The world of advertising on Facebook and Instagram is dynamic. Managing effective ads requires constant learning, testing, and optimization. Marketers should utilize available data sources — particularly free sources — to better understand how the social platform landscape is shifting. We can use Google search data (via Google Trends) to identify social media opportunities – especially related to advertising.

To help make this process easier, we’ve developed a dashboard that captures and compiles a variety of search data — all based on Google Trends. You can access that resource here. This article highlights context for the dashboard and guides advertisers on how to approach using Google Trends as a powerful resource in their toolkit.

What is Google Trends?

Google Trends is a free, publicly available tool that we can use to identify search popularity for specific search terms, as well as general search topics.

A few caveats: The information is provided for aggregate groups of Google users, and is not traceable user-level data. Also, note that Google Trends does *not* report direct search volume data. Instead, it is intended to provide base data to compare changes in search popularity over time. Importantly, it also provides comparison for popularity between terms or topics.

The tool can be simple to use, but there are many nuances for understanding all the possible data comparisons. While this article won’t go into depth on all of the limits and considerations of Google Trends, I will cover some key points within the context of the Social Media Search Activity tool we have available on Jon’s site.

Why Does This Matter?

Search behavior is a fantastic indicator of what people care about. You can think of it as an unfiltered read on what problems people are trying to solve.

For advertisers, this can be a powerful source of understanding what people want to learn more about. Or, what services people are seeking. Best of all, you can understand how these interests shift over time. This enables an adaptive approach where advertisers can consistently adapt their offerings to match against human needs.

To better understand potential implications, let’s explore some examples….

“Facebook Ads” as a Google Search Interest

Below, we’ve used Google Trends to snapshot a single search query: Google searches including the term “Facebook Ads,” as measured over time:

We’re looking here at a portrait of changes over time. Search popularity is presented on a scale from 0 to 100. Every chart you look at in Google Trends will be indexed in this way. This means that the high point is relative to whatever query or group of queries you are comparing in your data set.

In this specific example, we can see that there was a high point in the beginning of June 2020. We can also see that recent activity is continuing to be higher than any prior period. What this chart can not tell us is what may be driving that increased search interest. We can look more deeply at “Related Queries” (which is covered a bit later in this article) to explore potential drivers of changing patterns.

To dive deep, we can segment specific time ranges and evaluate related queries just for an isolated point in time. As an example: we can look just at the beginning of June 2020, and then see how Related Queries compare during that period vs. some other time frames when popularity may have been less for a similar search term. Needless to say, the topic of Related Queries deserves its own series of articles.

In the meantime, on to the next component…

Google Searches of “Facebook Ads” vs. “Instagram Ads”

Here we’ve added a second search query to compare, so we can see “Facebook Ads” vs. “Instagram Ads.” Notice how we still see the same high point in early June for Facebook Ads.

However, the addition of “Instagram Ads” to our research shows that, generally speaking, searches for Instagram ads are far less popular than searches for Facebook ads. We can also see that the volatility in search popularity for “Facebook Ads” is not as evident in searches for “Instagram Ads.”

Comparing Google Searches for “Ads” on Various Social Media Platforms

Now we’ve added additional social media platforms to our same advertising research term comparisons, including Google Ads, Twitter Ads, and Snapchat Ads. We can add up to 5 queries in any single view.

The snapshot above shows that search popularity for “Google Ads” is about twice as large (in popularity) compared to “Facebook Ads.” Additionally, “Google Ads” and “Facebook Ads” both experience similar changes in search popularity over time.

For some extra nerdy fun, you can export the underlying comparison data and calculate correlations between terms. Again, this may be material for a future article. However, I can share that “Google Ads” and “Facebook Ads” are highly correlated in this comparison. In this specific example, they have a correlation of .94 (whereas a score of 1.0 would be a “perfect” correlation). Meanwhile, “Facebook Ads” and “Instagram Ads” have a correlation of 0.86, which is also quite strong. This means that searches for “Google Ads” are more highly correlated with “Facebook Ads” than the same for “Instagram Ads”. Both comparisons have a strong correlation overall. These are interesting findings that we could explore more deeply.

Identifying Potential Opportunities based on Related Search Queries

We can look at how “Related Queries” differ between the search activity for various social platforms. Google Trends defines “Related Queries” as “users searching for your term also searched for these queries.” In this example, we’ll look at “Top Terms”, which are further defined as “terms that are most frequently searched with the term you entered in the same search session, within the chosen category, country, or region.”

Here in our homemade dashboard, we’ve pulled data for search popularity of “ads” for the different platforms, and placed these results into a side-by-side view for comparison:

We can learn quite a bit from this single view. First, “facebook ads” is the most common related query for searches for “Google Ads”. This is an indication of what we found in the correlation assessment earlier. People looking for information on Google Ads are often ALSO searching for Facebook Ads in the same search session. We can also see that people are often looking for information on “google analytics” in similar sessions that they are looking for info on “Google Ads”.

Meanwhile, with Facebook Ads we can see that “ads manager” is a common theme. This is an indication that people are seeking information on the Facebook ad buying interface. If you are in the space of offering services for advertisers, such findings can be a potential source of important insights.

Assessing Risks and Threats for your Social Media Platforms

Search data can provide a way for us to assess interest in a certain action that could be considered negative to a brand or cause. Deleting a specific platform is an example:

Here, we can see that interest in “deleting Instagram”is actually higher than “deleting Facebook.” This may be a surprising finding.

Again, as with other areas of search, there is opportunity for us to dive much deeper. The point is: there is insight in being able to explore and compare searchers’ activity regarding these social media platforms. It’s also interesting to be able to see when spikes happen. Or, to see how those spikes may –or may not– correlate, such as Facebook and Instagram in our example above.

Comparing Searches for Facebook Advertising Terms

Google Trends provides the ability to compare essentially any search term(s). We can apply that to an analysis more directly looking at search terms related to “Facebook Ads”:

An interesting finding from this is the strong association between searches for “Facebook Pixel” and searches for “Facebook Business Manager.” It’s also interesting to see how the popularity of searches for “Facebook Pixel” is actually higher than all of the other comparison searches here. If you are in the business of offering services related to Facebook Ads, focusing on the pixel could be an opportunity.

Search Data and Social Media: Powerful Possibilities

There are countless potential applications for using the information available from Google Trends. The dashboard we’ve provided is intended as a starting point for understanding generalities of how search trends can inform us about what’s occurring in the social media advertising landscape. Hopefully, the search data tool is useful for quickly identifying opportunities for social media. Or, at least to serve as the starting point for a deeper dive.

Feel free to bookmark the dashboard resource. The Google Trends information will stay updated over time, automatically. We’ll also plan to keep human-crafted written commentary, updated regularly.

Your Turn

How are you using Google Trends to identify advertising opportunities or threats? Do you find the dashboard we’ve developed useful?

Let us know in the comments below!

The post Using Google Trends to Identify Social Media Opportunities appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

You may be aware of a little Facebook ads experiment that I’m running right now. If not, go ahead and read this post (and maybe even become part of the experiment!).

The experiment is ongoing, and I’m going to save the bulk of my results until when it’s complete. But some things are so interesting, I just can’t help but write about it!

Based on the nature of my experiment, I’m able to learn some very interesting things about the decay of ads, activities and increase in costs over time.

Bottom line: If someone doesn’t act during the first two days of your ad, they become increasingly less likely to act as days pass.

Let’s take a look…

[Tweet “A Facebook ads CTR of 63% and a CPA of $.01? It’s happening. Take a look at this…”]

Experiment Refresher

I won’t completely rehash my experiment here (again, read this post), but it’s important to outline a few main points since they significantly impact my results — and allow me to learn things that won’t typically be seen.

1) I’m featuring a series of Facebook ads tips within ads. These tips are exclusive to those participating in the experiment and will only see this content via ads — or after someone engages with the ad. So far, there are eight total tips, and I’ve been promoting a new tip every few days since January 4.

2) You will only be targeted to see the newest tip if you read the preceding tip. Note that the only people seeing any of these ads will be very tightly targeted. They either opted in to see the ads in the beginning via a Website Custom Audience, or they see the latest tip because they read the last one (also via a WCA).

3) Once you read a tip, the ad will no longer be shown to you. This is again done via WCAs. This doesn’t prevent you from seeing the ad organically, of course (via comments, likes and shares), but this is a very big reason for a dramatic change in performance over time. As each day passes, the most engaged users are no longer targeted.

4) I’ve used both CPM and now Daily Unique Reach bidding. I’m not optimizing for an action because I want everyone who opted in to see the ads. I originally went with CPM, but frequency became too high, and I was especially wasting money once the ads were running for several days. Beginning midday January 15, I began using Daily Unique Reach across three ad sets (one for each placement) for each tip. This assures frequency can’t be more than 3.0 overall for a single tip in a day.

5) Once it’s been 14 days since last participating in the experiment, I stop showing a user the ads. I use a 14-day duration for some of my Website Custom Audiences to accomplish this. As you’ll see, I may want to limit this further (maybe seven days?). I want to leave some buffer, though, because it’s impossible to guarantee that I can show everyone the ads in a timely fashion.

Click-Through Rate Over Time

First of all, I’m not all that big on CTR. However, I feel this metric does a good job of at least showing the engagement level in these ads — and how that engagement changes over time.

Keep in mind that the only people seeing my ads are those who want to see them. In order to see Tip #1, they must have clicked the ad that invites them to participate. In order to see Tip #2, they must click the ad for Tip #1 (or otherwise see the page for Tip #1).

As a result, these click-through rates are off the charts. Higher than anything I’ve ever seen before. Absolutely insane.

Let’s take a closer look by day (up to Day 5)…

Day 1

• Highest CTR: Tip 3 (63.55%)
• Lowest CTR: Tip 6 (11.25%)
• Median CTR: 28.37%

Day 2

• Highest: Tip 2 (46.31%)
• Lowest: Tip 5 (9.57%)
• Median: 12.21%

Day 3

• Highest: Tip 2 (38.83%)
• Lowest: Tip 5 (5.30%)
• Median: 8.66%

Day 4

• Highest: Tip 2 (34.74%)
• Lowest: Tip 4 (4.51%)
• Median: 7.45%

Day 5

• Highest: Tip 2 (29.75%)
• Lowest: Tip 4 (3.67%)
• Median: 7.82%

Here are a couple of notes…

1) The first tips tended to get a higher CTR on Day 1. The exception is Tip 8, but otherwise this is consistent. An explanation for this may be simple fatigue as the excitement wears off the longer participants are in the experiment. (This is what I want so that I can reward those who make it to the end!)

2) Even the first three tips died eventually. The third tip had a CTR of 63.55% on Day 1, but had fallen all the way to 6.91% by Day 3. Tip 2 survived longer, posting 29.75% on Day 5, but quickly dropped to 5.87% on Day 6.

3) Tips later in the experiment had lower initial CTRs, but they didn’t die as suddenly either. Interestingly, Tip 6 has remained steady from Days 1 through 6, keeping between a range of 8.66% (Day 3) and 17.69% (Day 4).

4) The lowest CTR recorded is 0.58% (Day 11 for Tip 1). This is insane how much it dropped over time. Part of this is that those who were interested were quickly separated from those who weren’t. Excitement was high during the first few days.

Cost Per Website Click Over Time

My main goal of this experiment was to drive traffic. Of course, the cost of driving that traffic will depend on several factors. I’ve seen CPMs as high as $9.49 for a given day and as low as $1.83. That, combined with the rate of getting those clicks, will cause a wide variation in costs.

But as you can see above, there is definitely a theme here: Costs are at their lowest during the first two days. After that, it gets increasingly more costly.

Let’s take a closer look by day (up to Day 5)…

Day 1

• Lowest Cost Per Website Click: Tie – 4 Tips ($.01)
• Highest Cost Per Website Click: Tie – 4 Tips ($.02)
• Median Cost Per Website Click: $.01

Day 2

• Lowest: Tip 2 ($.01)
• Highest: Tie – Tips 3 and 4 ($.03)
• Median: $.02

Day 3

• Lowest: Tie – Tips 2 and 7 ($.02)
• Highest: Tip 3 ($.06)
• Median: $.05

Day 4

• Lowest: Tie – Tips 2 and 7 ($.03)
• Highest: Tip 5 ($.08)
• Median: $.05

Day 5

• Lowest: Tip 7 ($.03)
• Highest: Tip 1 ($.09)
• Median: $.06

These are insane numbers for me. I’m used to getting a Cost Per Website Click in the area of $.15 to .20. On Day 1, I’m routinely getting either $.01 or $.02.

But notice the steep dropoff. Even following Days 1-5 doesn’t do this dropoff justice. Yes, the median rises from $.01 to $.06 in five days, but take a look at Tips 1 and 2 in particular.

By Day 7, I was paying $.20 for Tip 1. That’s now up to $.74 by Day 16. Again, remember that this was the first tip after the opt-in. It’s not a surprise that there are so many dead beats for this tip. That significantly decreases with each new tip.

