting and optimization were previously very different things. Now they are converging.

Maybe this doesn’t make sense yet. But once it does, it will help you better understand the systems at play which determine who sees your ads — and your role in them.

How People Saw Your Ads Pre-Expansion

Previously, advertisers had a critical role when it came to who saw their ads. They provided the initial targeting inputs.

Facebook (before there was Meta) then optimized to show your ads to people within that initial audience who were most likely to perform your desired action. This was “Optimization for Ad Delivery.”

Both were critically important. But optimization couldn’t fix bad targeting. If you provided a flawed pool of people to work with, you would not get good results.

For years, I contended that targeting was the most important advertiser responsibility. It could make or break your advertising.

How People See Your Ads with Expansion

That started to change pretty dramatically once Meta introduced Detailed Targeting Expansion (which eventually became Advantage Detailed Targeting).

Advantage Lookalike and Advantage Custom Audience soon followed.

The advertiser provides the initial targeting inputs. Just as important, they indicate a performance goal.

In some cases, the advertiser has the option to turn expansion on. In others (like when optimizing for conversions), it’s on by default and can’t be turned off.

When on, your initial inputs will be prioritized. But if Meta believes that you can get better results (determined by your performance goal) by expanding the audience, people who would not have qualified within your targeting inputs can see your ads.

The level of transparency has mostly been zero. Until recently, advertisers had no way to know how many of the people reached were via targeting inputs how how many were due to expansion. That’s at least partially corrected with the introduction of Audience Segments.

How People See Your Ads with Advantage+ Audience

While the mechanics of Advantage+ Audience sound very similar to expansion via Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience, there are some very important differences.

Any inputs you provide when using Advantage+ Audience are mere audience suggestions.

If you don’t provide any suggestions, Meta will start with your prior conversions, pixel data, and previous engagement with your ads. In other words, the targeting will prioritize remarketing without providing inputs.

Another difference from Advantage expansion is that after prioritizing that initial audience, it can go much broader. The focus will be showing your ads to people most likely to perform the action that you want (defined by the performance goal).

As is the case with Advantage expansion, there is minimal transparency regarding a breakdown of performance between your inputs and the algorithmically generated audience. But one can assume that a much larger percentage of those reached are found by the algorithm — especially since you don’t need to provide inputs at all.

How People See Your Ads with Advantage+ Shopping

Advantage+ Shopping Campaigns take this even further. The targeting inputs you can provide are virtually nonexistent. No interests, custom audiences, or lookalike audiences. You can restrict by country and set a current customer cap by defining your current customers in your ad account. But that’s it.

Otherwise, the people who will see your ads are determined via machine learning. All Advantage+ Shopping Campaigns utilize a conversions performance goal (either “most conversions” or “value of conversions“), and the conversion event will define which specific action determines success. This was previously locked in as Purchase, but you can now select any standard or custom event.

Who will see your ads? This is determined using machine learning based primarily on the conversion event.

The Performance Goal is Your Targeting Now

There’s a very good argument (okay, it’s my argument) that the performance goal is more important to who sees your ads than your actual targeting inputs. Especially now that your inputs are only suggestions with Advantage+ Audience and no inputs are provided with Advantage+ Shopping, it’s difficult to make the case that these inputs are as important as they once were.

But that doesn’t mean that you don’t have any role when it comes to determining who sees your ads. Even when you have no targeting inputs at all, like with Advantage+ Shopping Campaigns, there remains one very critical step.

Your performance goal is the targeting now.

If you want purchases, set a conversions performance gaol with Purchase as your conversion event.

If you want post engagement, set that as your performance goal. But don’t expect purchases.

This will automatically include some of the people you would normally target manually via remarketing audiences. The rest are filled in algorithmically based on the performance goal.

But here is something you need to understand: The audience won’t always be the same.

The people who see your ads when the performance goal is Conversions (with Purchase conversion event) will not be the same as the people who see your ads when your performance goal is Post Engagement. Your performance goal determines what you care about — and that is the central lever for determining how Meta optimizes and makes adjustments.

If your performance goal is for anything top of funnel (link clicks, landing page views, Post Engagement, or ThruPlay), you are very likely to run into quality issues. The reason is that Meta doesn’t care whether these people do anything else — because the assumption is that you don’t either. So your ads will be shown to people most likely to perform that action, which could be because they click on everything or a placement often results in that action.

The Role of Ad Copy and Creative

It’s popular to say that targeting has moved to your ad copy and creative (I even said it). While what you do with the ad is absolutely important, I contend it remains secondary to the performance goal.

That’s not to diminish the importance of ad copy and creative. It may be splitting hairs to say that one is more important than the other.

If you pick the wrong performance goal, you’ll need the perfect ad copy and creative to get any results.

If you create a sub-par ad, the right performance goal can help get you some results.

In both cases, your potential is limited. You’ll get the best results by setting the appropriate performance goal and being on top of your game with ad copy and creative.

The Future of Who Sees Your Ads

If you’re following the trends outlined here, it shouldn’t be all that difficult to predict the future of targeting. The continued focuses on privacy, tracking, and malicious uses of targeting (discrimination and manipulation of elections) makes the continuation of this trend a near certainty.

You may argue that your inputs still mean a lot because you use the original targeting options and don’t always optimize for conversions. You regularly find ways to avoid audience expansion.

But do not expect these options to remain. Meta clearly wants advertisers to use Advantage+ Audience. It couldn’t be more obvious that the original methods are on the way out.

And we’ll very likely see the hands-off targeting approach of Advantage+ Shopping Campaigns expanded to other objectives. Meta already hinted at this related to leads.

If you haven’t already started to shift from “targeting” to “people who see my ads,” it will be forced on you eventually. Might as well get used to it.

Your Turn

How do you see your role in who sees your ads?

Let me know in the comments below!

The post The Evolution of Who Sees Your Ads appeared first on Jon Loomer Digital.

Meta advertising has changed rapidly the past few years, and that leads to several common targeting mistakes. These errors are largely due to a combination of ignorance and stubbornness.

I admit that I miss the old days of Facebook advertising — before it was Meta advertising. More than anyone, I preached the gospel of targeting and the art of finding your ideal audience. I found great success with micro-targeting and I found great enjoyment in all of the levers I could pull with targeting to find what works and what doesn’t.

But things are changing. And while I was initially resistant to much of the change, I’ve grown to embrace it. You should, too.

If you’re making mistakes with targeting now, it’s likely one of these six things. And it’s impacting your results…

Improper Use of Exclusions

The improper use of exclusions can go two ways.

1. Nonexistent or incomplete use of exclusions.

The use of exclusions is a fundamental way to avoid wasted ad spend. It’s a step that’s often missed by beginner advertisers.

If you’re promoting a product that can only be purchased once, there’s no need to show ads to people who have already purchased it. If I’m promoting my Power Hitters Club – Elite membership, I will excluded current members using a custom audience exclusion.

While that customer list custom audience is a start, it’s unlikely to exclude all current members due to matching incompleteness. When an exclusion is required, you should exclude that group in as many ways as possible.

An example is if you ever run lead ads using instant forms while also having a landing page on your website, there are at least three different ways you can exclude people who already performed this action: Lead form custom audience, website custom audience, and customer list custom audience.

2. Overuse of exclusions.

While you should use exclusions, you can overdo it. It’s not uncommon for advertisers to exclude all current customers when promoting a product that current customers can buy. They consider it a “true prospecting” campaign and only go after completely new customers.

This is missing a golden opportunity. Your satisfied customers are the ones who are most likely to buy again and again. Excluding them eliminates the least challenging sale.

The response from those who take this approach is that they have a separate ad set for remarketing. But if you have one ad set for broad remarketing and one for prospecting, you’re going to drastically increase the costs to reach your remarketing audience. Remarketing audiences are small audiences, which translates to higher frequencies and CPMs.

It’s inefficient and unnecessary now. Don’t exclude current customers unless those customers can’t buy the product you’re promoting.

Expansion Ignorance

Many advertisers hate hearing this, but your targeting inputs mean less than ever before. This isn’t to say you should remove all targeting inputs and go completely broad (though it’s something to try). But even when you do provide inputs, the targeting is often going broader.

Either advertisers don’t know or they’re willfully ignorant about the role of audience expansion. And it doesn’t necessarily matter whether you’re using the original audience options or Advantage+ Audience.