On the flip side, look at Tip 4. That is consistency. While it was very average on Day 1 at $.02, it remains at $.07 on Day 10.

Contributing Factors and Considerations

A couple of very important factors to point out here.

First, these ads are launched in sequence. So when I launched Tip #1, the only competing ads had to do with inviting people to the experiment, welcoming them and allowing for the opt-out.

Tip #8 is running at the same time as the ads for all of the other tips. While someone won’t be served Tip #8 at the same time as Tip #7, they may be served tips for multiple ads in the same day if they click often.

Also, keep in mind that I changed my bidding approach on January 15 to Daily Unique Reach. This seemed to have a very positive impact on CTR (you’ll see the up-tick for many of these tips at about the same spot). I was wasting money on showing the same people the ads multiple times in a day — this became particularly bad several days in.

Note that this improvement in CTR doesn’t appear to have a major impact on Cost Per Website Click. Part of this could be due to an increase in CPM while using Daily Unique Reach. But this could be contributing to the lower Day 1 CTR numbers, but overall steady results over time.

Finally, understand that this experiment is fluid. While Tip 1 may have been published on January 4th and promotion started that day, some people didn’t start participating until several days — or weeks — later. As such, these results aren’t completely pure. “Day 10” doesn’t represent promoting that tip to the same audience for the 10th day. Many of the most engaged have already seen it and are now excluded, but some new people are being continually added.

Lessons Learned

This underscores two main points for me…

1) If you make ads interesting — if people are looking forward to seeing them — the results can be incredible. CTRs of over 50% and Costs Per Website Click of around $.01 are unheard of — at least for me.

2) The people most likely to act will do so in the first two days. This may be the most important lesson of all. Watch costs closely because if you don’t take advantage in those first two days, it won’t get any easier.

Your Turn

I’m having a lot of fun running this experiment, and as you can see there is plenty to learn. I’m looking forward to sharing more in the future!

What do you think of these results? Anything surprise you?

Let me know in the comments below!

The post Facebook Ads Results: Click-Through Rate and Cost Per Action Over Time appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

About a month ago, Facebook rolled out the ability for Facebook marketers to target organic posts by interest. Is this something you should do?

I immediately started an experiment, and today I’m ready to share the results of the first phase.

[Tweet “Does targeting your organic Facebook posts by interest result in more distribution?”]

What is Interest Targeting of Organic Posts?

As you know, when running Facebook advertising there are numerous ways that you can target your ideal audience. One of those ways is through interest targeting, where you can show your ads to people based on the things they interact with on Facebook.

But starting in December, this can also be applied to organic publishing by brands. Using interests, you can reach fans (only fans) who have specific interests. This may be helpful if you feel certain fans who have specific interests may be more valuable than others.

To do that, click on the targeting icon when creating a post from your page…

Then click “Select Targeting”…

Next, select “Interests”…

Click “All Interests”…

And enter in the interests you want to target…

When you do this, your organic post (no payment for ads) will only be shown to your fans who have those specific interests.

My Experiment

I wondered if this would be effective so I decided to run an experiment. I’m breaking this into two different phases:

• Targeting ads at fans by interest
• Using these learnings to target organic posts by interest

In the first step, I simply wanted to see if ads equally distributed among fans with particular interests (or any interest) resulted in a higher engagement or conversion rate.

If I find that there is a significant benefit, I will later start testing this organic interest targeting. Note that the benefit needs to be significant because by using this feature you will further limit your audience.

First, thanks to Audience Insights, I was able to isolate four interests to work with:

1. Amy Porterfield
2. Social Media Examiner
3. Mari Smith
4. Facebook for Business

Each of these interests are very common among my fans and website visitors.

Next, when promoting blog posts I created the following ad sets:

• All Fans
• Fans + Amy Porterfield Interest
• Fans + Social Media Examiner Interest
• Fans + Mari Smith Interest
• Fans + Facebook for Business Interest

I then used URL tagging so that I could identify which audience was targeted when traffic comes to my site. This way, I could not only see the traffic driven by the ad (what Facebook tells me), I could also learn more about the behavior of these people when they were on my site (what URL tagging + Google Analytics tells me).

Finally, I tracked several conversions as well with Facebook conversion tracking.

I spent approximately $192 on each of these ad sets.

My Results

Following are the results of the first phase of this test — using ads to measure the engagement and conversion rate of my fans, some with specific interests and some not.

Note that I’d normally care about metrics like Cost Per Website Click. However, in this case I was most concerned with the rate at which those reached clicked on the link (Cost Per Website Click can be heavily influenced by CPM). Additionally, I cared about what they did after clicking.

So let’s take a look at the Website Click Rate by audience (Website Clicks/Reach). Note that I’m not using CTR (Click-Through Rate) because that looks at any click — not necessarily a click to website.

• Fans with Amy Porterfield Interest: 4.2% Website Click Rate
• Fans with Social Media Examiner Interest: 4.4%
• Fans with Mari Smith Interest: 5.1%
• Fans with Facebook for Business Interest: 4.0%
• All Fans with Any Interest: 3.7%

Following is the rate of conversion reported by Facebook for those who opted into anything while visiting my site:

• Fans with Amy Porterfield Interest: 0.36% Conversion Rate
• Fans with Social Media Examiner Interest: 0.18%
• Fans with Mari Smith Interest: 0.36%
• Fans with Facebook for Business Interest: 0.17%
• All Fans with Any Interest: 0.13%

Keep in mind that I was promoting a blog post in these cases with no clear call to action to opt-in or buy — other than the ads on the sidebar. So this was pure traffic, nothing else. I was not expecting conversions, but any conversions that happened would be a bonus.

Combining URL tagging and Google Analytics, I’m able to break down time on site as follows:

• Fans with Amy Porterfield Interest: 0:46 Time on Site
• Fans with Social Media Examiner Interest: 1:00
• Fans with Mari Smith Interest: 1:07
• Fans with Facebook for Business Interest: 0:36
• All Fans with Any Interest: 1:16

On one hand, my fans with interests in Amy Porterfield or Mari Smith in particular led to higher engagement and conversion rates. Those with an interest in Amy Portefield also led to less time on site.

My Strategy: Unchanged (So Far)

Let me say up front that I expected to get better results from fans who had specific interests. By separating those who have interests in related people and brands from those who don’t, I’m focusing on those who are more likely to be deep into my industry (no casual fans, friends, family, etc.).

Let’s think about what happens when you target an organic post only at fans who have particular interests. While the rate of engagement may improve, you’re also limiting your audience — maybe significantly — by doing so.

By focusing on my fans who have these interests, my audience is limited as follows:

• Fans with Amy Porterfield Interest: 26% of the Full Audience
• Fans with Social Media Examiner Interest: 35%
• Fans with Mari Smith Interest: 21%
• Fans with Facebook for Business Interest: 56%
• Fans with Any of These Four Interests: 70%

So if I focus only on one interest, I am cutting out between 44% and 79% of my total audience. If I target all four interests, I cut out 30%.

The website click rate was a little bit higher when using interest targeting, but is it enough of a difference to warrant cutting out at least 30% of my audience from seeing my next post?

The conversion rate was higher for other interests, but would I get more conversions — or just a higher rate — by targeting this way? My guess is it would only be a higher rate, but a smaller number.

Finally, I found the time on site to be telling. Fans with the Facebook for Business interest were pretty close to worthless, spending less than half as much time on site as targeting fans with any interest. And I received better time on site across the board when not limiting by interest.

I do struggle with this a bit. To make a change in my organic posting strategy, I wanted to see a noticeable advantage for doing so. There does appear to be an advantage — at least in rates. But will that result in more website clicks, opt-ins and sales?

I can’t say for sure. We know how Facebook works. We know that if a post is getting great engagement, it will be shown to a wider group. So it’s possible — but far from conclusive — that limiting my initial audience by 30% or more could ultimately lead to more results.

But I doubt it. I will now start experimenting a bit with some limited interest targeting of my organic posts, but I do not expect to stick with this for the long haul.

Based on the results I’ve seen so far, I’m going to eliminate the Facebook for Business interest and run a limited test focusing on fans who interact with Mari Smith, Amy Porterfield or Social Media Examiner. That would eliminate 53% of my audience without those interests.

Your Strategy?

I can’t yet bring myself to make a major change here, but I’m still leaving the door open to the next phase of this test. And my results don’t have any impact on whether this would work for you.

I think this is something to be considered, particularly for brands that have a casual audience. Maybe you built your audience through contests, and many of your fans aren’t valuable. Maybe you bought fans. Or whatever the cause, maybe you simply get a very poor engagement rate from your fans.

If Facebook doesn’t show your content to many of your fans, it could be because of these things. It may help by limiting the distribution of your posts to only fans who have particular relevant interests. It’s certainly something worth trying!

Your Turn

Is interest targeting of organic posts something you’ve experimented with? What results are you seeing?

Let me know in the comments below!

The post Should You Use Interest Targeting of Your Organic Facebook Posts? appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

As a visitor to my website, you may have recently seen a Facebook ad from me inviting you to participate in an experiment.

The results I’m seeing so far from this experiment are incredible, so I wanted to briefly break down what it is I’m doing and the thought process behind it.

[Tweet “Facebook ads don’t have to suck. What if people looked forward to seeing them? Check this experiment…”]

Ads Don’t Have to Suck

Late at night on December 30, I was wide awake in bed. For whatever reason, I was grappling with the perception of Facebook ads and ads in general.

Ads intrude. Ads sell. Ads push. Ads are seen as a necessary evil to use the Facebook platform.

But what if people wanted to see my ads? What if it was a pleasant surprise to them? What if they felt they had to click? What if these ads served them content they couldn’t see anywhere else?

Creating a Facebook ads campaign that works is all about reaching the right people with the right message at the right time. But what if we took that a step further?

Facebook ads, in general, exist to show you something you may have otherwise missed. They behave as a reminder to buy that product, opt in to that offer or click that link.

These ads don’t truly provide value.

I’m guilty of this, too. I promote the content you may have already seen (though I do exclude those who already read a certain post when promoting it). I push to make sure that you didn’t miss it the first time.

Facebook Ads vs. Email Newsletters

Let’s think about ads the way we think about our email newsletter. No one wants to sign up for your stupid newsletter if all it does is remind you to read a post.

I have work to do on this personally. I email every time I publish a new blog post. The hope is that I can provide value, background or a different angle within the email version.

Still, that’s boring. That’s why you should provide lead magnet content in exchange for the email address.

People are no longer opting in for a boring email newsletter. They are giving you an email address to see content that they couldn’t see anywhere else.

Ads Can Serve Exclusive Content

At 1:10am, I sent the following series of texts to John Robinson, my Backup CEO:

Crazy idea: Facebook ads Easter egg course…

Completely free but unique. People would want to click my ads.

I was tired. It may not have made the most sense at the time. But the idea was very clear in my mind. It was a huge, shiny lightbulb that was keeping me awake.

Facebook ads don’t have to suck. If done right, people may look forward to seeing them. They don’t have to push you to see content you may have seen otherwise.

Facebook ads, like an email opt-in, could be a benefit to the user.

The Experiment

I had very little time. I knew that by noon of that day (11 hours away), my family and I were heading for the mountains for a little New Years vacation. I needed to get working.

So I created the ad you saw at the top. I targeted fans and website visitors.

The concept was simple:

1. Click the ad to opt in (using a Website Custom Audience)
2. Get served an EXCLUSIVE Facebook advertising tip
3. Those who viewed that tip would be served another (and another…)
4. There’s a surprise for those who make it to the end

The audience was highly relevant. Those who participate would be the most engaged members of my fan base and website visiting community. Those who participate SHOULD be extremely engaged.

I decided to add a wrinkle to up the engagement even more and lower the waste: An opt-out.

I am running a second ad that looks like this…

If you don’t want to participate, I don’t want to waste money showing you the ads. This goes not only for the initial pool of people, but I also allow those who initially opted in to change their minds.

I know. This is beginning to sound a bit nuts. I am spending money to show exclusive content to a small number of people. I’m also spending money to ask people to opt out of seeing my ads.

But I have a theory. These are my most engaged users. The audience may not be huge, but they are the ones most likely to opt in and buy.

I’m not pushing anything in these articles. But each tip will include ads in the sidebar for my free ebook, Power Editor training course, one-on-one service and Power Hitters Club.

I’m tracking conversions for all of these things. I’m also using UTM parameters to track further in Google Analytics.

I am going to dump more than $3,000 into this experiment. Will it be worthwhile? We’ll see…

Early Results

So far, so good.

Here are the early stats on the people viewing Tip #1…

And here are the early stats on the people viewing Tip #2…

What’s even crazier about Tip #2 is that the CTR on mobile is 68%!

I know. It’s a small sample size. While I’m spending the bulk of my budget just getting people to participate, I’ve spent only a few dollars to drive participants to content.

But this is eye opening. We’re still talking about a total of 819 website clicks for about $15. That’s freaking ridiculous.

The entire campaign has also resulted in 213 conversions worth $1,041. Most of those conversions are free opt-ins, but we know there is long-tail value there, too.

Participate in the Experiment!

Initially, I felt like I shouldn’t write about this experiment until it was complete. But I want anyone who wants to participate to get on board.

DO NOT CLICK THIS LINK UNLESS YOU WANT TO PARTICIPATE!

The thing is, you should already be seeing the invitation ad on Facebook if you’ve been there lately. Or maybe you were ignoring it (you wouldn’t do that!).

But otherwise, go here to essentially opt-in to this Facebook ads experiment.

Once you visit that link, you’ll soon see Facebook ads from me delivering a series of tips. These tips will essentially break down precisely how the experiment was run.

And if you don’t ever want to see these ads, you can opt out.

Note that this is a two step opt-out. I understand some people will be curious and just can’t help but click! So you’ll be asked to click another link once on that first page.

More to Come!

I can’t wait to reveal the results of this. Stay tuned!

Have you ever done anything like this before? What do you think of the concept of serving exclusive content via Facebook ads — making them something that is desired?

Let me know in the comments below!

The post An Experiment: Facebook Ads Don’t Have to Suck appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

When you analyze your Facebook advertising reports, are you focusing on all of the right metrics? Many aren’t, even when their base metrics are correct.

A prime example of this is with website clicks. Recently I’ve been seeing that I can get a very cheap cost per website click when targeting Lookalikes and interests. In fact, it’s been far lower than the same costs when targeting my fans.

But how could this be? It could mean that either Facebook is doing an insane job assembling Lookalikes and interests. It could also mean that the quality of my fan base is not what I thought.

That’s why it’s important to dig beyond the website click to determine the quality of those actions.

[Want to master Facebook ad reports? It’s one of the featured topics in my 2015 FB Mastery Workshops!]

[Tweet “This experiment shows the importance of digging into the quality of the FB Ad actions reported…”]

The Experiment

During the past few weeks, there were two main posts that were at the center of this experiment:

1) It May Be Time for You to Quit Marketing on Facebook

2) No More Promotional Posts: Facebook Will Penalize More Brand Content

In each case, I created four different ad sets, one for a different audience:

• Fans: 25-49 in US, UK, Canada and Australia who speak English
• WCA 30: Website Visitors (past 30 days) 25-49 in US, UK, Canada and Australia who speak English
• Lookalike Audiences: Based on Fans and Website Visitors 25-49 in US, UK, Canada and Australia who speak English
• Interests: Mari Smith, Amy Porterfield, Facebook for Business or Social Media Examiner 25-49 in US, UK, Canada and Australia who speak English

Ages and countries are based on what I know about my current customers. I based my Lookalike Audiences on groups that I regularly target and who convert. The interests were determined using Audience Insights as those that are most common among my audience.

I also used URL tags so that I could track those who clicked the link from each ad to determine what they did while on my website. For example, the campaign name for the tag may be “quitlookalikes” for the ad reaching those people identified as a Lookalike Audience.

I also excluded people who would have already read the blog post using Website Custom Audiences to eliminate waste.

For each campaign, I created ads that promoted an existing post. I split up spend between each audience as evenly as possible.

Quit Facebook Post (Spend)

• Fans: $136.17
• WCA 30: $135.95
• Lookalikes: $136.28
• Interests: $136.21

No More Promotional Posts (Spend)

• Fans: $71.04
• WCA 30: $71.03
• Lookalikes: $71.04
• Interests: $71.04

For the remainder of this blog post, I will combine results for identical audiences. For example, total spend targeting fans is $207.21 ($136.17 + $71.04).

• Fans: $207.21
• WCA 30: $206.98
• Lookalikes: $207.32
• Interests: $207.25

Cost Per Website Click

The primary purpose of these campaigns was to drive traffic to my website. Here are the total number of website clicks and costs per website click for each audience:

• Fans: 749 Website Clicks ($.28 per Website Click)
• WCA 30: 1,416 Website Clicks ($.15 per Website Click)
• Lookalikes: 1,635 Website Clicks ($.13 per Website Click)
• Interests: 1,242 Website Clicks ($.17 per Website Click)

Since my primary objective was driving website traffic, it would appear on the surface that my most efficient audiences were Lookalikes and WCA 30. Even Interests were in the acceptable range.

On the other hand, it cost me $.28 per website click when targeting fans. This is a group that I target regularly. They are those most likely to engage with my post. Yet, it would appear that targeting them this time was a complete waste of money.

Conversions Generated

Keep in mind that my primary objective here was to drive website traffic to blog posts that were not positioned to convert. I did, however, have both a pop-up and top right widget promoting my ebook. So a percentage of those who visited these posts would undoubtedly convert.

When I created these campaigns, I also tracked pixels associated with this ebook so that I could see how many of those who clicked the ad to read my post ended up converting as well.

Following is the number of ebook conversions by audience:

• Fans: 22
• WCA 30: 19
• Lookalikes: 3
• Interests: 9

That’s crazy, right? Before we saw that fans resulted in fewer than half the website clicks of Lookalikes, yet they converted at a far higher rate.

Here’s a look at the percentage of website clicks that resulted in a conversion:

• Fans: 2.9%
• WCA 30: 1.3%
• Lookalikes: 0.2%
• Interests: 0.7%

Something to keep in mind here is that fans and recent website visitors are far more likely than interests and lookalikes to have previously subscribed to my ebook (more than 10,000 people have subscribed). As a result, a percentage of fans and recent website visitors couldn’t have subscribed again.

To summarize: It cost me far more to drive website traffic when targeting fans, but those people ended up being far more likely to convert. On the flip side, I could drive lookalikes to my website very inexpensively, but conversions were virtually non-existent.

Time on Site

Since I was using URL tagging on the links in my ads, I can break down the amount of time people spent on my site who clicked those links with the help of Google Analytics.

Full disclosure: A small percentage of those who clicked the links wouldn’t have viewed my ad. If someone shared my ad to Twitter or somewhere else, the link could have then been clicked by someone not in my target audience. Still, that percentage will be small, so this analysis remains valuable.

Here is a breakdown of average time on site by audience:

• Fans: 1:12
• WCA 30: 0:55
• Lookalikes: 0:26
• Interests: 0:33

Based on conversion results, this shouldn’t be a surprise. Fans spent more than twice as long on my site than Lookalikes and interests. My recent website visitors weren’t far behind, spending more than double the time of Lookalikes and 0:22 more than interests.

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Dig Beyond the Click

This is an example of why you need to dig beyond the click when analyzing your results. I’d typically say that someone focusing on Cost Per Website Click when traffic is the objective is looking at the right metrics, but clearly that’s not always enough.

My instinct has always been to target those who are most closely connected to me first. That’s why I always target fans and WCA 30. I rarely go beyond that since I have less trust in the quality of the audiences when using interests and Lookalikes.

But if I didn’t know any better, I would have stopped my ads that targeted fans. In fact, I may have escalated budget on the ads targeting Lookalikes.

As you can see, that would have been a huge mistake. Interests and Lookalikes were giving me empty clicks while fans and recent website visitors provided quality visits.

In the end, the quality of actions you’re measuring is most important!

Your Turn

Have you broken down your results in a similar way? What are you seeing from these four different audiences?

Let me know in the comments below!

The post Facebook Ads Reporting: Not All Website Clicks Are Created Equal appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

One of the primary struggles of marketers is understanding what is important. Taking the data given to them and making it into something meaningful.

This is why so many focus on secondary metrics, and obsess unnecessarily over things that have very little to do with their success. It’s why there is such a need for a way to measure actual value of Facebook marketing efforts.

Many have tried — though unsuccessfully — to measure the value of a page like. While I do believe you can measure this number for your own page, it certainly isn’t something that you can apply globally. The value of your fan is not equal to the value of mine, as the source and context are important.

The same goes for determining the value of an individual Facebook post. I can assign a dollar value based on what particular actions are worth to me, but what these actions are worth to another brand are irrelevant.

Still, this is an exercise that I’ve become increasingly curious about. I promote my blog posts at an average rate of $.16 per website click. Traffic is valuable to me, so it’s a routine that I repeat every time I’ve published a new post (and I will do it again upon publishing this one).

As a result, I can determine that a website click from a Facebook post is worth $.16 to me. That then allows me to look at individual organic posts in a whole different light.

Forget Reach for a moment. Forget comments, likes and shares. If your objective was to drive website traffic, how many website clicks resulted from your post, and what is that worth to you?

Last week, I experimented with a post and I want to share my results.

In the end, this is only one post. It’s my post, and as such it is the smallest of sample sizes. But you can participate in a similar exercise to learn the value of your own posts.

My hope is that this will help you better focus on the metrics that actually matter and isolate the posts that make the most impact for you.

[Tweet “Jon Loomer determined that the value of one of his organic Facebook posts is $989.47. Here’s how…”]

The Experiment

To determine the value of one of my Facebook posts, I needed to track it from beginning to end — or as close to the end of a post’s organic distribution as possible.

So I would need to know more than just how many people saw the post or engaged with it. I wanted to know what people did after clicking on it.

For the purpose of this experiment, I am focusing on a link post that drives users to my website. While I would eventually promote the post, I delayed promotion for two days in this case so as not to impact my organic results.

The Post

I wasn’t planning to write a second post last week, but I decided I wanted to run this experiment shortly after my Monday post was published. And since I didn’t catch it from the beginning, the results weren’t going to be complete.

So I decided to write a post about something that has been bugging me — a rant in response to those marketers who constantly complain about Facebook.

I knew that the post would be popular since it was a controversial topic. This would make for a good test subject since it would bring a decent sample size of traffic without promotion.

URL Tagging

In order to track the activities of users who clicked the link to my post from Facebook, I would need to utilize URL tagging. Using the Google URL Builder, I created a URL with the UTM campaign of “quit-organic.”

A couple of things to keep in mind…

1) This will include users who clicked the tagged link outside of Facebook. This happens when someone reads my original post on Facebook and then shares it via email, Twitter or somewhere else. That same link could then be clicked from another social network.

I’m okay with this since the original click came from my share. Had I not shared that link, it’s unlikely the user would have seen and clicked it on Twitter.

2) Not all conversions are tracked. What’s nice about Facebook ad reports is that you can see how many people convert (buy or register), even if they leave your website and come back at another time — or even on another day. Those conversions won’t be captured in this experiment.

Remember that this is focusing purely on the organic post, so I won’t have access to that data within the ad reports. I need to instead focus on Google Analytics (with help from my URL tagging), but the conversions reported will not include those return visitors.

Determining Values of Actions

First, it’s important to remember that you need to determine what is valuable to you. I focus on specific actions — particularly those actions that are my campaign objectives. As a result, I do not assign substantive value to reach, comments, post likes or shares.

That doesn’t mean that the actions I value are the only ones. It all depends on the goals of your business.

For the post in my experiment, the primary two actions I can measure are link clicks and page likes. We know that these things happen from a link share.

Note that my blog post is not selling any product. It’s not even pushing registration for anything. But when those users click my link and come to my site, they may convert even though it wasn’t the initial objective. I want to capture these people.

I then want to track the following actions:

• Link Clicks
• Page Likes
• Conversions

Measuring page likes will be an inexact science since Facebook doesn’t report number of page likes that result from a specific organic post (only ads). But I can make a very close guess that won’t significantly impact my results. Also note that since this is an organic post, the only page likes that will result will be from those non-fans who see it after a fan engages with it.

I will measure all kinds of conversions — including sales, though I don’t expect sales in this case.

The value of these things are unique to me based on what I have spent to acquire these actions in the past. It does not matter what you or anyone else pays for these actions. That would be irrelevant.

So I ran reports for all of 2014 to determine the value of a link click, page like and various types of conversions based on what I’ve spent to acquire them.

When determining the value of a page like, I only focused on those campaigns where the objective was Page Likes. Ads run with another objective that yielded a handful of page likes were not counted.

For link clicks, the objective needed to be Clicks to Website. Additionally, I only focused on the promotion of my own blog posts.

For conversions, the objective was Website Conversions and for that specific conversion type. For example, I found the average cost for a registration to my 9 Ways Ebook and Power Editor Webinar.

There were also a handful of other conversions that I’m not running ads for — my dimensions infographic and the general newsletter opt-in. As a result, I simply found the average of the ebook and webinar values to assign to “all other” conversions.

For me, then, the values of these things are as follows:

• Link Click: $.16
• Page Like: $.57
• Ebook Registration (Conversion): $1.61
• Power Editor Webinar Registration (Conversion): $1.13
• Any Other Opt-in (Conversion): $1.37

The Value of My Post

Using Google Analytics and URL tagging, I am able to isolate the number of link clicks, ebook and webinar registrations that came directly from my organic post before it was promoted.

• Link Clicks: 5,232
• Ebook Registrations (Conversions): 36
• Power Editor Webinar Registrations (Conversions): 3
• Other Opt-ins (Conversions): 14

For page likes, we need to make an educated guess. When I later promoted this post, I would receive 59 page likes for 2,452 website clicks. At that rate, I’d receive about 126 page likes when this post was running organically (and really, it very likely generated more than that as an organic post).