1. Original audience options.

You’re resistant to the newfangled targeting of Advantage+ Audience and insist on kicking it old school. So you opt-out of it by making a couple of extra clicks to use the original audience options. You even click past the “Are you sure?” message about how it could lead to higher costs.

You enter in a bunch of interests and behaviors. This is how to go after your ideal customer, you think. These detailed targeting options define the exact person you want to reach. You’re doing something super smart.

But, you’re also optimizing for conversions, link clicks, or landing page views. And when you do that, your audience is automatically expanded using Advantage Detailed Targeting.

2. Advantage+ Audience.

It’s possible you may not even know there is a “new way” and “old way.” You spend significant time and budget trying to find the ideal targeting using custom audiences, lookalike audiences, and detailed targeting.

But, guess what? This targeting is only a suggestion.

It doesn’t matter whether you’re optimizing for conversions, link clicks, or landing page views. It doesn’t matter whether you’re using custom audiences, lookalike audiences, or if you click a box to expand the audience.

Your inputs are only suggestions and people well beyond this group will see your ads.

Your inability to understand and appreciate that targeting is expanding beyond your inputs may not be a mistake in and of itself. But this will lead to mistakes as well as wasted budget and time.

Too Narrow When Optimizing for Conversions

Many advertisers treat targeting like it’s 2017. There’s no better example than when optimizing for any type of conversion.

There was a time when a critical step was picking the targeting. Which interests, behaviors, lookalike audiences, or custom audiences made a huge difference to your results?

But when you’re optimizing for conversions now, your targeting inputs aren’t nearly as important as they once were. This is seen in the examples above when running manual Sales campaigns using either Advantage+ Audience or the original audience options, as described in the previous section.

But Advantage+ Shopping Campaigns go even further. You don’t provide any targeting inputs in this case. Meta does it all automatically.

There are two important factors that contribute to how your audience is chosen…

1. Performance goal optimization.

In the case of Advantage+ Shopping Campaigns, it’s typically a purchase (though it can be any standard or custom event now). The algorithm’s primary focus for delivery is to get you as many of that goal action as possible within your budget.

Success is determined by the ability to satisfy that goal. Adjustments will be made to delivery to get you more of those actions where possible.

2. Prior activity.

Whether you’re using Advantage+ Audience without targeting inputs or creating an Advantage+ Shopping Campaign, the initial focus of targeting is determined for you based on your pixel activity, conversion history, and prior engagement with your ads.

The mistake is that advertisers go far too narrow when optimizing for conversions when it simply isn’t necessary. Your biggest obstacle to success is limited budget and audience size. The more you limit the audience, the more you will restrict the algorithm and drive up your costs.

It’s also not as easy to go narrow as it once was, for the reasons already described. In some cases, your attempts are futile and the audience will expand anyway.

But, if you provide broad custom audiences while using the original audiences and turn off Advantage Custom Audience, you are likely doing more harm than good. You may get some short-term results. But you’ll exhaust that audience and run into a ceiling quickly.

It’s an obstacle that’s easily avoidable since Meta will automatically go after relevant people based on pixel activity, conversion data, and ad activity anyway — if you simply allow it.

Overdoing Demographic Granularity

Once again, there was a time and place for this. Especially when optimizing for any type of conversion, those times have passed.

We all did this years ago, and it was smart advertising at the time. We constructed the profile of our ideal customer. Their likes and dislikes, age range and gender, even their incomes and zip codes.

You may have even run breakdowns to find the groups of people by age and gender that lead to the best results so that you can then focus only on them.

You think that’s necessary now, but it just isn’t.

I’ve written before about how this may be necessary when optimizing for top of the funnel actions. But if we’re to be honest, nearly all top of funnel advertising is flawed anyway.

It’s one thing if you are unable to sell to customers under a certain age. Or your product is for women. There’s no reason to get cute messing with these settings simply because you believe that your product appeals more to men between the ages of 35-44.

If your goal is to drive more purchases, don’t try to outsmart the algorithm. It will learn. By restricting options, you are likely driving up costs unnecessarily — and limiting your pool of potential customers.

Too Broad When Optimizing for Top of Funnel

I alluded to it above. Avoid restricting your audience unnecessarily when optimizing for any type of conversion (especially a purchase). But that changes when optimizing for top of funnel actions.

Why? Because ad set optimization is literal. The algorithm’s only goal will be to get the action that you want. And while that is why you should limit restrictions when optimizing for conversions, it’s why top of funnel optimization can fail spectacularly.

Let’s say you’re optimizing for post engagement. You are a women’s clothing brand. You are hoping to attract potential customers by showcasing your new line.

Because you optimized for post engagement, the algorithm will only care about getting you engagement. It doesn’t matter (to Meta) whether that engagement is from a potential customer or not.

So, you’ll get plenty of engagement if you don’t limit by gender. Comments, video views, reactions, and image clicks. But you can bet that the vast majority of this engagement won’t be helpful.

You have to put guardrails on targeting when optimizing for these top of funnel actions because after all of these years, there are still very few ways to optimize for high quality actions that aren’t conversions.

I realize this sounds like a contradiction. I’ve given two very opposite sets of advice: 1. Stop restricting your targeting, and 2. Restrict your targeting. But the important context is whether you’re optimizing for the top or bottom of the funnel.

On one hand, I’d tell you it may be best to avoid running campaigns for engagement or clicks at all. But if you do, you’d better use some strict targeting — and make sure that the audience can’t be expanded.

Unnecessary Extra Ad Sets for Cold Targeting

This is connected to expansion ignorance. It’s also related to advertisers’ refusal to evolve with how things work now.

I avoid making absolute statements like “you should never create multiple ad sets for cold targeting now,” so I won’t do that here. If you’ve found success doing it that way and you aren’t getting results by combining those ad sets, do what works for you.

But consider me skeptical.

The idea that this would be more efficient than combining the cold targeting ad sets into one is illogical. It goes against how it works now and opposes Meta’s recommendations.

In the past, it made sense to split out ad sets for different cold targeting segments. Not only were there more interests and behaviors to choose from, but you could have very distinct groups of people. The overlap could have been controlled.

But that’s not the case now. If you’re using Advantage+ Audience (and that’s what Meta recommends), your inputs are only suggestions. If you’re using the original audience options with interests and lookalike audiences, those audiences are expanded.

There may be initial differences in performance between your ad sets in the beginning. Some of it will be based on your inputs and some will be due to randomness. But if they’re all optimized the same way and run the same ads, the differences will eventually be minimal. The algorithm will expand to show to people most likely to convert.

That overlap is not beneficial. You’re undoubtedly getting flooded with recommendations about auction overlap and audience fragmentation with suggestions to combine your ad sets. You’re willfully ignoring those warnings.

In most cases, you should use one ad set per campaign for cold targeting. If you need further evidence that this is where things are headed, look to Advantage+ Shopping Campaigns. You can’t even create a second ad set in that case.

And an educated guess would be that the future of campaign creation will look like Advantage+ Shopping Campaigns. You can fight it with your multiple ad sets all you want for now, but it very well won’t be possible at some point.

Watch Video

I recorded a video about this, too. Watch it below…

Your Turn

What common targeting mistakes do you see?

Let me know in the comments below!

The post 6 Targeting Mistakes Advertisers Make appeared first on Jon Loomer Digital.

We’re entering a new era of ads optimization driven by broad targeting and automation. While this has long-term potential for sales campaigns, there are obvious weaknesses outside of that focus. How does Meta improve optimization to work in all situations?

We also want a solution that is in line with Meta’s current philosophy and campaign creation flow. We can’t add a bunch of manual inputs to improve results when Meta’s entire focus is on streamlining the process. We want to find a realistic solution.

Let’s first review the problem and an example of ads optimization gone wrong. Then I’ll provide a recommended solution…

The Problem

The flaws and weaknesses of Meta ads optimization can be traced to one simple point: The goals of optimization are literal.

What I mean is that Meta’s systems have tunnel vision. When you set your performance goal and (if necessary) conversion event, the algorithm’s primary focus is getting you that thing. This is how you’ve defined success for yourself, so Meta will deliver your ads and make adjustments to satisfy it.

This works great at the very bottom of the funnel. Not only can you optimize for number of purchase events, but you can optimize for the value of those purchases. Meta’s focus will be on satisfying that, which puts the algorithm and Meta on the same page.

This can also work for conversion leads optimization. Meta’s focused on generating quality leads who end up buying from you. That’s what you want, too.

It all goes downhill from there.

If you optimize for link clicks, landing page views, ThruPlays, post engagement, Page likes, or even leads, the advertiser and Meta’s ad optimization will have divergent goals.