So now let’s determine the value of each action for this post…

• Link Clicks: $837.12
• Page Likes: $71.82
• Ebook Registrations (Conversions): $57.96
• Power Editor Webinar Registrations (Conversions): $3.39
• Other Opt-ins (Conversions): $19.18

TOTAL VALUE: $989.47

An Analysis of Value

Some may think I’m cherry picking this post, so let me break down the number of website clicks my shares of blog posts have driven during the past 30 days (paid in parentheses):

• No More Promotional Posts: 8,263 Website Clicks (2,235 Paid)
• Facebook Doesn’t Care About Your Reach: 1,902 Website Clicks (447 Paid)
• Daily Unique Reach: 429 Website Clicks (8 Paid)
• URL Tags: 1,035 Website Clicks (145 Paid)
• Copyblogger Killed its Facebook Page: 5,053 (2,723 Paid)

So the traffic generated from this post is consistent with two of my recent high performers.

Something to consider is that this post also likely generated a lot of “mainstream” attention. As a result, they may not be my target audience for products. My more typical traffic is much more likely to result in a sale.

It also doesn’t consider my sales funnel. I added 63 people to my email list, but there are also likely hundreds of new people I can retarget with ads since they have now visited my website. Many of my sales result from nurturing, not first-time visitors.

Overall, it’s a fun exercise though certainly not perfect. But considering how much I value traffic, it reiterates just how valuable a highly engaged fan base actually is. Without sharing this post organically, I would have needed to spend nearly $1,000 to get similar results.

Your Turn

I encourage you to run a similar experiment with your own content. Remember that you should use actions that are valuable to you, and you should research your own data to determine those values.

How much are your Facebook posts worth? Let me know in the comments below!

The post The Value of a Facebook Post [Experiment] appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

As an advanced Facebook marketer, you eat up the data. You want every insight into what is happening with your marketing.

A great, but underutilized, tool to help measure impact is URL tags — also known as UTM parameters. In this post, I am going to explain what URL tags are, my experiment and how you can use them, too.

[Tweet “Here’s an experiment showing how to use URL tags to measure viral impact of your Facebook post…”]

What Are URL Tags?

URL tags are added to the end of a URL to help you better track and measure clicks on a link. Tags make it easier to know where users were when they clicked your link, but they can also help you learn a whole lot more.

To help you understand URL tags (and use it yourself), check out Google’s URL Builder.

To build a URL with tags, you’ll need to tell Google the following (* marks required):

• • *URL: Original link to your website
  • *Campaign Source: The referrer, like Facebook or a Newsletter
  • *Campaign Medium: Promoted post, banner, email, etc.
  • *Campaign Name: Product, promo code or slogan
  • Campaign Term: Paid keywords
  • Campaign Content: Use to differentiate ads

You really can’t make mistakes on any of this. This will simply be useful later to identify activity for this campaign within Google Analytics.

As you add the required content, Google builds the URL for you in the bottom text box. You should then use that URL in your campaign.

My Experiment: Applying the URL Tags

I don’t often use URL tags, but I decided to use it for my post What Copyblogger Could Have Done With Its Facebook Page. Since I expected the post to be popular, I wanted to measure how much impact a single share of mine could make.

As soon as I published my post, I created the following URL with the URL Builder:

What Copyblogger Could Have Done With Its Facebook Page

I then shared this link to my Facebook fans organically before promoting it with an ad. That ad would target my fans, website visitors and a Lookalike Audience.

My Experiment: Website Clicks on My Post

As of the moment I write this, Facebook reports that my post has generated 4,739 link clicks.

Some of this was organic and some paid. According to Ads Manager, the ads I’ve run have generated 1,790 of those clicks.

My Experiment: Total Clicks on Tagged Link

Now let’s dig into Google Analytics to see the total number of people who have clicked this link. We do this by clicking on “Acquisition” and “Campaigns” within the side navigation.

As you can see below, Google reports that this campaign has received 6,446 total clicks so far.

My Experiment: Where Did the Other Clicks Come From?

Wait a minute… Google is reporting that my URL with campaign tags received 1,707 more clicks (+36%) than Facebook reported. Is this just another example of data not matching up?

But then I clicked on one of the many shares of my post on Twitter…

Guess where that link goes when you click on it? You guessed it. Here…

Recognize that link? It’s the URL I built when it was shared to my Facebook page.

Are you following what happened here? I only used that URL once, when I shared it to my Facebook page. No one else would have created it. But when many of those 4,000+ people clicked the link and loved it, what do you think they did?

Yep, they shared it. Sometimes they shared it to Facebook again. Sometimes they emailed it. Sometimes they shared it to Twitter and who knows where else.

In fact, of the 14,215 total views of that post so far, 45% can be traced back to my initial share to Facebook. That’s pretty powerful, right?

Bottom line is that these URL tags help me see the impact of my share on Facebook first hand. The source of this person’s link could be many degrees separated from the initial share — mine!

How You Can Use URL Tags

I did this mainly for entertainment purposes. But you should consider using URL tags to help you measure the true impact of your marketing.

For example, the next time you launch a promotion — whether for yourself or a client — attach URL tags to your link share or ad. This way, you can use Google Analytics to separate the traffic driven by your share from those of others.

And if you share that link in multiple places (which you should), add a tag consistent with that source (email, banner ad, etc.). This will help you isolate what worked best.

Remember that Google will return not only the number of people who clicked the link directly, but those who clicked it after others have shared it. As with my Facebook example, this shows how viral your initial post went.

Your Turn

Have other creative examples of using URL tags? Let me know in the comments below!

The post How Using Facebook URL Tags Can Help Measure Viral Impact of Posts appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

Cookie cutter articles are routinely written that proclaim that “the Facebook sidebar is a wasteland” or “mobile is where it’s at on Facebook.” They’re all wrong.

In this post, I’m going to explain why before presenting my own data to show you what I’m seeing regarding the evolution of CPM costs and Cost Per Page Like by placement, dating back to August of 2013.

[Tweet “Facebook CPM and Cost Per Page Like are evolving, depending on placement. Check this out…”]

The Problem With Universal Truths

Reports come out regularly that give us an idea of the trending costs and performance of Facebook ads. They are interesting, but they are nothing more than a mash-up of hundreds of advertisers.

How much were those advertisers spending? What were they promoting? What were the sizes of their potential audiences? Were they using CPM, CPC or oCPM? Were their ads effective?

These reports are good for entertainment purposes. They can even provide a lightbulb moment, inspiring you to take a second look at your ads.

But do not let these reports guide your advertising habits. Focus on your results only.

The truth is that the Facebook advertising landscape is constantly evolving. Costs will go up and down based on competition. And what you see may not be what I see depending on industry, audience, copy, imagery and a long list of factors.

Using the RIGHT Data

Something else to consider is that advertisers are often distracted by the wrong data. Don’t be one of those advertisers.

For example, your Click Through Rate on the sidebar may be awful. It probably is. But it’s generally much, much cheaper to reach users in the sidebar, too.

In the end, the only metric that truly matters is your Cost Per Desired Action. Everything else can cloud the picture and lead you down the wrong path.

Back in November…

I’ve heard more times than I can count that you shouldn’t advertise on Facebook’s sidebar. In fact, I’d estimate that 9 of 10 advertisers I talk to completely ignore it in favor of the News Feed.

But I’ve found that the sidebar works just fine. In fact, back in November I reported seeing that the sidebar was more effective than mobile for getting page likes, registrations and even sales.

As with everything, things change. It’s why I suggest that you constantly monitor your results to optimize based on what is working and what isn’t.

My Data

I decided to pull all of my results dating back to August of 2013. The reason I selected this starting point is based on sample size. I started investing close to a minimum of $2,000 per month starting in August.

I ran two reports to get a better handle on costs based on placement. Know that you can run a similar report using your Facebook ad reports.

I first broke down the impressions and spend for all advertising by placement. On average, I spent the following per month:

• Desktop News Feed: $1,010.45
• Mobile News Feed: $714.22
• Desktop Sidebar: $169.26

Since competition will differ wildly depending on placement (I previously found that the cost to reach users on the sidebar was 1/20 of Desktop News Feed and 1/50 of mobile), I then found the CPM for each placement.

Next, I focused only on my page like ads run during these months to track the cost of a page like by placement over time.

Following is my average ad spend for page likes per month, by placement:

• Desktop News Feed: $385.03
• Mobile News Feed: $367.35
• Desktop Sidebar: $92.72

As you can see, I spend about 45% of my ad budget on building a relevant audience.

My Advertising Habits

It’s first important to note that I use Optimized CPM almost exclusively.

If you aren’t familiar with oCPM, this is Facebook’s default bidding method. Facebook will optimize your audience, showing your ad to the people most likely to perform your desired action. Your bid is also dynamic, as Facebook will bid what is necessary to reach that audience (budget and audience size being important factors).

As a result of using oCPM, my CPM prices will always be significantly higher than advertisers who use manual bids. But I’m confident I also get the corresponding “optimized” results (see my study on using oCPM over CPM).

Additionally, I promote to audiences of varying sizes, from a few thousand to several million. The size of the audience will also impact the cost to reach those people.

Facebook CPM by Placement

You’ll recall that back in November, I saw a huge difference in CPM depending on placement. Let’s see how that has evolved since August…

As you can see, I saw CPM drop steadily across all placements from August through December, but it then rose quite a bit beginning in January.

Previously, the cost to reach users on mobile devices was significantly higher than the cost to reach them in the desktop News Feed. Beginning in December (January being the exception) that is no longer the case for me. CPM for desktop and mobile News Feed is now nearly identical.

Average CPM for my ads by placement from August through December of 2013 was as follows:

• Desktop News Feed: $2.14
• Mobile News Feed: $4.36
• Desktop Sidebar: $.08

Average CPM for my ads by placement in 2014 is currently as follows:

• Desktop News Feed: $6.72
• Mobile News Feed: $7.49
• Desktop Sidebar: $.15

Note that CPM doubled for the sidebar, tripled for desktop News Feed, and nearly doubled for mobile. There are many explanations for this, and we shouldn’t apply a global rule.

I considered not reporting dollar figures at all since the ratio is actually most important. If I begin focusing on a smaller audience — or raise my budget for the same audience — oCPM prices are bound to increase. So I encourage you to focus more on the ratios.

It now costs me 45 times more to reach users in the desktop News Feed and 50 times more to reach users on mobile than the sidebar. In other words, the ratio remained steady for mobile News Feed vs. sidebar, but price of desktop News Feed has increased.

My Theory: Fewer users are accessing Facebook via desktop than ever before. Meanwhile, advertisers continue to favor this real estate. As a result of increased competition, prices are increasing for the desktop News Feed.

Cost Per Page Like by Placement

It’s costing me quite a bit more to reach users now than it was at the end of 2013. So how is this impacting my Cost Per Page Like?

As you can see in the chart above, the cost of Page Likes has remained steady or even dropped on mobile; desktop News Feed is constantly evolving; and the sidebar is at an unacceptably high rate.

Here’s a breakdown of the average Cost Per Page Like by month from August through December of 2013:

• Desktop News Feed: $.40
• Mobile News Feed: $.49
• Desktop Sidebar: $.40

Desktop was most efficient for me, whether in the News Feed or sidebar.

Now let’s look at January through April of 2014:

• Desktop News Feed: $.55
• Mobile News Feed: $.44
• Desktop Sidebar: $.46

The median cost is a bit misleading given the way costs are trending for me. I’m currently seeing a cost of $.80 per Page Like in the sidebar, which I cannot continue to spend. Mobile is currently easily my most affordable placement.

Let’s also keep in mind that my ads may simply be less effective now. We can’t ignore that possibility. I continue to refresh them monthly. But generally, I’m seeing an increase in costs for Page Likes on desktop (especially for sidebar) while mobile is now more attractive.

Overall, while CPM has doubled or tripled for my ads, the cost for Page Likes has increased but not at that rate. Facebook advertising appears to be getting more competitive, but oCPM is optimized to the point where the increasing CPM costs are not resulting in huge increases in costs for Page Likes.

Reminder: A Word of Caution

Once again, these results are based on my advertising only. This is not meant to be a universal report on how Facebook advertising costs are trending.

This is what I am seeing based on the audience I target, the creative and copy I’m using and the budgets I set.

The bottom line here is that the landscape is constantly changing. Monitor your results to determine the best possible placement for your ads.

Your Turn

What results are you seeing? Is mobile becoming more affordable for you, too?

Let me know in the comments below!

The post Know Your Facebook Ad Rates: CPM and Cost Per Page Like by Placement appeared first on Jon Loomer Digital.

[AUDIO VERSION: I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

First, let me be very careful about the information I’m about to share. It will be easy for some to see this as either a loophole that can be exploited to reach the News Feed or a hard and fast rule about when you should post.

This is neither.

What I’m sharing here is merely anecdotal based on what I’ve seen recently in my results. It’s a sign that Facebook may have tweaked something. Or it could be an unexplained phenomenon limited to my page.

But I’ve heard enough from other page managers that I think there’s something to it. And regardless, there are reasons why posting in this way should be successful.

[Tweet “Might you reach more people on Facebook if you post during non-peak hours? Check this out…”]

The “Best Time to Post” Myth

For years, we’ve seen scores of reports proclaiming the best time to post. It could be at 8am on Monday, for example. But such reports were flawed in so many ways.

First, they were based on a mash-up of data from different brands and users from around the world. What is good for me is not necessarily good for you. And time zone never seemed to be accounted for in these results.

But that was the obvious weakness in proclaiming a “best time to post.” There’s another issue that most have failed to understand.

These reports labeled a time “the best” because that’s when the most users were online. But in reality, this also creates the most possible competition at those times.

If you post at 8am when the most people are online, that also gives your post the most possible competition. Facebook will then need to filter out more noise to show the posts that people will care about most.

The result: You were bound to reach a lower percentage of people who were online at that time.

While I do tend to use the “most fans online” time as a starting point for posting, it’s looking more and more like that’s not particularly effective.

When My Fans Are Online

As you can see from this graph, my number of fans online is highest between about 6am and 3pm my time. There are an average of 17,518 fans online at that time.

The least activity occurs between 10pm and 3am, when there are an average of 12,261 fans online (or 70% of peak activity).

You’d expect that my results would follow accordingly. You’d expect my posts at peak-times to receive more reach and engagement than my posts at non-peak times.

But lately, that’s far from the case.

Peak Time Performance

While I don’t obsess over Reach, this metric is the best starting point since I can separate paid from unpaid performance using Organic Reach. I can’t see, for example, how many people engaged when seeing my post organically vs. when it was an ad.

So let’s take a look at the Organic Reach of my posts during peak times from March 12 through April 8 (27 total posts). Again, this is when there are an average of 12,261 fans online.

I split this up according to post type since we know this can make a difference.

As you can see here, links (8,574 average) and status updates (8,629) are reaching more people than photos (6,900) during peak times (6am – 3pm).

Non-Peak Time Performance

Note that I have a strategy that includes sharing evergreen links late at night. Therefore, I’ve only shared links during non-peak times.

I shared just as many posts in this study during peak times as non-peak times (27). Notice I’m reaching more people with links during non-peak times (12,963 average) vs. peak times (8,574).

Other Times Performance

Up until now, we’ve looked at the times between 6am-3pm and 10pm-3am. But what about the “other” times?

There were 35 of these posts, and links (11,982 average) outreached photos (7,890) and status updates (8,738)

Comparison of Organic Reach

First, let’s compare Organic Reach by time period, side-by-side.

As a reminder, I only used links during non-peak times.

As you can see above, posts published during the peak times were consistently outperformed by posts published during non-peak or “other” times. This is despite the fact that only about 70% of the fans were online during the non-peak times.

When I take a look at my top 10 posts by Organic Reach during this period, only one (#8) was posted during peak times. The post that received the most distribution was shared at 1:20am and reached 33,344 people.

Remember: These late-night posts aren’t breaking news or shares of new blog posts. They are all re-shares of older, evergreen content.

An Important Point on Organic Reach

Now, I’d be foolish not to point out that Facebook has changed the way they report Organic Reach. In my mind, it’s now underreported when you promote a post.

If you reach a user both organically and paid, Facebook only counts that person paid. Because of that, the Organic Reach numbers for any post that was promoted are a bit misleading. And since I promote most new blog posts that are shared during peak times, this is important to point out.

So let’s also take a look at actions…

Comparison of Actions

Now, we all know that actions are more important than Reach. So I wanted to see how website clicks stacked up during these times whenever I shared a link.

The important thing here is that I need to label any post that received promotion since that significantly impacts the numbers. So let’s take a look at the top 10 performing link posts by website clicks…

[Side Note: I’d consider the performance of my top performing post as evidence that my ad targeting is very good. It received the most link clicks even though it reached about 17,000 people — a number that is far less than some of the other posts on this list.]

While the top performing post was shared during peak times, most of those people (10,904) were reached with an ad. Only one more of these posts was shared during peak times, and that one generated the eighth most link clicks, though it didn’t receive promotion.

I also find it interesting that neither of the two posts shared during peak times were published during the highest activity period (earlier morning) during those peak hours.

Conclusions

It’s important to note that we’re dealing with relatively small sample sizes here. But it would be foolish to ignore the potential trend.

One would expect that posts published when 30% fewer fans are online would not only reach fewer people but receive fewer desired actions. I am certainly not seeing that right now.

A big reason for this is because I have an international audience. As a result, I don’t have the huge disparity of fans online during peak vs. non-peak times that many brands will have. So undoubtedly, that international audience is helping me.

But that isn’t the only explanation here since this still ignores the fact that I have fewer fans online during those times. In my opinion, the explanation is simple:

Good Content + Less Competition = High Performance

The typical Facebook user will have about 1500 potential stories on a given day. Facebook filters that down to about 300. Clearly, Facebook needs to do more filtering during peak times due to the competition level.

If you post at times when you have fans online but there is less noise in the News Feed, the probability you’ll be filtered out decreases.

But there’s an added benefit here: Story Bumping.

I’m hearing more and more from my friends on the east coast that they’re seeing my late-night posts at the tops of their News Feeds when they get up in the morning.

An easy explanation for this is that posts already have momentum because they were shown during low-competition times and received high engagement, so Facebook surfaced those posts in favor of other, newer posts published during peak times.

Or so I think. It’s difficult — even impossible — to know for sure. But this is certainly something worth monitoring.

Do my results mean you should start posting only at 2am? No. This is exactly what I want to avoid. If everyone suddenly starts posting at 2am, competition increases and the benefits disappear.

It means you should look at the composition of your fan base and experiment. Always experiment!

Have you seen similar results? Let me know in the comments below!

The post Facebook Content Strategy: Is it Better to Post at Non-Peak Times? appeared first on Jon Loomer Digital.

First of all, let me be clear about something: I really don’t care much about Facebook Reach. Our obsession with it is all wrong.

I care about actions. And a high Reach doesn’t necessarily mean high engagement, link clicks, registrations or sales. Just as getting those actions doesn’t necessarily mean high Reach.

Additionally, when it comes to Reach I care more about how many people are being reached in a given day, week or month — the single post isn’t that important.

But since people care so much about the Reach metric, I want to be sure you are at least measuring it properly. So the purpose of this post is to step you through a process to uncover “true” percentage of fans reached with a single post that you’ve likely never tried before.

[Tweet “Here is how to measure the TRUE percentage of fans you reached with a single Facebook post…”]

The Problem With How You’re Measuring Reach Percentage

First of all, the typical marketer incorrectly assumes that Organic Reach or even Total Reach is the equivalent of Fan Reach. Not true.

Total Reach is the total number of people you reached — fans and non-fans, paid and unpaid.

Organic Reach is the total number of people you reached without advertising — fans and non-fans.

So when you take a ratio of Total or Organic Reach over total fans and say that’s the percentage reached? Completely wrong.

But even if you do know how many fans you reached, chances are that your methodology is still a complete mess.

Let’s say you have 50,000 fans and you published a post that reached 5,000 of them. While that does indeed mean you reached 10% of your fans, it misses a very important point.

The problem here is that we’re so hung up on Facebook’s News Feed filtering that the common refrain is that Facebook kept 90% of our fans from seeing that post. That’s incredibly far from the truth.

A few more than half of your fans will be on Facebook every day. And of those on, they’ll only be there during certain times.

So while you may have only reached 10% of your fans with a post, you shouldn’t have been expected to reach a big chunk of them.

The trick will be to find how many you should have been expected to reach.

The Solution

In a perfect world, you’d be able to compare the number of fans reached to the total number of fans who were online and could have seen the post. While there are holes in what I’m going to tell you, it’s far more accurate than current methods.

I am going to show you how to find the following:

1. Total Number of Fans Reached With a Post
2. Total Number of Fans Online When it Was Posted
3. Percentage of Fans Reached Who Could Be Reached

This method won’t consider fans who have hidden all of your content. It won’t consider fans who saw or could have seen your post more than an hour after it was published. It’s not perfect. But it will provide a much clearer picture into your true percentage of fans reached.

1. Download Post and Page Level Exports

Go into your web Insights and click the “Export Data” button.

Then pick a time period that ends no more recently than a week ago and starts about five months prior to that. I’m using 9/14/2013 – 3/14/2014.

You’ll want to use the “New” export, and separately download both the Post and Page Level versions.

2. Find the Total Number of Fans Reached Metric

This sounds simple, but few marketers know how to find this.

It’s not the Total Reach — that includes fans and non-fans, paid and organic. It’s not Organic Reach — that includes both fans and non-fans.

Within the Post Level Export, go to Column T of the Key Metrics tab (the default view).

If you occasionally promote posts like I do, you now have two options:

1. Eliminate all posts that were promoted
2. Subtract fans reached via ads (Column V) from total fans reached

Since I promote a decent number of posts, I’m going to do the second. While this will eliminate all fans who were reached both organically and paid, that’s actually the way Facebook measures Organic Reach now anyway.

I then clean up my document to include the following columns:

• Permalink
• Type
• Posted
• Total Reach
• Organic Reach
• Fan Reach
• Paid Fan Reach
• Organic Fan Reach

It’s up to you how much of this information you want to keep. But I feel it will be helpful when understanding why one post did better than another.

3. Focus On Specific Posts

You’re going to see why momentarily, but let’s focus only on posts that were published within five minutes of the top of the hour. So you could use anything published between 8:55 and 9:05, for example.

The result for me is a sample size of 78 posts. Following is the breakdown by +/- 5 minutes on the hour (my local time):

• 0:00 – 4
• 1:00 – 3
• 7:00 – 3
• 8:00 – 7
• 9:00 – 5
• 10:00 – 5
• 11:00 – 6
• 12:00 – 9
• 13:00 – 4
• 14:00 – 2
• 15:00 – 3
• 16:00 – 3
• 17:00 – 2
• 18:00 – 1
• 19:00 – 5
• 20:00 – 1
• 21:00 – 5
• 22:00 – 6
• 23:00 – 4

4. Find the Total Fans Online During Those Times

The reason we’re focusing on +/- five minutes from the top of the hour is because Facebook provides data on how many of your fans were online during that specific hour on that specific day within the export.

If you posted at 7:30, there’s less confidence that a high percentage of your fans who were on during the “7:00” hour could have seen your post since some would have come on before you published.

So the key is to document all of those who were on Facebook within an hour after you published because the expectation is that if they could have seen it they would have.

Now, you probably know that there is a “When Your Fans Are Online” section of the web Insights. This is nice, but it’s for a “recent 1-week period.” The export provides exactly how many fans were on for the specific day and hour in question.

Within your Page Level Export, go to the final tab called “Daily Liked and Online.”

Here you’ll see how many of your fans were online to see any post from any source during each particular hour on each day.

If you are slick with Excel, you can run some formulas to pull this info and put it into your spreadsheet. Otherwise, you’ll need to add it manually.

5. Find the Percentage of Possible Fans Reached

Now, simply divide the Organic Fan Reach by the Total Fans Online for that particular hour on that day, and you’ll get a more accurate percentage of potential fans reached.

My Results

There are many different directions I can go with this. Let’s break down the highlights…

Highest Percentage of Possible Fans Reached: 78.7%

I created this text update on October 26 at 7:05pm that organically reached 4,978 of a possible 6,326 fans online:

What’s crazy is that it didn’t receive all that much engagement.

I had three other posts that reached at least 70% of my possible online fans. Two were text updates and one was a link. Two were in October and one the day after Christmas.

Lowest Percentage of Possible Fans Reached: 3.5%

This photo shared on November 26 at 11:55am reached a miserable 361 of a possible 10,374 fans online:

Well, the problem with that is we know this isn’t accurate. For the longest time, Facebook has misreported reach of cover photo updates.

Second Lowest Percentage of Possible Fans Reached: 6.0%

Since the cover photo share numbers aren’t accurate, let’s go to the next post. This is a link share on December 23 at 8:05am:

Note that this post did receive advertising, which would have negatively impacted the number of fans reached organically. But it’s also two days before Christmas, which may not have helped.

Average Percentage of Possible Fans Reached: 27.4%

Frankly, my results are across the board, as you can see. But on average, I can expect to reach a little more than a quarter of the fans who are currently online.

Percentage of Possible Fans Reached by Month

Take this for what it’s worth since we’re looking at small sample sizes. But here you go (number of posts in parentheses)…

October (17) – 44.1%
November (11) – 29.6%
December (15) – 23.5%
January (19) – 23.8%
February (8) – 20.8%
March (8) – 26.3%

Percentage of Possible Fans Reached by Post Type

The results here shouldn’t be a surprise based on what we know. But keep in mind that text updates aren’t getting the same advantages as they once did (number of posts in parentheses)…

Link (46) – 23.5%
Text Update (21) – 40.5%
Photo (11) – 31.4%

Percentage of Possible Fans Reached by Paid vs. Non-Paid

The results heavily favor organic reach of fans when not paying (number of posts in parentheses)…

Paid (11) – 19.2%
Non-Paid (67) – 28.9%

It’s easy to distort what this means. Some will say it means you shouldn’t promote posts. Stop…

Remember again that Facebook has changed the meaning of Organic Reach. If you reach a fan both organically and paid, Facebook now only counts the paid event. Therefore, the Organic Reach is underreported, and these two are likely much closer.