Yes, the advertiser wants that initial action. But they want that initial action because they have another ultimate goal. And they’re usually optimizing for that initial action because they don’t have the budget to optimize for that ultimate goal.

But Meta’s optimization doesn’t see it that way. It’s only trying to get you as many of that one action as possible within your budget. There is no concern for quality or what these people will do later.

That’s a problem for ads optimization generally. But it becomes a bigger problem in this world of broad targeting.

Example of Optimization Gone Wrong

This blog post was inspired by a complaint I’ve been hearing a lot lately related to Advantage+ Audience. The situation that keeps coming up is advertisers promoting a brand or product that’s catered to women.

They create a campaign optimized for Post Engagement.

Within Advantage+ Audience, the advertiser defines their target audience by detailed targeting and gender.

Of course, these are only targeting suggestions since we’re using Advantage+ Audience. The algorithm can go beyond these suggestions.

So, just select a gender constraint in Audience Controls, right?

Nope, gender isn’t an option. If Meta believes that more engagement can be found by reaching men, it will show your ads to men.

This is intentional. Meta’s documentation on Advantage+ Audience only mentions the ability to exclude ages or locations.

You can imagine how this could lead to disastrous results for a brand focused on women. The inability to exclude men should be fine when optimizing for purchases. If men don’t purchase, the algorithm learns and doesn’t show to men. But you can imagine that men will engage with ads featuring women. And unfortunately, it will be some of the creepiest engagement.

Meta’s ads optimization doesn’t care whether it’s creepy engagement. It only cares that there’s engagement. And that means that this ad will be shown to more men.

The Solution: Ranking Actions

We need Meta’s optimization goals to be in line with our long-term advertising goals. How do we fix this?

The easy solution to the problem above may be to allow the exclusion of men in Audience Controls. But that’s only a Band-Aid that doesn’t solve the underlying problem.

Meta’s optimization needs to have a foundational understanding of what we want. Yes, we want engagement. But we want engagement from relevant people who could potentially become paying customers.

There needs to be a reworking of the algorithm. To do that, we can rank what’s most important to us.

For example, our performance goal may be Post Engagement, but our ultimate goal is a purchase. And if not a purchase, a lead. So, Meta’s optimization will prioritize engagement that eventually results in a purchase or lead.

How do we do that? Well, having the advertiser rank their priority events in the ad set would never fly. This conflicts with Meta’s desire to simplify campaign creation.

Maybe this could be an addition to Ad Account Settings. We’ve already seen this for account-wide exclusions related to Advantage+ Shopping and manual sales campaigns (Customer Acquisition). It wouldn’t be crazy to allow advertisers to prioritize events there.

But, this is also something that is mostly universal. Meta’s ads algorithm should be smart enough to prioritize this for us. This ranking should apply for most advertisers:

1. High-Value Purchase
2. Any Purchase
3. Conversion Lead
4. Any Lead or Registration
5. Deep website engagement (time spent, return visits, events fired)
6. Deep page engagement (long-time follower, quality DMs that aren’t reported or ignored)
7. Deep post engagement (watch videos to completion, share posts, prioritized reactions like Love, quality comments that aren’t marked spam)
8. All other light-touch engagement (clicks, reactions, views)

To clarify, this will act as a foundational ranking for helping the algorithm learn. Even if your performance goal is Post Engagement, it will focus on satisfying that goal. But the optimization will prioritize engagement that comes from people who have or will eventually perform these other actions (greater weight added based on order).

Not all post engagement is created equal. That’s obvious. We just need Meta’s ads optimization to understand that, too.

The Only Other Direction

This is something that must be fixed. Yes, optimizing for top-of-the-funnel has always been a questionable strategy, but it becomes completely worthless when the push is to go broad. Unless Meta’s optimization gets smarter about what quality engagement looks like, there’s only one other option.

If Meta can’t fix it, then eliminate it. Advertisers are burning money, and many don’t realize they’re doing it. The only other direction that makes sense is to only allow for conversion optimization. Because this is the only time when the new brand of Meta advertising makes sense.

Your Turn

Maybe this is a pipe dream, but these are critical issues that can be addressed and it would drastically improve advertising results. What do you think?

Let me know in the comments below!

The post How Meta Could Improve Ads Optimization appeared first on Jon Loomer Digital.

We’re moving into a new era of Meta advertising. Meta wants us to go broader with our targeting, and in some cases there is evidence that it’s effective. But there are times when broad targeting is bound to fail.

You may even get results that appear to be positive. But that’s part of the issue here. The performance fails once you scratch below the surface.

In this post, I want to highlight some specific situations when this is bound to happen. At the end, I’ll lay out a way that I’m trying to counter this. But ultimately, it would be helpful if Meta would come up with a way that wouldn’t require creative solutions.

What is Broad Targeting?

There’s often confusion regarding what I mean when broad targeting is discussed, so let’s define it.

When “broad targeting” is mentioned, I mean one of three things:

1. All targeting inputs are removed.

This could be because you’re using the original audience options and don’t provide anything for custom audiences or detailed targeting (only using geographic and demographic targeting). It could also be because you’re running an Advantage+ Shopping Campaign.

2. Targeting expansion is on.

You’re using the original audience options and provide custom audience, lookalike audience, or detailed targeting inputs, and you either manually turn on one of the Advantage audience expansion tools (Advantage Detailed Targeting, Advantage Lookalike, Advantage Custom Audience) or they are automatically turned on due to objective. As a result, your audience will be expanded to reach people beyond your inputs if it will lead to more or better results.

3. You’re using Advantage+ Audience.

This is now the default option when setting targeting in manual campaigns, though you can currently switch back to the original audience options. When using Advantage+ Audience, your targeting inputs (which are optional) are used only as targeting suggestions. The algorithm can then go much broader.

Optimization is Literal

This is going to come up repeatedly, so let’s address it now. It’s important that you understand that Meta ads optimization is literal. This is both a benefit and a weakness. Here’s what I mean…

Ad delivery is driven by your performance goal. The algorithm’s focus will be on getting you as many of the actions you want within your budget.

Meta will optimize and make adjustments (including who sees your ad, which will be important) based on satisfying that goal. Whether you want conversions, link clicks, impressions, or something else, Meta’s focus will be on helping you get as many of that thing as possible because that is how you’ve defined success.

That’s a benefit if all you really want is the thing that you’re optimizing for. It’s a weakness if you expect people who perform your optimization event to also perform other actions. You expect, for example, people who click a link to land on your website then behave like a normal human who may do other things.

But the algorithm doesn’t care about those other actions. It only cares about satisfying that initial goal.

When Broad Targeting Works

Broad targeting is most effective when both of the following are true:

1. Your performance goal is Conversions (number or value) and the conversion event is Purchases.

2. Your budget is high enough to generate the volume required for Meta to learn and properly optimize.

The reason this is the ideal situation is that, first, optimization is literal. Meta’s only focus is getting you purchases. When your audience is expanded, the algorithm won’t go after people likely to result in accidental or low-quality purchases. That’s just not a wide-spread and predictable issue.

Going broad is beneficial here because your results are often limited by your audience size. You add those limitations and they can restrict the algorithm from finding you more results. Additionally, that tighter audience can drive up frequency, creative fatigue, and ad costs.

A high enough budget to generate volume is helpful to get great results, but it’s not required. Let’s not get bogged down in what “high enough” means. The bottom line is that the algorithm can better learn when there’s volume of data to learn from.

Optimizing for purchases is when broad targeting is at its best. A notch below would be any other type of conversion. It’s not on par with purchases because the quality of that conversion can be an issue when the algorithm expands your audience and goes for anyone within the targeting pool.

That is also because optimization is literal. You want leads? The algorithm will get you leads. Unless you optimize for conversion leads, the algorithm does not care what those people do after subscribing.

This could potentially be controlled with tighter targeting. But once you allow Meta to target anyone beyond your target group, the characteristics of your ideal audience mean close to nothing.

When Broad Targeting Fails

Broad targeting can be an unmitigated disaster when optimizing for any top-of-the-funnel action. The problem is that, more than likely, you’ll be able to generate what appear to be great results. Meta will think they’re great results, too. But they’re likely low quality.

The reason, again, is that optimization is literal.

This is why most experienced advertisers would tell you it’s almost always a waste of money to optimize for link clicks or landing page views. The algorithm will do everything it can to get you those clicks at the lowest cost. And they could come from people who click everything (for no known reason), accidental clicks, and even bots or click fraud before they are detected.