Percentage of Possible Fans Reached by Hour

We’re going to have serious sample size issues, but I present this data anyway…

0 (4) – 31.5%
1 (3) – 20.7%
7 (3) – 20.5%
8 (8) – 25.7%
9 (5) – 14.8%
10 (5) – 29.9%
11 (6) – 23.3%
12 (8) – 24.6%
13 (4) – 24.8%
14 (2) – 15.9%
15 (3) – 21.9%
16 (3) – 26.3%
17 (2) – 43.5%
18 (1) – 28.2%
19 (5) – 33.1%
20 (1) – 18.2%
21 (5) – 43.1%
22 (6) – 55.4%
23 (4) – 33.7%

It doesn’t surprise me a whole lot that I reach a high percentage of fans late at night. Less competition.

But also keep in mind that I rarely (never) promote those posts. So they are at an advantage due to the way Facebook reports Organic Reach.

Conclusions

I won’t say I have black and white conclusions. But I enjoy digging into this data.

Since the information used here has imperfections, we can’t say for sure just how many fans were kept from seeing my posts. But this does provide a closer idea of just how many were being reached and the circumstances surrounding that.

While I may be reaching an average of 9.3% of my total fans with a single post, the truth is that I’m reaching closer to about 27% of those I have the chance of reaching.

How much of that is because fans have hidden my content? How much of that is due to Facebook’s News Feed filtering? It’s tough to say, but I at least have a better sense of the answer now than I did prior to this study.

Your Turn

Go ahead and try this yourself. What percentage of your possible fans are you reaching with a single post?

Let me know in the comments below!

The post Experiment: What Percentage of Facebook Fans REALLY Saw Your Post? appeared first on Jon Loomer Digital.

[AUDIO VERSION: As an experiment, I also recorded an audio version of this blog post. Click below to listen. Let me know if this is something you find helpful!]

Back in December, many (but not all) brands began seeing a drop in Organic Reach. The immediate reaction was that Facebook was squeezing brands to force them to pay for advertising.

To test this theory, I ran an experiment of my own News Feed covering a 24 hour period. What I found then was that 28.4% of the content I saw in my News Feed came from 38 different brands who didn’t pay a penny to reach me.

More than three months have passed since then. Have things changed? Is the composition of my News Feed any different?

What I found was quite surprising. The composition of my News Feed has indeed changed, but not the way you’d expect.

The Experiment

The methods of my experiment three months ago and today were nearly identical. I did the following…

• Sorted my News Feed by “Most Recent”
• Documented the source of every story in my News Feed covering a 24-hour period
• If a post came from a friend, I marked it as “Friend”
• If a post came (organically) from a brand, I marked it as “Page” and noted the name of the page
• If a post was an ad, I marked it as “Ad”

I decided to dig a little bit deeper this time, however, and I also noted any time a brand was given free advertising by my friends via commenting, liking, sharing and checking in. I marked these posts as “Friends – Page I Don’t Like.”

Results: The First Experiment

First, let’s recap what I found three months ago…

• 373 Total Stories
• 239 Stories from Friends (64.1%)
• 106 Organic Stories from Pages (28.4%)
• 25 Ads (6.7%)
• 3 Stories from Lists (0.8%)

Keep in mind that for some time now, Facebook has shown — on average — about 300 of a possible 1,500 or so stories to the typical user on an average day. I was seeing a bit more than that.

Of the 106 organic page posts I saw, several brands reached me multiple times. In fact, there were three brands that reached me 10 times or more during that 24-hour period.

I concluded that my News Feed did not reflect what was being reported. If brands were getting squeezed — if they were forced to pay to reach me — I’d expect to see fewer organic page posts. But I feel 28.4% is a very healthy percentage.

And if brands could only reach me by paying, I expected to see more ads. But 6.7% seemed to be a reasonable percentage.

Results: The Second Experiment

Three months later, did anything change? You bet it did…

• 484 Total Stories
• 245 Total Stories from Friends (50.6%)
• 227 Organic Stories from Pages (46.9%)
• 10 Ads (2.1%)
• 2 Stories from Groups (0.4%)

Three months ago, I noted that I was seeing 24.3% more stories in my News Feed than the typical user (Facebook said to expect around 300). Now, I’m seeing 61.3% more than the typical user and 29.8% more than I saw three months ago.

But of course, that’s not the big news here. The biggest news is the quantity of organic page posts I am seeing. Nearly half (46.9%) of my News Feed is composed of organic posts from brands. And those 227 posts came from 74 different pages.

Reminder: I saw 106 organic posts from 38 different pages three months ago.

I rarely like pages. I can promise you that the number of pages I like now is virtually unchanged from three months ago. The 374 pages I like represent an accumulation over my seven years on Facebook.

Now, something I didn’t measure three months ago was how my friends were also helping share brand messages. The addition of this data makes the prevalence of organic brand-related content all-the-more startling.

Of the 245 stories I saw from friends, 49 (20%) promoted a brand in some way by checking in (17), sharing a page photo (10), liking a page (8), sharing a page link (6), commenting on a page post (4) or liking a page post (4). Let me reiterate: None of these were ads.

So we could take this a step further. Of the 484 total stories I saw, 276 (57.0%) were organic posts that promoted brands in some way. These are startling numbers.

Oh, and you know how the common refrain right now is that brands can only reach you by paying for ads, right? Well, only 2.1% of my News Feed stories (down from 6.7% three months ago) were ads.

A Few Notes on Weaknesses

First, the obvious: This is an extremely small sample size. I am only one user. What I see does not reflect what you see.

Second, I can’t be sure about how much I’m not seeing. However, I do have evidence that I see a very high percentage of page content. As mentioned earlier, I only like 374 pages in all, and many of these are now inactive. I did a quick check of all movies I like, and the only one that posted during the past 24 hours showed up in my News Feed.

To suggest that 20% of the pages I’ve liked over several years were active during a single 24-hour period is actually quite reasonable.

Third, maybe I like a lot of pages that are favored by Facebook. The brands who showed up most frequently were big brands who posted a lot like Bleacher Report (19), The Onion (19), Mashable (16) and Tech Crunch (12). These brands certainly weren’t being kept from my News Feed, but they are also considered “news” pages that may get some favoritism.

However, that doesn’t explain why I also saw stories from much smaller brands like these (number of likes in parentheses):

• Alyssa Griffeth Real Estate (72)
• golfcolorado9holes.com (89)
• Osgood Team – Rocky Mountain Real Estate Advisors (213)
• BeManaged (250)
• Moody Eyes (303)
• Ad Club Denver (831)
• Rely Local Douglas County (1,112)
• Brew Crew Ball (1,362)
• Christopher S. Penn (1,372)
• Naked Specs (1,416)
• Powers Collectibles LLC (1,614)
• Webonize: Online Marketing for Small Business (1,837)
• Parker Colorado Community Blog (2,365)
• My Kids’ Adventures (2,542)
• Roto Arcade (4,077)
• Mike Gingerich .com (4,271)
• Carla Neggers (6,953)
• Share 4 Kids Foundation (8,551)
• The Nonprofit Facebook Guy (9,762)
• AdEspresso (11,411)
• Grandma Mary (11,692)
• Beth Kanter (14,693)
• Inbound Zombie (17,001)
• Econsultancy (24,498)

That’s 24 pages that I had eyeballed as ones that would have smaller audiences. There could have been more.

Finally, results are fluid. Maybe something crazy happened during the 24-hour period I analyzed. Maybe there was a bug in filtering. Maybe brands posted far more frequently than they do typically, which slanted my results.

Maybe.

What Does It All Mean?

Now, I’m not going to make any grand proclamations about how my own results are proof that something definitely is or isn’t happening. But I have a theory regarding why I see what I see.

I won’t argue whether Organic Reach is down. That’s not debatable for most pages. What could be up for debate is the accuracy of that data since we repeatedly see bugs and inconsistencies with Reach data.

However, I know that many brands have seen a drop in measurable actions as well. While I’m not convinced Reach means much of anything, a drop in actions is certainly convincing that something is going on.

And yet, I’m seeing a TON of organic content from brands. Why?

Facebook can’t harm user experience and be successful. So it’s in their best interests to allow users to see the brand content they want to see. And Facebook knows which content that is through user actions.

So I believe that we’re still seeing just as many brand posts as we’ve always seen. Maybe even more, if you take my latest results seriously. For this to happen while so many brands have seen a drop in Reach, however, Facebook needs to be concentrating my News Feed with content I engage with most.

That doesn’t mean that the system is perfect. Some good brands may be getting harmed in the process. But some brands are seeing no change in — or even improved — Reach since December. And based on my latest experiment, this is not surprising.

That’s a theory. It’s still difficult to explain the doubling of organic brand content I saw in this latest experiment, but we can also go back to the “small sample size” explanation in that case.

Bottom line is that the results are interesting. They don’t reflect the overwhelming sentiment from brands that we’re approaching the Organic Reach Apocalypse.

Lesson: Post Frequently

This is the one lesson that appears clear from my results: If you want to reach more people, post frequently.

Of the 74 big and small brands who reached my News Feed during a 24-hour period, 41 posted multiple times. In fact, 29 posted three times or more.

I’ve said it repeatedly, but I’ll say it again: Stop obsessing over the reach of a single post. Reach more people by posting multiple times per day. Measure your reach over a given day or week rather than on a post-by-post basis.

Understand that even though 74 brands reached me, I wasn’t on Facebook for 24 hours. I only saw those posts because I ran this experiment. I didn’t notice the vast majority of those posts.

That’s not Facebook’s fault. That’s understanding that users aren’t on Facebook 24 hours per day. Post throughout the day to give yourself more chances to reach your audience!

Your Turn

I encourage you to run a similar experiment. What are you seeing? And how do you explain the results?

Let me know in the comments below!

The post A Second Test: Are Brands Organically Reaching the Facebook News Feed? appeared first on Jon Loomer Digital.

During the past couple of months, I’ve had several people tell me of a trick they found to increase Facebook Reach using multiple images in a post. I don’t chase Reach, so I found the concept somewhat interesting, but I was skeptical.

The messages I’ve received regarding this have skyrocketed of late, so I figured it was time to pay more attention to it. So with this post I plan to do the following:

• Explain “the trick”
• Provide some success stories
• Share my test and the results
• Some explanations for the results
• My recommendations

Let’s dive in!

[Tweet “Do multi-image Facebook posts lead to far more reach and engagement? Here’s a test…”]

“The Trick”

The rumor goes that if you create a post with multiple images in it, you will reach far more users than if you do a typical image share. Note that this isn’t sharing a photo album, but doing a standard text share while adding images.

Let me show you how that’s done…

In the example above, you can see that this is done within the “Status” area of the publisher. Type your message and then click the camera icon to add multiple images from your desktop.

The result might look a little something like this…

The key is for the images to look presentable when uploaded together. If you post two or three images, all will be presented side-by-side within the News Feed, on your Timeline and within the permalink.

Here’s how it might look when you share three square images in the News Feed…

And here is sharing four…

Success Stories

One of the first people who told me about this was Patrick Cuttica of SocialKaty. Patrick provided a couple of examples:

• Home decor brand page with 5k-10k fans saw 262% increase over average Reach of five prior single image posts
• E-Commerce apparel brand page in 20k-40k fan range saw 280% increase in average organic reach over five prior single page posts

Reach is fine (actually, I really don’t care), but Patrick highlighted a couple of more important points: The decor page saw a 989 % increase in post clicks while the apparel page saw 870%. In each case, this happened even though fewer stories were generated.

Here are a few more success stories people shared with me…

From Michelle Goulevitch:

If you post 2 images instead of 3 its a better look in the news feed. Not only is reach up on these types of posts, but my engagement is up too (yay!).

From Dennis Meador:

Yes I post 3-4 pics at a time and get 2-3 times the reach even with same likes/comments.

From Kati Heffield:

Top post (Tomahawks) 2 picture post- 1 Comment, 17 likes, 1 Share (1,107 Reach)
Middle post (Philpott Fact #5) single image post-8 comments, 1 share, 5 Likes (433 Reach)
Bottom post (Philpott Fact #4) single image post- 3 Likes (226 Reach)

Conventional FB logic says that the middle post should get much higher reach because of all the comments. But the Top post and the multiple images definitely shows that your rumor seems to be correct!

From Jose Mathias:

Have seen that actually, with a page of 4,200+ likes. Multiple images reach like 3000 while text 2000 and links around 900-1000.

From Bridget Cleary:

We’ve found the same, by posting multiple images the reach seems to have improved

From Claire Chesneau:

Yes, funnily enough I posted multiple images the other day (taken by someone else) and the reach seemed pleasantly enthusiastic. Just thought it was a one off……

My Test and Results

Okay, very convincing. But I’m always skeptical of any “tricks” to get more Reach. Word of such things spread quickly, but people often focus on the results that they want to find. And any “trick” that focuses on Reach isn’t all that interesting to me.

But the talk of an increase in engagement got my attention. So I decided to give it a whirl.

First, I created this post on my Facebook page on a Saturday afternoon…

The post did very well. It received 37 comments, 86 likes and 11 shares as of writing this blog post. The Reach was at least double what I’ve seen for a single image post lately. But most impressively, it accumulated nearly 2,000 consumptions (post clicks).

While you might guess most of these would be photo views, they were not. Only 66 were photo views while 1,861 were “other clicks.”

It’s tough to take much from this. While the post did do very well, it’s difficult to determine how much of that was due to the method of sharing and how much due to the subject matter. It got a ton of engagement, but how much of that was due to an increase in Reach? And how much of the increase in Reach was due to the added engagement?

Also, my example only scratches the surface because I used test images. This was intentional, however, since I was looking to get to the core of whether posting method mattered — I didn’t want the images themselves to influence the results.

But this test got people excited and a flurry of engagement resulted. So I can’t really take much away from this test.

Explanations for the Results

Still, I’m convinced people are seeing results. So the question is, why?

One theory is that Facebook classifies such a post as a text update, thereby giving them the typical Reach of such a post. Well, I’m getting conflicting info on that.

Within web Insights, that does appear to be the case…

The icon you see in the “Type” column is for text updates. Here’s an example of a photo…

But within the post level export, I get another story…

It’s possible that Facebook is still treating it as a text update, however, and that their systems are confused. It’s certainly a theory to consider.

There was also the possibility that Facebook was miscounting as a result of showing multiple images within the same post. For example, Facebook may have been counting the same person as a unique user when they saw the post and when they saw each individual photo.

I decided to test that with this narrowly targeted post…

Only my wife and I saw the post, confirmed also in the post level export. We both clicked into the photos multiple times as well, and that didn’t impact the reporting (also a strange tidbit: Facebook didn’t report our photo views).

There’s also the possibility that this is all very normal and natural. One photo can get a lot of engagement. Photos often get the most clicks. You add another photo (or more), and it just makes sense that such posts would receive more engagement.

The main thing with such posts is that you’re adding up the engagement of each individual photo as well as the post itself. When done appropriately, it makes a whole lot of sense that you could get a ton of engagement. And if you get a ton of engagement, the Reach should follow naturally.

Based on the reports I’ve heard from others, there’s a very real possibility that such posts are receiving more Reach than you’d expect from photo posts. What isn’t entirely clear is whether this is unnatural. Are you somehow “gaming” the system to get Reach and engagement Facebook is not intending?

My gut says no. But more testing is needed.

My Recommendations

Let me be straight with you: I hate topics like this one. I hate when someone finds a new trick to game the system, and then everyone and their moms start doing it, too.

All in the name of Reach.

You should look at this first in terms of utility: Do you think that sharing multiple images in this way will provide value? Is it something you think your fans will respond to?

That’s what I’m most curious about. And as I see how they are displayed, I think it’s entirely possible that this could be a very effective way to share content with my audience.

Quick Tip: In my test, I only used square images that were 1200×1200 pixels. Facebook appeared to crop out the outer 5px or so, but kept each image square.

I plan on experimenting with it. I recommend you do the same. But when you do, make sure you look beyond the metric of Reach. Does it lead to more engagement? More stories? More website traffic? More sales?

When you report back to me, please focus on these things.

How About You?

Have you experimented with this technique? What results are you seeing?

Let me know in the comments below!

The post Do Multi-Image Facebook Posts Lead to Increased Reach and Engagement? appeared first on Jon Loomer Digital.

If you read the pages on this website, you know what to expect. Fact and data based information, (mostly) unaffected by emotion.

Since my primary goal is accuracy, I don’t try to be first when there’s breaking news. And I’ll hold off publishing a post until I have all of the facts.

Luckily I took this path recently because I was on the verge of writing a post that would have reported that Total Reach is over reported. And that would have simply added fuel to the fire for anyone upset about a drop in Reach.

But I took my time. I ran a controlled test. And what I discovered was a pleasant surprise.

Facebook Organic Reach is under reported — specifically when you promote posts.

Sure, that’s not necessarily “good” news. We want Facebook to be accurate. And as we’ll see soon, what appears to be inaccurate data may actually be by design. But it’s always nicer to hear that stats are better than you initially thought.

This post is going to break down the following:

• What I initially found that sparked concern
• The possible explanations
• The controlled test
• The results

[Tweet “Facebook is under reporting your Organic Reach — at least when you advertise.”]

What I Found

You may know that I’ve been putting together the lessons for my Insights training course. I had completed the definitions of all important terms, an overview of web Insights and was giving a tour of the new post level export.

In the definitions section, I covered Organic Reach, Paid Reach and Total Reach. I talked about how Total Reach will never equal Organic Reach + Paid Reach because you’re bound to reach people both organically and with your ad.

When I got to the post level export section, I revisited this. I used my data as an example to show how this was the case.

There was one, big problem: In the example I was going to use, Total Reach equalled Organic Reach + Paid Reach.

I looked at more posts within that export. Same thing. Then I exported five months of data.

That export included 390 posts. Of those 390 posts, 71 had been promoted. Guess what? In every single case, Total Reach equalled Organic Reach + Paid Reach.

Keep in mind that this was using the new export. The old export wouldn’t be relevant because of Viral Reach (which is now folded into Organic Reach).

The same problems I spotted in the new post level export were found in web Insights. Here is an example…

This is a post that I first published organically and then promoted only to fans. So in that case, you know there will be overlap between Organic and Paid Reach. But the sum of the two equals Total Reach.

I was immediately alarmed by this because I know it’s not the way things are supposed to work. In fact, I was able to find this entry in the Facebook help center titled, “Why does the sum of paid reach and organic reach differ from the total reach?”

Total reach counts the number of unique people who saw your posts, regardless of where they saw it. If your post reaches a person organically and through an ad, that person will count as one for organic reach, one for paid reach and one for total reach.

That entry is three months old at time of this blog post.

Exploring the Possible Explanations

This was a bit alarming. I could think of only three potential explanations:

1. Users were only being targeted organically or via an ad, not both
2. Total Reach was being over reported
3. Organic Reach was being under reported

I immediately eliminated the first option since I know for a fact that users can both be reached organically and see an ad.

Let’s explain a bit more what #2 and #3 mean…

Over Reporting Total Reach
Let’s say that you create a post that reaches 1,000 people organically. You then promote it, mainly to fans, and reach 1,000 people with that ad. We’ll say that 300 of the people reached with the ad were also reached organically.

If Facebook is over reporting Total Reach, they would say that your ad reached 2,000 people. In reality, it would have reached 1,700 people, or 300 of those people twice. In this case, Facebook would be over reporting Total Reach by 17.6%.

Under Reporting Organic Reach
We’ll stick with the same example as above. This time, though, once your ad reaches those 300 people who already saw it organically, Facebook changes those people to Paid Reach only. So you now reach 700 people organically, 1,000 people paid and 1,700 total.

In this case, Total Reach would have been correct, but Facebook was under reporting Organic Reach by 30%.

The more I thought about it, the more I was convinced: Facebook was over reporting the Total Reach of posts when also promoted.

It just seemed most likely. But I knew I was dealing with a very sensitive situation. People are going absolutely nuts regarding the recent drop in Reach. If I wrote that Facebook was over reporting Total Reach — all while people were upset it was too low — things were going to blow up.

But I also think that Reach is a seriously overvalued metric, and I don’t want to be the center of such a firestorm. That is, unless I could verify my theory to be true.

A controlled test was necessary.

The Controlled Test

I held off on the blog post. I knew that it was something I could test and prove (or at least provide convincing evidence for) one way or the other.

Following is what I did…

First, I created an organic post that was very tightly targeted. The expectation was that only my wife would see it (and I, as the admin, would also be able to see it).

Since post targeting only applies to the News Feed, I immediately hid it from my timeline. You may have seen early tests where I forgot this important step.

I then went to my wife’s computer and checked her Facebook News Feed to see the post.

As you can see in the image above, only two people saw this post. This screen grab was taken four hours after it was published.

It was safe at this point to promote it. So I then went into Power Editor and created a Custom Audience for my wife only. I promoted that post, making sure that she would be the only person seeing the ad.

When this test completes, the following should be true:

• Organic Reach = 2 (My wife and me)
• Paid Reach = 1 (My wife only)
• Total Reach = 2 (My wife and me)

Here are the results I was looking for…

1. Organic Reach (2) + Paid Reach (1) = Total Reach (3). If this happened, Facebook was over reporting Total Reach.

2. Organic Reach (1) + Paid Reach (1) = Total Reach (2). If this happened, Facebook was under reporting Organic Reach.

The Results

Here is a screen grab of that post, 13 hours after it was published and eight hours after it was promoted…

As you can see, after the promotion, Facebook has lowered this post’s Organic Reach from 2 to 1. Total Reach is correct at 2 and Paid Reach is correct at 1.

I went into the new post level export and confirmed the same data. Organic Reach, which had been at 2 within the export prior to promotion, fell to 1.

So, this confirmed that Facebook wasn’t over reporting Total Reach. They were instead under reporting Organic Reach.

Now, however, I needed to figure out why. Or at least the source.

I found a clue within the old post level export.

As you can see, the numbers are precisely as they should be in the old export.

What Happened?

Based on these results and the three month old help center entry, it’s quite clear that Facebook has changed the way they handle Organic Reach.

We already knew that Organic Reach changed. This was as a result of Viral Reach going away, and being folded into Organic Reach.

However, Facebook’s decision not to count Organic Reach for a user once they are reached with an ad is completely new. And this change was applied across web Insights as well as the post level export.

When I asked about this within the Developer forum, the response I received — as difficult as it was for me to understand — was that this was by design.

Why is it by design? I really have no idea.

If it actually is by design, I guess you could say that Organic Reach isn’t under reported at all. It is what it’s supposed to be.

But I counter that with the help center entry. No warning was given to this change. And there’s no good explanation I can think of for why this change makes any logical sense.

So until I hear otherwise, I will continue to refer to Organic Reach as under reported when a post has been promoted.

Now What?

At this point, it’s still not clear if this is a bug or intentional. I did receive one response that indicates it’s intentional, but that’s not an open and shut case. I still think there’s a good chance it’s a bug.

Some will try to connect an under reporting of Organic Reach with Facebook trying to get you to advertise. Stop. That’s not what this is.

If Facebook wanted to intentionally deflate Organic Reach to get you to advertise, they would do so on posts that weren’t promoted. This only impacts posts that have already been promoted.

Rest assured that when you promote posts, your Organic Reach is actually higher than you think it is. Your true Organic Reach will be a mystery since you won’t know how many of those you reached with your ad were also reached organically.

In the end, my guess is that a small percentage of marketers look all that closely at Organic Reach anyway. The focus is likely on Total Reach. And that metric remains accurate.

For me, this is just one more example of why Reach isn’t a metric you need to worry about. Facebook can change the way they report it. They can change it again tomorrow and make you think you reached double the people. It just doesn’t matter.

Focus on the metrics that lead to your business goals. Those could include likes, comments, shares, link clicks, sales and other conversions.

Don’t waste your time with Reach.

Your Turn

What do you think about this revelation? Let me know in the comments below!

The post Controlled Test Results: Facebook Organic Reach is Under Reported appeared first on Jon Loomer Digital.

A phrase I repeat regularly when it comes to succeeding with Facebook ads is “Never assume anything.” If you assume you know, you don’t. As a result, you may either waste money unknowingly or miss an opportunity.

This is why I recommend starting most campaigns broadly in terms of targeting and placement. If you assume mobile is going to be most cost effective and sidebar won’t work, for example, you may be surprised by the actual results.

Since I’m always second guessing, experimenting and trying new things, I recently decided to test a long-held advertising habit when targeting Fans only.

[Tweet “Should you use CPM or oCPM bidding when targeting Facebook Fans with promoted posts?”]

My Typical Bidding Behavior

First of all, a very quick overview of your bidding options when creating Facebook ads:

Cost Per Click (CPC): The maximum you’ll pay per click on your ad. Since distribution is based on an auction format, the amount you ultimately pay per click will depend upon the competition.

Cost Per 1,000 Impressions (CPM): The maximum you’ll pay per 1,000 impressions of your ad. Once again, the amount you pay will depend upon competition for the same audience and placement.

Optimized Cost Per 1,000 Impressions (oCPM): Facebook optimizes your ad by showing it to the people most likely to perform your desired action (within your target). Additionally, bidding is automated. Your bid will change dynamically based on competition, assuring that you’ll reach your desired audience.

Since oCPM is “optimized,” the final CPM price is typically much higher than CPC or regular CPM. But — also because it’s optimized — I’ve found it’s almost always most efficient.