Low-quality results are already an issue with top-of-the-funnel optimization prior to broad targeting. But if you can at least define your audience succinctly, you may be able to place some guardrails on the algorithm. You’d likely still run into quality concerns, but that will definitely be an issue if Meta can remove those guardrails and target whomever they want.

If you say you want link clicks and can’t place limited restrictions on whom you want to reach, Meta will find link clicks. You may luck out and get a few quality clicks, but most are likely to be a waste of money.

It seems as though Meta knows this because the only time detailed targeting and lookalike audiences are automatically expanded using the original audiences is when optimizing for conversions. You can turn this off when running top-of-the-funnel optimization.

Interesting, isn’t it?

An Approach to Solving Broad Targeting Issues

Let’s try to find a creative solution to this problem of quality when optimizing for top-of-the-funnel actions while going broad. Because if we did, the algorithm could actually benefit us by helping to find lower cost (and hopefully higher quality) actions.

Let’s use the example of link clicks and landing page views to drive traffic. We need an alternative when we have content that we want people to consume — and not another immediate bottom-of-the-funnel action would be expected.

This is my life, actually. My blog is important, and I do still want to drive traffic to it. We’ll want to optimize for some type of conversion, but a purchase or even lead won’t happen at a high enough rate as a direct result of reading my blog post to make that optimization realistic — without needing to spend quite a bit to get it.

You may already know that I have a large slate of custom events that fire on my website that represent some of the quality actions that I want.

Here are examples:

• Timer Events that fire at 15 seconds, 1 minute, 2 minutes, and 3 minutes
• Scroll Depth event that fires at 50% scroll
• Item in View event fires when someone views the comments
• Video Viewed event fires when someone plays an embedded YouTube video
• Click event fires when someone clicks my bot or a share button
• Internal Link Click event fires when someone clicks any link that takes them to another page of my website

These events are helpful for both optimization and reporting. To optimize for one of these events, we’ll need to create an Engagement campaign that uses the Website conversion location and Conversions performance goal.

But, which event should be the optimization event? This may not sound like an important question, but I’ve found some of the same issues with my custom events when it comes to the algorithm being literal. If I optimize for a timer event, I’ll end up with people spending lots of time on the page, but they never do anything else. If I optimize for scroll, they’ll scroll, but immediately abandon.

We also need to consider costs and volume. If it ends up costing $10 for one of these events as the central conversion event, I’ll need to spend $500 per week just to exit the learning phase for what is essentially a traffic campaign.

Here’s an example of what I’m experimenting with now, but I may still make adjustments…

I’m running ads that send people to one of my short-form video custom post types. Because of that, the YouTube video is embedded at the top of the post and there’s a short blog post below it.

For now, I’ve chosen to set the central conversion event as VideoWatched, which is the event that fires when someone starts the embedded YouTube video.

When optimizing for such an event that can happen over and over, a critical element is the Attribution Setting. Make sure it’s 1-day click only, otherwise the results will be inflated.

I’m using Advantage+ Audience, but with targeting suggestions of people who have fired the VideoWatched event during the past 180 days or those who are in the top 25% of time spent on my website. I’m hoping this initial group will provide the actions I want to give the algorithm something to learn from prior to going broader.

I’m also excluding anyone who viewed one of seven different posts for at least 15 seconds I’m going to promote in this campaign. This will also prevent unnecessary frequency.

I’ve created multiple ads for different video posts, hoping to give the algorithm something that will work. Since I already post Reels, people are accustomed to seeing these in the feed. So I use link ads that use the featured image of these videos, making it clear that they will be watching.

So far, the results have been solid. A reasonably high percentage of the people who click are spending at least 15 seconds (or a minute), scrolling, clicking internal links, and watching the embedded video. This isn’t shocking because I’ve experimented with this approach before. The caveat here is that we’re trying to make it work with broad targeting.

It’s early, but the Video Watched event volume is still the lowest of those custom events, so I may make an eventual adjustment and optimize for something else, like internal link clicks.

What Meta Could Do Instead

Coming up with creative solutions is fun, but it’s aggravating that this is necessary. Top-of-the-funnel optimization is already problematic, but if Meta’s going to encourage or virtually force broad targeting via Advantage+ Audience, it’s practically burning money.

Meta could fix this by solving the quality issue. The custom event approach may not be as useful as it could be because the learning is isolated to my website. Why can’t Meta create standard events that represent some of this quality engagement so that there is more data?

Meta could add a Quality element to Traffic campaigns (or other top-of-funnel objectives). Do you want the algorithm to focus on the most link clicks or landing page views? Or do you want the highest quality traffic (people who are more likely to spend more time, click around, and return later), knowing that it will cost more?

This has long been a complaint, but it becomes a bigger problem if you can’t put guardrails on targeting. Whether it’s traffic or some other type of engagement, the algorithm — given a huge pool to go after — will find weaknesses to get you the most results possible. And those results will often be low quality.

Watch Video

I also recorded a video to walk through this. Watch it below…

Your Turn

How do you approach broad targeting?

Let me know in the comments below!

The post When Broad Targeting Fails appeared first on Jon Loomer Digital.

Meta announced that more detailed targeting options will be removed on January 15th. It’s the continuation of a trend that began more than three years ago.

Don’t say that I didn’t warn you.

This is a reminder that you cannot rely heavily on granular targeting. Not only might it go away, but your targeting inputs matter less and less with each new update.

I didn’t write this post to scare you. Instead, I’m hoping to break through to help you understand what’s happening and is bound to happen. It’s time to embrace this direction and prepare for it.

Targeting Trends

It’s debatable when the first shoe dropped related to the current direction of targeting. From about 2012-15, the focus was on developing new powerfully specific ways to target people. New detailed targeting to reach people by actions and behaviors. New custom audiences to reach those who have engaged with you. All focused on your inputs.

Maybe things didn’t officially start shifting during the election season of 2015, but that’s certainly when the seeds of change were planted. The use of targeting to manipulate elections attracted scrutiny. Meta (then Facebook) found itself under significant regulatory pressure.

Cambridge Analytica surely contributed to greater awareness of ways people were being tracked and how it was used for targeted advertising. Meta developed new rules for special ad categories that were aimed at preventing discrimination in the areas of employment, credit, and real estate.

Then iOS 14 happened a few years later, and doubts emerged around how accurate and complete some of our ad targeting was. Meta took an interest in machine learning, AI, and modeling to help repair lost data and fill in the gaps.

Meta performed a sweep to remove certain ad targeting options in the early parts of 2022. Those removals focused on “detailed targeting options that relate to topics people may perceive as sensitive, such as options referencing causes, organizations, or public figures that relate to health, race or ethnicity, political affiliation, religion, or sexual orientation.”

The removal of those targeting options feels like deja vu now.

During the past couple of years, Meta launched several products that helped clarify the path that we’re on…

1. Advantage Targeting Expansion Tools. Meta launched Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience. When used, Meta can expand your audience beyond your targeting inputs if more or better results can be found. In some cases, it’s an option that you can turn off. In others, expansion is on by default.

2. Advantage+ Shopping Campaigns. Meta launched its advancements in machine learning and AI to help e-commerce advertisers. When running Advantage+ Shopping Campaigns, no advertising inputs are provided at all beyond a cap on reaching current customers. Instead, Meta relies on signals and historical data.

3. Advantage+ Audience. Meta applied what it was doing with Advantage+ Shopping to create a version for any objective. Advantage+ Audience allows you the option of providing targeting suggestions, with the expectation that delivery can go much broader. If you don’t provide suggestions, the algorithm will focus on pixel data and conversion history as a starting point.

While you can switch back to the old targeting method, Advantage+ Audience is now the default approach.

We could even include Chrome’s phaseout of third-party cookies in 2024 as another critical change that is likely to impact how targeting happens.

Targeting Today

Given all of these trends, the picture of targeting today looks like this…

1. You have fewer detailed targeting options than ever before.

2. Your targeting inputs aren’t always required.

3. When provided, your targeting inputs mean less than ever before because the algorithm can and will go broader.

These are facts and are not debatable. Whether or not you insist on using granular targeting options and you believe they are more effective does not matter. In some cases, the impact of those granular inputs are probably far less than you think because the audience is expanded. In others, you may actually hurt your results by refusing to evolve with the trends.

That isn’t to say that broad targeting works 100% of the time for all advertisers and you should never use granular targeting. Instead, know that the way you are targeting opposes the way Meta wants. That will eventually catch up to you.

What’s to Come

Unless something shocking happens, we’re heading in a rather obvious direction.

1. It’s quite possible that Meta will repurpose Advantage+ Shopping Campaigns for other objectives (this is already in the works for leads). This means the possible removal of all targeting inputs. Before you consider it a bad idea, this also assumes that Meta’s delivery algorithm improves to make it possible.

2. If not #1, Advantage+ Audience becomes the default requirement. No more switching to the old targeting methods. Any targeting inputs you provide, regardless of the objective, will be suggestions only.

3. Detailed targeting options continue to dwindle, eventually settling on a broader category of behavior. Again, this assumes that we’ll be able to use detailed targeting at all soon. But if we do, this approach of broader categories makes the most sense since it gives Meta more control over these smaller interests that end up falling into sensitive areas.

What You Should Do

It’s time to be blunt. There’s no stepping around this. You can’t keep targeting the ways that you always have. And frankly, you may be hurting your results if you are.

The first thing you should do is an audit of your current advertising approach. Maybe it worked five years ago. Does it actually make sense now?

And by “making sense,” I don’t just mean that you’re getting good results. It’s possible you’re still getting decent results in spite of yourself. Are you creating five ad sets per campaign for different cold targeting options? Due to targeting expansion, there’s probably way more overlap in that targeting today than there would have been in the past.

Start embracing some of the targeting methods that will continue to be available in the coming months and years. Experiment with Advantage+ Shopping Campaigns if you’re promoting an e-commerce brand. Start using Advantage+ Audience, both with and without targeting suggestions. Does it matter which one you use? Does that approach impact longevity?

Let me be clear that I did not immediately embrace broad targeting. I resisted the initial Advantage Targeting Expansion options when they were released. Over time, I began to see that they did help results (with some exceptions). I now fully embrace Advantage+ Audience with targeting suggestions.

I’m not necessarily saying to completely abandon something if it works, especially if you’re positive that the alternative does not (though I’d question that). Instead, prepare yourself the best you can for what is likely inevitable. Test broader targeting. Don’t just test it because you hope to prove that it fails. Try to find ways that will make it work for you.

Because one day, and that day is likely not far into the future, you likely won’t have a choice.

Your Turn

Have you embraced broader targeting?

Let me know in the comments below!

The post Meta’s Removal of Detailed Targeting is a Reminder of What’s to Come appeared first on Jon Loomer Digital.

One common point of confusion for Meta advertisers is Meta’s expansion of ad targeting. In some cases, advertisers don’t realize it’s possible at all (but is). In others, it’s completely unclear how much an audience was expanded.

To make things worse, Meta has added new targeting features related to audience expansion that sound similar but are different in subtle ways.

The concept of targeting expansion began with Advantage Detailed Targeting (originally Detailed Targeting Expansion). That same approach was applied to Lookalike Audiences (Advantage Lookalike) and even custom audiences (Advantage Custom Audience).

Those options weren’t good enough. Meta then launched Advantage+ Audience, which essentially combines the three other options to allow advertisers to provide targeting suggestions prior to going broader.

If you’re confused just by reading this intro, it’s understandable. There’s way too much going on in the space of audience expansion. It’s become too complicated, and advertisers are mostly left in the dark regarding when and how expansion happens.

My goal is to shed some light on this. Let’s break down how Meta defines the use of targeting expansion in these cases. I’ll then share my request for how Meta could clear up the confusion with necessary transparency.

By the end, I’ll detail a test that I am starting that could help provide some necessary clarity.

Let’s go…

Advantage Targeting Expansion

As mentioned at the top, there are three types of Advantage targeting expansion. Let’s define each one…

1. Advantage Detailed Targeting. This only applies if you’ve entered audiences within Detailed Targeting (interests and behaviors). If Meta’s systems find improved performance opportunities outside of your targeted audience, the audience can be dynamically expanded to take advantage of those opportunities.

A handful of objectives and performance goals allow you the option of turning this on or off…

But, in most cases, Advantage Detailed Targeting is on by default and can’t be turned off. Here’s an example when the performance goal is to maximize the number of conversions…

2. Advantage Lookalike. This functions similarly to Advantage Detailed Targeting, but it’s specific to the expansion of Lookalike Audiences. When you create a lookalike audience, you select a percentage (from 1-10%) to isolate those people most similar to your source audience.

Let’s assume your lookalike audience is based on the top 1% of those most similar to your source. Advantage Lookalike allows Meta to expand beyond your chosen percentage if better results can be found by doing so.

As is the case with Advantage Detailed Targeting, you are given the option of turning Advantage Lookalike on or off when using certain performance goals.

In other cases, it’s on by default and can’t be turned off.

3. Advantage Custom Audience. This works just like Advantage Detailed Targeting in that Meta can expand beyond your selected custom audience if better results can be found by doing so. The difference here is that you will always have the option of turning this expansion off.

Advantage+ Audience

And finally, Meta rolled out Advantage+ Audience as an improvement to the prior three options. This is Meta’s recommended approach to targeting now. If you use Advantage+ Audience without any customizations, Meta will utilize AI and machine learning to find your audience based on pixel data, conversion history, and prior engagement with your ads.

As you can see in the image above, you have the option of providing an audience suggestion. If selected, you could enter information like detailed targeting, lookalike audiences, or custom audiences. When you do this, Meta will prioritize those suggestions before going more broadly.

If you’re confused by how this is any different than the other three options, Meta explains it here:

Advantage+ audience creates the broadest possible audience to search within, giving Meta’s AI lots of flexibility.

In comparison, Meta’s original audience options, including Advantage options (Advantage detailed targeting, Advantage custom audience and Advantage lookalike), can limit the potential of Meta’s AI which can be less effective.

In other words, the first three Advantage options can expand your audience, but Advantage+ Audience has the ability to expand your audience even more. And that, according to Meta, allows it to get you better results.

Request for Transparency

So, here’s the problem…

When we create an ad set that uses any of these options, we will never know any of the following:

1. Whether the audience was actually expanded
2. How much the audience was expanded
3. The results associated with the expanded audience (outside of your targeting inputs)

We know that the audience can be expanded. We know that it’s likely to be expanded. But, theoretically, it won’t necessarily be expanded at all.

If you take the definitions of these features literally, Meta will only go after people outside of your targeting inputs if it will lead to improved performance. It’s reasonable to assume there are cases when that expansion isn’t required.

Especially if your budget is small or your beginning targeting audience is large. Or maybe you’re getting great results out of the audience you’re using and Meta can’t do better than that.

We simply don’t know. It’s a guess.

There’s a simple (in my non-technical opinion) solution, if Meta wants to fix this. Create a breakdown option for Audience Expansion so that separate rows are created for your Ads Manager results:

1. Targeted (or suggested) audience
2. Expanded audience

This will give us a transparent look at how much our audience was actually expanded — and whether that expansion was truly beneficial.

Of course, that’s not coming any time soon, if ever. It’s a request.

A Test

In the meantime, there are two simple questions I’d like answered:

1. How much are these audiences expanded?
2. How much more is the audience expanded when using Advantage+ Audience?

There won’t be a perfect way to measure this because limitless factors are likely to impact whether the audience expands and how much. But I wanted to run a test that would force Meta to expand the audience and then see if there’s a difference between the approaches.

There are two primary ways you can get Meta to expand an audience when expansion is on:

1. Spend a ton of money
2. Target a tiny audience

I don’t feel like burning money, so I’m going with the second.

I created a campaign with three ad sets targeting a custom audience that should include a few hundred people. The ad sets, as you probably guessed, are different based on the use of expansion.

1. Custom audience only (no expansion)
2. Custom audience plus Advantage Custom Audience
3. Advantage+ Audience plus custom audience as an audience suggestion

I then created an A/B test in Experiments so that there wouldn’t be any overlap in the targeting.

Since our focus is on how much the audience is expanded, I’m going to use Reach as the performance goal. This also allows me to set a frequency cap of 1 impression in 7 days to further force expansion to happen.

Since the performance goal is Reach, we can assume that Meta will gladly expand the audience to simply reach more people. But will it expand more when using Advantage+ Audience than Advantage Custom Audience?

It’s very possible, if not likely, that the amount of expansion is also influenced by the performance goal. Would it matter if I instead optimized for leads? Conversions? Link clicks? Something else?

Let’s focus on one thing at a time. I started this test today, and I’ll report back when I have something of substance to report.

My guess is that the ad set targeting the custom audience without expansion will burn out quickly and may stop delivering entirely. I’ll be curious to see if there’s any difference at all in expansion between the other two.

Depending on these results, I’ll want to run future tests related to different performance goals as well as get a clearer sense of performance between the three approaches.

Your Turn

What’s your experience been with audience expansion?

Let me know in the comments below!

The post How Much Does Meta Expand Your Audience? appeared first on Jon Loomer Digital.

In a previous post, I discussed how you should approach Meta ads targeting now. Things have changed quite a bit, and it’s important that you evolve with those changes. But, what does the future of Meta ads targeting look like?

I don’t have a crystal ball. These are all predictions. But, if you’ve been paying attention during the past few years, you’ll likely agree that these predictions are reasonable, if not likely.

Some of you will read this and feel comfortable, knowing that these changes are unlikely to impact you since you’ve adjusted well to the evolution of Meta advertising so far. But I also know that this will make some of you very uncomfortable.

When is the “future,” exactly? I could see some, if not all, of these changes enacted during the coming year. It wouldn’t shock me if some happened suddenly in the very near future.

I have no inside information. It’s always possible I’m wrong. But, here’s what I expect will happen…

Where We’ve Been Heading

We’ve been trending in a natural direction for a few years now…

1. Thousands of interests removed.

2. Tracking challenges related to iOS 14 and privacy changes impact remarketing.

3. Meta begins expanding targeting beyond the audiences we’ve selected — first as an option and then by default (in most cases).

4. Advantage+ Shopping Campaigns roll out, which eliminate targeting inputs.

5. Advantage+ Audience targeting rolls out, which allows optional targeting “suggestions.” Otherwise, Meta will find your audience based on pixel activity, conversion history, and prior engagement with your ads.

Maybe you’ve resisted it. But there is a very clear, natural progression happening here.

Advantage Audience Expansion Will Be Eliminated

Once Meta started rolling out Advantage+ Audience, predictable confusion resulted. There are now four different features that sound like nearly the same thing.

1. Advantage Detailed Targeting: If Meta’s systems believe that better performance is available beyond the detailed targeting inputs you’ve provided, your audience can be dynamically expanded.

2. Advantage Lookalike: If Meta’s systems believe that better performance is available beyond the lookalike percentage that you’ve selected, your lookalike audience can be dynamically expanded.

3. Advantage Custom Audience: If Meta’s systems believe that better performance is available beyond the custom audiences you’ve provided, your audience can be dynamically expanded.

4. Advantage+ Audience: Advertisers have the option of providing targeting suggestions using detailed targeting, lookalike audiences, and custom audiences. Meta will prioritize matching those suggestions prior to moving more broadly.

The differences are subtle. In each case, you provide initial targeting inputs (though with Advantage+ Audience, they are merely suggestions). Meta can expand beyond that audience to get you better results — though, Advantage+ Audience seems to suggest that expansion definitely will happen.

Advantage+ Audience also has the potential to go much broader. And if you don’t provide targeting suggestions, Meta will use your past conversions, pixel data, and engagement with prior ads to build and evolve your audience.

The typical advertiser will not understand the subtle differences. They also won’t understand that Meta released Advantage+ Audience as the enhancement that is intended to be more effective than the prior three options.

There truly is no reason for Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience to continue to exist. You can accomplish nearly the same goals (with improved results, according to Meta) by simply using detailed targeting, lookalike audiences, or custom audiences as suggestions — if you use anything at all.

Meta should, and likely will, eliminate those three options. It’s the natural progression, and I’d be surprised if they survived much longer.

Advantage+ Audience Will Become Fixed Default

We’ve seen this progression with other Ads Manager features in the past. Meta makes or plans to make a setting a fixed default. There are protests. Sometimes (like with Advantage Campaign Budget), Meta backs off.

We’ve seen this with Advantage Detailed Targeting and Advantage Lookalike for specific optimizations. You no longer have the option to turn them off.

We’re starting to see signs of this related to Advantage+ Placements. Meta, at the very least, wants to discourage adjusting from the default.

You have limited ability to make any adjustments to Advantage+ Shopping Campaigns, including targeting. The entire purpose of Tailored Campaign setup is that it’s streamlined and you can’t edit defaults.

Meta’s process with these decisions is rather straight forward. They analyze results when advertisers use the default and when they make manual adjustments. If results are consistently superior by keeping the default, Meta will either lock it in or make it difficult to change.

At the moment, advertisers have the ability to bypass Advantage+ Audience and use old targeting methods. But it’s not entirely clear and obvious that this is possible. It’s an intentional design decision to discourage these changes.

Meta will surely monitor to compare results when advertisers use Advantage+ Audience vs. the original targeting options. They have some of these results already, which is why we’re seeing the current design.

It’s logical to conclude that, while there may be isolated exceptions based on objective or optimization, the original targeting options will be discontinued. You will still be able to use detailed targeting, lookalike audiences, and custom audiences as targeting inputs during this phase. But they will only be as suggestions.

I can see this happening first with detailed targeting and lookalike audiences. It’s possible that custom audiences without expansion will survive — or at least for now.

Most or All Manual Targeting Inputs Will Be Removed

Why not keep going?

Once again, this isn’t a particularly bold prediction. We’ve seen it already with Advantage+ Shopping Campaigns. You cannot provide any detailed targeting, lookalike audiences, or custom audiences for targeting — even as suggestions.

Advantage+ Shopping Campaigns, according to reports from Meta, have been more effective than prior Sales campaigns optimizing for purchases. If it can work for Sales, why not for other objectives and optimizations?

There will likely come a time when these targeting inputs won’t be possible for any campaign type. Meta will dynamically determine your targeting based on:

1. The performance goal
2. Past conversions
3. Pixel data
4. Engagement with prior ads
5. Global user engagement data

In a way, detailed targeting will still exist, but only Meta will use it. The data is all there for Meta to find, and your inputs won’t be needed.

I do think this could be problematic given the current Ads Manager structure. Eliminating targeting inputs makes sense for purchases. But Meta may need to provide additional layers of performance goals to provide clarity regarding what you actually want for this to work in other cases.

One could argue that removing targeting inputs could be a smart move for Meta related to privacy and perception, as well. If advertisers are unable to select specific interests and behaviors, the process of delivering ads may seem less “creepy” to non-advertisers.

Maybe Not Now

I can hear the complaints through my computer screen. “This will never work.” There are bound to be reservations about instituting such an approach with Meta’s current advertising feature set. And many of those reservations are valid.

But Meta’s machine learning and AI will only improve. No matter what you think of the effectiveness of Advantage+ Shopping Campaigns, Advantage+ Audience, or any of the audience expansion tools now, think about a year or two from now.

Think about the advancements we’ve seen in AI just this year. A future without targeting inputs shouldn’t seem far-fetched.

Your Turn

Hey, I could be wrong. But I feel strangely confident about these predictions. They don’t feel particularly bold. It’s the natural progression of where we’ve been and where we appear to be heading.

What do you think of these predictions for the future of Meta ads targeting?

Let me know in the comments below!

The post The Future of Meta Ads Targeting appeared first on Jon Loomer Digital.

Meta advertising is nothing like it once was. The role of the advertiser has changed. New tools and features have emerged. The strategies have evolved. And if you approach targeting now like it’s 2018, you’re going to struggle.

Unfortunately, that’s exactly what we see. Some advertisers have embraced this brave new world. Others are resistant to it and insist on forcing their old strategies like a square peg in a round hole.

It’s time to wake up. In this post, we’ll evaluate how targeting has changed and how you should approach it now.

Targeting Before

Back in the day, there were three distinct buckets of targeting.

1. Cold Targeting. We loved uncovering the magical combination of interests, behaviors, and lookalike audiences to bring the best results from a cold audience. We experimented with grouping interests in one ad set and lookalikes in another. Or we’d layer interests on top of lookalikes. Should you use a 1% lookalike or 5%? What about 10%? We tested and tested and found the answer.

Even location, age, and gender were important details. A part of the country isn’t leading to conversions? Exclude it. Mainly women between 25 and 34 are buying? We’ll only target them.

2. Warm Targeting. If you wanted to go after a group of people who knew who you were and were likely to convert, there were several places to start. Target your page followers, anyone who engaged with your page or posts, people on your email list, or anyone who visited your website.

This was a go-to targeting strategy.

3. Hot Targeting. These people are hot for a reason. They performed a very specific action. I created a whole strategy around it using Evergreen Campaigns. I’d push people through several stages of a campaign, showing them a different ad every few days. And it worked great!

There was a good reason to use all three approaches. It was generally seen as good practice to have multiple ad sets, if not multiple campaigns, dedicated to each audience segment.

The Evolution of Targeting

There were a couple of turning points. One was the Cambridge Analytica scandal. While it happened in and around 2015, it wasn’t revealed until 2018 and the impact to targeting would come after. One of the main lessons was to prevent bad actors from using sensitive targeting to manipulate elections.

Another turning point was iOS 14 and the movement towards greater online privacy generally. Facebook would face greater scrutiny regarding what was collected, how it was used, and giving users more control.

These combined forces led, directly or indirectly, to the removal of thousands of interests and the inability to target specific groups when a special ad category is involved. Opt-outs also cut into remarketing audiences, making them less complete and less dependable.

In the meantime, Facebook — and eventually Meta — would need to come up with solutions that would overcome these disadvantages. That led to a focus on AI, machine learning, and expanded audiences.

The move towards broad targeting began with Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience. You provided targeting, but the algorithm would be able to reach people beyond that group if it would lead to more results.

The next step was Advantage+ Shopping Campaigns, which virtually eliminated targeting inputs completely. Beyond having some say over how much you’d reach current customers, the algorithm had entire countries of users to target without restrictions.

Eventually, this same approach would begin rolling out to any campaign objective in the form of Advantage+ Audience. You can provide targeting suggestions, but otherwise the algorithm will use pixel data, conversion history, and ad engagement history to build a starting audience.

How to Approach Cold Targeting

An argument can certainly be made that there’s very little reason for interests and lookalike audiences now. But even if you use them, there’s no reason to use them the way we did before.

You don’t need to obsess over which interest is most effective because, in most cases, Advantage Detailed Targeting is automatically on and can expand your audience anyway.

There’s no reason to constantly test different lookalike audiences and percentages because Advantage Lookalike is often on by default, which will expand the percentage if necessary.

The evolution towards broad and expanded audiences changes our approach, whether you like it or not.

There’s simply no reason to spend much time on testing different audiences since the algorithm can go beyond the audience you use anyway. It’s a complete waste of time to have multiple ad sets for different cold targeting approaches when the overlap is likely to be significant and audience fragmentation may result.

What should you do?

Embrace broad targeting for cold audiences. If you’re optimizing for a purchase, test Advantage+ Shopping Campaigns.

Otherwise, use Advantage+ Audience. Add some targeting suggestions if you want. But the true power will be how the algorithm learns beyond that initial group.

I wouldn’t be surprised if we eventually see the elimination of Advantage Detailed Targeting and Advantage Lookalike in favor of Advantage+ Targeting only since the functionality is similar and confusing. But otherwise, you should embrace the expansion of your audiences when given the option.

Bottom line…

1. Create fewer ad sets for the purpose of cold audience segmentation.

2. Embrace expanded audiences when given the option for cold targeting.

3. Embrace machine learning and AI for the broadest of targeting.

Is Remarketing Dead?

This is a common refrain, and it’s at least partially valid.

Generally remarketing is mostly unnecessary. What I mean by that is that it probably isn’t necessary to target the “warm” audiences we defined at the top of this post. These are the types of groups that will be built into the initial focus of broad targeting.

You could make an argument to use some of these remarketing audiences in testing. For example, target all website visitors and turn on Advantage Custom Audience. Or provide a group of custom audiences as your targeting suggestions when using Advantage+ Audience. In both cases, though, it’s a matter of using this group as a starting point with the hope that it helps the algorithm.

We’ll figure out with time whether using custom audiences in these ways was beneficial or if the algorithm would have searched the most valuable people in those groups out anyway. But for now, it doesn’t hurt to experiment with this.

Something I haven’t bought into is abandoning remarketing completely. I still subscribe to abandoned cart remarketing for simple reasons: It works, it’s inexpensive, and it’s very profitable.

If you have a small budget, the broad targeting approach isn’t likely to yield many conversions. But you can spend a very limited amount by retargeting people who abandoned cart and get results.

Maybe I’ll change my stance on this eventually. For now, I still see remarketing to the hottest of audiences makes a ton of sense.

Your Turn

How has your targeting approach evolved?

Let me know in the comments below!

The post How to Approach Meta Ads Targeting Now appeared first on Jon Loomer Digital.

Meta first introduced Advantage+ Audience when it announced AI-powered ad tools in May of 2023. It will sound eerily similar to the other Advantage audience expansion products. There are some important differences.

Let’s take a detailed look at what Advantage+ Audience is, how it works, how to set it up, and my expectations for the future of Meta ads targeting.

What Is Advantage+ Audience?

You have access to Advantage+ Audience if you see this in the ad set…

Advantage+ Audience is a targeting setting that allows Meta to use AI to determine the audience that will see your ads. To determine this, Meta’s systems are constantly learning from your pixel data, conversions history, and people who have engaged with your previous ads and content.

You can choose to either trust Meta to find your target audience for you without any input or you can provide some inputs as audience suggestions. Your suggestions can include:

• Custom Audiences
• Lookalike Audiences
• Age Range
• Gender
• Detailed Targeting

Meta will prioritize reaching people who match these suggestions before expanding to people more broadly.

Meta provides some convincing stats regarding the effectiveness of Advantage+ Audience.

While I don’t care a whole lot about a 28% lower Cost Per Click or Landing Page View (that doesn’t mean a lower Cost Per Action), the 13% lower Cost Per Product Catalog Sale and 7% lower Cost Per Website Conversion are worth notice and potentially significant.

Audience Controls

Audience Controls are tight constraints, rather than suggestions, that Meta must respect when choosing your Advantage+ Audience. This includes:

• Locations
• Minimum Age
• Excluded Custom Audiences
• Languages

This works much like the Audience Controls for Advantage+ Shopping. You may only ship to customers in certain countries or states. Your product may not be available to people under a certain age. Or your product may not be relevant to those who already bought it.

These controls are necessary in those cases.

When You Can’t or Shouldn’t Use It

Advantage+ Audience is not available for the following situations:

• When promoting a Special Ad Category
• Advantage+ Shopping Campaigns
• Advantage+ App Campaigns
• Retargeting campaigns using Advantage+ Catalog

Meta already utilizes AI to generate your audiences for Advantage+ Shopping and Advantage+ App Campaigns.

Meta also recommends that you don’t use Advantage+ Audience generally when remarketing.

Set Up Audience Controls

When you click to Show More Options within Audience Controls, you’ll see minimum age, excluded custom audiences, and languages.

These are the tight constraints that Meta must follow when finding your audience. So, even when finding people beyond your suggestions, these rules will apply.

Here are a few things to consider…

1. Location. First, you will need to select countries or states if you can only ship to certain locations. But you should also consider including only certain countries here, too, depending on your optimization. Unless you optimize for a purchase, you can expect your ads to be delivered primarily to the cheapest countries. By default, Meta will include your home country here.

2. Minimum Age. There’s really no reason to include anything here unless there’s a legal reason to set a minimum age. Ignore the age of your “typical” customer. Meta will sort this out.

3. Excluded Custom Audience. Use this only when necessary. For example, you’re promoting a product that can only be purchased once.

4. Languages. Meta recommends leaving this blank unless you want to show your ads to people in a language that isn’t common to a location.

Set Up Advantage+ Audience

First, a couple of things to keep in mind here…

1. You can provide an audience suggestion, but it’s optional. At some point, you should experiment with both approaches: Providing a suggestion and not.

2. You can switch back to original audience options. If you’re not ready for this, know that it’s not forced on you (yet, at least). You can still go back to the old ways of targeting.

But you can provide custom audiences, lookalike audiences, an age range, gender, and detailed targeting as suggestions.

Just keep in mind that these are not tight constraints. Meta will prioritize these suggestions initially, but your ads can still reach people who wouldn’t qualify. If you have a tight constraint on age, you need to provide it in Audience Controls.

How Is This Different?

If you’re confused by how this is different than simply using Advantage Custom Audience, Advantage Lookalike, and Advantage Detailed Targeting, I totally understand. This stuff is eerily similar.

In both cases, Meta prioritizes the initial audience — at least, at first. In both cases, Meta can expand the audience to reach people beyond that group.

Here are the main differences, the way I understand it…

1. Age and Gender. When you use Advantage Detailed Targeting, Advantage Lookalike, or Advantage Custom Audience, Meta uses age and gender as tight constraints. The expanded audience will respect those settings. That isn’t necessarily the case with Advantage+ Audience.

While you can set a minimum age when setting up Advantage+ Audience using Audience Controls, the age range that you provide is a mere suggestion. The same goes for gender. You may think only women care about your product, but Meta can reach men if it’s determined it will help you get better results.

This may sound crazy, but it’s a matter of trusting the AI. Meta is learning from your data and results. You shouldn’t expect your ads to suddenly get shown to men if only women purchase.

2. Extent of Expansion. Meta actually calls this out specifically when talking about the benefits of Advantage+ Audience.

Advantage+ Audience provides the “broadest possible audience to search within.” Meta even says that this limitation prevents those other options from being more effective. That inability to go as broad limits the AI.

This also goes back to one of my original issues with Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience. We’ve never known how much Meta actually expands our audience when these are turned on. There’s not a way to see results broken down by people you targeted versus those who were part of the expanded audience.

The result was that most advertisers assumed that Meta was significantly expanding our audiences. But that may not have been the case.

Versus Going Broad

Going broad with targeting (removing all interests, behaviors, custom audiences, and other targeting) has increased in popularity among advertisers. How is this different?

You could set up an ad set using broad targeting without any inputs. You could also use Advantage+ Audience without any targeting suggestions. My understanding is that your ads will be delivered differently in each case.

Or at least slightly differently. We’ve heard about the power of broad targeting, but Advantage+ Audience is another level of AI targeting power. It’s similar to how the targeting power for purchases is greater for Advantage+ Shopping Campaigns than simply going broad when optimizing for purchases.

At least, this is how Meta advertises it. Feel free to test!

When To Use It

Like any strategy, you should experiment and find when it works best for you. If you had asked me a year ago what I thought about broad targeting or any of the audience expansion products, I’d give you a much different answer than I would now. Don’t assume that this is a bad idea.

In my opinion, this is best when optimizing for any type of conversion, especially a purchase (when not running Advantage+ Shopping Campaigns). I’d be wary of using it for top-of-the-funnel campaigns that optimize for link clicks, landing page views, video views, or any type of engagement, but we should still experiment.

The reason I’d be hesitant to use this for surface level engagement is that the algorithm will just work harder to get you those cheap actions. And those cheap actions are often not high-quality actions. And the algorithm won’t care.

That brings up a whole different philosophical discussion and a potential solution if Meta ever wants one. They could provide optimization options for high-quality traffic and engagement to prevent this potential issue.

The Future of Targeting

This is the direction Meta ads targeting has been going ever since the launch of Detailed Targeting Expansion (before it became Advantage Detailed Targeting). More automation. Less control. More AI-powered learning. And ultimately, less transparency in reporting and more trust in the algorithm.

This is just a hunch, but I assume that Advantage+ Audience will eventually completely replace Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience. There’s simply no value in having them anymore. They are too similar, and keeping them will only confuse advertisers.

We’re getting much closer to a time when we won’t provide any targeting at all. While that may sound scary, the truth is that Meta already has our targeting in the form of historical data. We don’t necessarily need to provide anything because the algorithm already knows who has converted on our website and is engaging with our ads and content.

Watch Video

I recorded a video about this, too…

Your Turn

Have you experimented with Advantage+ Audience? What do you think?

Let me know in the comments below!

The post How Advantage+ Audience Works appeared first on Jon Loomer Digital.

Meta started rolling out Advantage targeting in 2021, allowing the ads algorithm to expand your chosen targeted audience in certain situations. How and when expansion works is still often misunderstood.

It makes sense why. This topic has been a moving target.

In just two years, of course, three different Advantage targeting products with expansion capabilities have rolled out (and a confusing fourth on the horizon). It doesn’t help that the names and rules for how they’re used have evolved during that time.

Let’s clear up the confusion now…

Advantage Detailed Targeting

Originally announced as Detailed Targeting Expansion, Advantage Detailed Targeting was the first audience expansion product available.

When Advantage Detailed Targeting is turned on, Meta will “dynamically expand the audience to reflect where we’re seeing better performance and we may expand your audience further to include similar opportunities.”

This expansion applies only to the Detailed Targeting (interests and behaviors) that you enter, and expansion will not impact restrictions you apply related to location, age, gender, or exclusions.

In the example above, there is a checkbox that allows the advertiser the option of turning it on and off. But it is automatically on (and can’t be turned off) when optimizing for any type of conversion, value, app event, or app install.

In these cases, it will look like this (no checkbox)…

Advantage Lookalike

Advantage Lookalike (originally Lookalike Expansion) came next.

While the audience expansion concept is the same as Advantage Detailed Targeting, the execution is slightly different. Using the Custom Audience that you based your lookalike audience on as a guide, Meta’s system will expand beyond the percentage you selected for your lookalike audience if it’s determined you can get better results by doing so.

Advantage Lookalike is automatically turned on for all conversion, value, and app promotion optimizations. In these cases, it looks like this…

As with Advantage Detailed Targeting, the restrictions (location, age, and gender) and exclusions you set will still apply. Advantage Lookalike isn’t available for Special Ad Categories like housing, credit, employment, politics, and social issues.

Advantage Custom Audience

Next came Advantage Custom Audience.

Once again, Advantage Custom Audience allows Meta to dynamically expand your audience and move beyond your selected custom audience if it’s believed that doing so can improve performance.

This feature will be turned on automatically regardless of optimization when a custom audience is selected. However, unlike the other two options, the checkbox remains and this option can be turned off.

This is probably good as advertisers may want to limit their targeting to a specific custom audience in some cases. But, be aware that this may be turned on — I’ve been burned by this in the past when I thought I was reaching a hyper-targeted group.

Advantage Audience

If you weren’t confused yet, it’s going to start getting confusing now…

If you select both a custom audience and lookalike audience while optimizing for a conversion or other action that won’t allow you to turn off Advantage Lookalike, it will look like this…

But if you optimize for an action like a link click or landing page view (among others) where you have the ability to turn both Advantage Custom Audience and Advantage Lookalike on or off, the name changes to Advantage Audience.

There’s no new functionality here. You just can’t individually turn Advantage Custom Audience and Advantage Lookalike on or off. It’s a group selection.

Advantage+ Audience

And now it’s going to get ridiculous.

Yes, it looks like I just listed Advantage Audience twice. But, this time I’m actually listing Advantage+ Audience (emphasis on the “+”). In a May 11, 2023 announcement about new AI-powered ads tools, Meta provided details about Advantage+ Audience.

Advantage+ Audience is an AI-powered targeting tool that will develop an audience for you based on pixel activity, conversion history, and ad engagement. You have the option of providing targeting suggestions that Meta will initially prioritize. You can also use Audience Controls as tight constraints.

This all sounds very similar to Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience, but there are slight differences.

1. Advantage+ Audience uses AI to generate your audience. This is why it includes the “+” in the name and the others do not.

2. Advantage+ Audience has the ability to go broader than the others. Meta tells us this, but it would have been difficult otherwise to know for sure since there is a lack of transparency in reporting.

3. Advantage+ Audience uses age and gender as suggestions. When turning on Advantage Detailed Targeting, Advantage Lookalike, and Advantage Custom Audience, age and gender settings are used as tight constraints.

My guess is that Advantage+ Audience will eventually replace the other three.

Should You Use Advantage Targeting?

Okay, back on topic. Let’s focus on the three actual features relevant to this post:

• Advantage Detailed Targeting
• Advantage Lookalike
• Advantage Custom Audience
• Advantage+ Audience

I was initially pretty terrified of these features. I put in certain targeting and I want to use that targeting! But with time, it’s grown on me. Expansion is that middle ground between hard constraint targeting and going broad.

The way these features are defined, targeting expansion can’t hurt you. It can only help you. The audience may not be expanded it all. But if it is, it’s because that expansion can get you better results.

The problem? We have no idea whether your audience was actually expanded, how much it was expanded, or how performance was impacted by that expansion.

There should be a pretty simple solution to this. Meta should add a breakdown for audience expansion that adds rows to your report for your intended audience and the expanded audience. Without that, we’re left guessing regarding whether this is actually beneficial.

More transparency could also give advertisers more confidence in these products.

Your Turn

What’s your experience been with Advantage targeting expansion products?

Let me know in the comments below!

The post Advantage Targeting: How Meta Audience Expansion Products Work appeared first on Jon Loomer Digital.