While I used to split test CPM vs. oCPM in particular, I rarely do anymore. I’ve simply found repeatedly that oCPM gives me the best Cost Per Desired Action (Page Like, Link Click, Conversion, etc.).

This includes targeting Fans, whether it be promoting a post or selling a product.

Second Guessing My Advertising Habits

However, I started wondering recently if using oCPM made sense when targeting my Fans only. Why, for example, do I need Facebook to optimize my audience? By reaching my Fans, I’m already reaching a naturally optimized group of people.

And if CPM costs less than oCPM (typically significantly less), might I save money by going with it instead? Or is oCPM so effective that the higher price per impression doesn’t matter?

This is what I wanted to find out. So I ran a test…

CPM vs. oCPM for Fans Test: Link Clicks

I promoted four different posts last week. In each case, I targeted Fans and email subscribers (who aren’t current Fans) only in the News Feed (desktop and mobile). I optimized for Link Clicks for each one.

These are organic posts that I simply promoted to reach more of my relevant audience in an effort to increase website traffic. Here are the four posts:

I promoted each for a very short period of time because I prefer to only have posts promoted while I don’t have another new blog post shared in my Fans’ News Feeds. They all ran from late morning when the post was originally shared to 6:00am the following morning.

In each case, I created separate campaigns for CPM and oCPM, with two different ads in each (one targeted at Fans and one at newsletter subscribers who aren’t Fans). Each campaign had a lifetime budget of $5, and I set a maximum CPM bid of $2.50 (reminder: oCPM is set dynamically).

To summarize, here are the campaigns that were created:

• Reach Rant – Fans and Subscribers – oCPM
• Reach Rant – Fans and Subscribers – CPM
• Increase Reach – Fans and Subscribers – oCPM
• Increase Reach – Fans and Subscribers – CPM
• News Feed Test – Fans and Subscribers – oCPM
• News Feed Test – Fans and Subscribers – CPM
• Facebook Offer ROI – Fans and Subscribers – oCPM
• Facebook Offer ROI – Fans and Subscribers – CPM

Since the impact of the ads targeted at the newsletter subscribers was so minimal (it was a small group and Facebook preferred targeting Fans), I will lump that ad in from this point forward and focus only on the campaign results.

CPM vs. oCPM: The Results

Here’s a comparison of results on a campaign vs. campaign basis:

A couple of things should immediately jump out at you:

1. The cost per 1,000 impressions is significantly higher (3-4X) when using oCPM
2. The budget was never reached (not even close) when using CPM

Now let’s take a look at the overall results, lumping all similar campaigns together to compare CPM vs. oCPM:

Okay, now let’s start breaking this down:

• Cost Per 1,000 Impressions nearly 4X higher for oCPM
• oCPM reached full budget, while CPM reached only 35% of it
• Cost Per Link Click nearly the same
• oCPM resulted in more than 3X the Link Clicks

Also, here’s another important stat that I haven’t covered yet: Total Impressions…

• CPM: 5,124
• oCPM: 3,817

Even though the ads are being shown to 74% of the audience, oCPM is resulting in three times the link clicks.

So what we find here is that while the Cost Per Link Click is nearly the same, oCPM brings more results. In order for this to happen, oCPM must actually be optimized — Facebook must be showing my ads to Fans most likely to click on a link in order to counter the significantly higher Cost Per 1,000 Impressions.

A Note on Bidding

Since I didn’t come close to reaching my budget using CPM, it’s important to note my maximum bid of $2.50. I could not bid higher than that due to my $5 daily budget.

The budget could have held the campaigns back some. However, keep in mind that the overall Cost Per 1,000 Impressions for my CPM campaigns was $1.37. Since I didn’t get particularly close to that $2.50 maximum, I question whether it would have made much of a difference.

That said, I have to recognize it as a potential limitation. I plan to try this again with a $10 budget and $5 maximum bid (if not $20 and $10).

In Conclusion

This study reaffirms my faith in oCPM. While using the CPM bidding method may seem like the most cost effective method on paper, oCPM is so well optimized that I still end up getting better results.

Now, keep in mind that these results are for my Page only. It’s a small sample size. While I trust the results for me, you should always test to see what works for you.

How about you? Do you tend to use CPM or oCPM when you target Fans only?

Let me know your approach in the comments below!

The post An Experiment: CPM or oCPM When Targeting Facebook Fans With Ads? appeared first on Jon Loomer Digital.

Facebook recently reported in a sales PDF that organic Reach is going to drop due to competition in the News Feed. Many marketers were upset by this, claiming they were once again being forced to pay for ads — led by an inflammatory post by AdAge.

I have plenty to say on that topic. But I want to be as scientific in my response as possible. And it can’t all be said in a single blog post.

As a result, I’m going to start slowly. Today’s goal is to dig deeper than the typical reactionary blogger and marketer when it comes to the quality and composition of the News Feed.

Back in August, Facebook shed light on how they determine what you see in your News Feed via their filtering algorithm (also known as EdgeRank). Facebook’s goal, they said, was to show you more of what you care most about and less of what you don’t.

Of course, brands in particular balk at this. We hate that we don’t reach 100% — or 20, 30, 40 or 50%. So instead of testing this, many brands straight up assume they are being screwed.

Now that reports indicate that organic Reach will drop further due to increased competition in the News Feed, I figured this was a good time to take a closer look at what is shown to me.

As a result, I am putting Facebook’s claim that they show me the content I care most about to the test. In particular regarding content shared by brand Pages.

[Tweet “Does Facebook show you what you want to see from brands in the News Feed? Here’s an experiment…”]

The Experiment

Facebook said that the typical user would receive approximately 1,500 stories per day from friends and Pages if the News Feed were unfiltered. Instead, users are shown only about 300.

[NOTE: Keep in mind this includes a TON of light-weight stories. Friend commented on this post, liked that post. Friend checked in here, played an app there. Trust me, you don’t miss the majority of those 1200 stories!]

So the first thing I did was go to my News Feed and open a spreadsheet. I then went story by story and documented the source of those stories covering the past 24 hours.

If the source of that story was a friend or a public figure I follow, I simply classified that as “Friend.”

If the source of that story was an organic post from a brand Page that I like, I wrote down the specific brand that it came from.

And if the source of that story was an ad — regardless of whether I otherwise Like the Page associated with that ad — I classified it as “Ad.” Note that this would include any Sponsored Stories that mentioned my friends.

A few things I was looking for…

• Are organic posts from Pages being squeezed out?
• Are ads taking over the News Feed?
• Are the posts I’m seeing from brands what I actually want to see?
• Is the number of stories being displayed consistent with Facebook’s claims?

Let’s find out…

The Breakdown of Stories

I actually received quite a few more stories than the 300 Facebook says to expect — 373 in all. Here is how those posts are broken down:

• 239 Stories from Friends (64.1%)
• 106 Organic Stories from Pages (28.4%)
• 25 Ads (6.7%)
• 3 Stories from Lists (0.8%)

I don’t know about you, but this seems like a fair breakdown. Not too many ads. Organic stories from brand Pages aren’t being drowned out. Lots of stories from friends.

The Breakdown of Page Stories

Now, here is where it gets interesting…

Those 106 Page stories came from a grand total of 38 unique brand Pages, for an average of 2.8 per Page. Most marketers seem to assume that only some of our posts are shown. But really, based on what I’m seeing, it appears I see almost everything that I care about most.

When I looked through that list of 38 Pages, these were definitely all brands I care about most (or have interacted with recently). And what’s funny is that I rarely — or almost never — comment on brand posts. But Facebook “somehow” knows what I like.

Take a look at what I saw from the brands represented the most:

• Mashable – 16
• TechCrunch – 15
• The Onion – 10
• Green Bay Packers – 7
• Post Planner – 5
• AllFacebook.com – 4
• NFL – 4

I almost never comment on content shared by these brands (Post Planner being the one exception). But I do read comments. And I do click on links. This was enough to tell Facebook that I would like to see a combined 31 link shares from Mashable and TechCrunch covering a 24 hour period.

Answering the Questions

Okay, so now let’s get back to some of the questions I was hoping to answer…

Q: Are organic posts from Pages being squeezed out?

It sure doesn’t seem that way. More than a quarter of the stories I saw in my News Feed covering a 24-hour period were organically from brands. I certainly don’t want 50% or more of the stories in my News Feeds to be commercial.

Q: Are ads taking over the News Feed?

Not at all. Only 6.7% of the stories I saw were ads.

Q: Are the posts I’m seeing from brands what I actually want to see?

Absolutely. I saw 31 posts total from Mashable and TechCrunch. As I look through the list of brands represented, there is a good reason for all of them to be there.

Q: Is the number of stories being displayed consistent with Facebook’s claims?

Yes. In fact, I saw 24% more stories than Facebook said I should expect.

Closing Thoughts

First of all, it’s quite clear to me that Facebook is showing me the content I want to see, particularly from brands. Or to put it more accurately, what I end up seeing is engaging content — I don’t know what I couldn’t see. Looking at that list, I have no complaints or arguments.

Second, what I see here conflicts with the biggest complaints from brands in a couple of ways…

One claim is that brands can’t enter the News Feed without paying for ads. This is clearly not the case since 28.4% of the stories I documented in this study were organic posts from brands.

Another claim is that Facebook is preventing brands from reaching people who actually want to see their content. Well, that’s certainly not the case for me when it comes to Mashable, TechCrunch and others. I’ve shown Facebook through my actions that I like their content, and as a result I see a ton of it.

This doesn’t mean that brands don’t have an argument. I understand the feeling of being shortchanged when you pay for ads to increase Likes but then can’t reach those users in the News Feed.

But you know what? That falls on us. The 38 brands that reached me during this 24 hour period have provided compelling content that inspires me to interact with them in some way. As a result, I see their content regularly.

Sure, filtering content certainly does fatten Facebook’s pocketbooks by making it more necessary to advertise. But — separating my roles as a user from that of a brand for a moment — I fully believe that it also improves my experience consuming content on Facebook.

The filtering isn’t perfect. Maybe Facebook should expand the number of stories highlighted in News Feed per day to 500. Maybe users should be given an unfiltered option.

But what I’m not seeing here is filtering done for the sole purpose of screwing brands, at the expense of user experience. If you want to reach more Fans organically, you need to compete with the mounds of content that users could see every day. To be “preferred,” you need to do one of two things: 1) Be awesome or 2) Pay to reach them.

Or you could do both and get even greater results!

What do you think? Are you seeing what you want to see in your News Feed? Let me know your thoughts in the comments below!

The post An Experiment: Testing Facebook’s News Feed Filtering Algorithm appeared first on Jon Loomer Digital.

We’ve been hearing for quite some time now about the effectiveness of running Facebook ads on mobile devices or in the desktop News Feed. But the sidebar? “Completely worthless!” they shout.

But is it true?

It’s conventional wisdom that it is. And maybe it’s true when applied to the general population. But sometimes you need to double check conventional wisdom to make sure it applies to you.

That’s why I decided to take a closer look. And what I found is likely to surprise you.

[Tweet “Think Facebook’s sidebar ads are a waste of your money? This may shock you…”]

First, Some Perspective

It’s always important to point this out from the start. We’re talking about the results for only one Page during a three month period. Sample size, industry, copy, landing pages, products and a whole lot more factor into these results.

I have decided to focus on all campaigns that I ran with the intention of one of the following things:

• Conversion (Checkout)
• Conversion (Registration)
• Page Like

I eliminated any Promoted Post that was created for the purpose of engagement, but still managed to receive one of these actions.

The four main countries I target in my ads are the United States, United Kingdom, Australia and Canada. As a result, cost per action will always be higher.

I know. You can get ridiculously low Cost Per Like. But I’m focusing on quality over quantity here. As a result, costs are higher (but you’ll see results follow).

My total ad spend in this case study is $4,212.30. It is broken down as follows:

• Desktop News Feed: $2,473.92
• Mobile: $1,194.38
• Sidebar: $544.00

Shortly, you are going to see why I believe I should be dedicating far more of my ad spend to the sidebar.

How I Did This

If you aren’t using the new Facebook ad reports, you need to do it now. This is how I’m able to break down what my Cost Per Desired Action is by placement.

No, I didn’t create a different ad for each placement. Facebook breaks all of this down for me, which is incredibly valuable. It now allows me to optimize and spend my money more efficiently based on the results.

Cost Per 1,000 Impressions by Placement

I took all of the ads run during the past 90 days and found the average CPM based on placement.

It is as follows:

• Desktop News Feed: $1.64
• Mobile: $3.95
• Sidebar: $.08

Keep in mind that this is an average covering all targeting. So when I target Fans only, for example, these will be significantly higher. But I also target large pools of non-Fans to bring the average price down.

You probably aren’t surprised that mobile CPM is the highest here, and it really isn’t close. I’ve actually seen sidebar CPM below $.05 in many cases.

Let’s think about what this means. While Mobile or Desktop News Feed may (not confirmed yet) result in more desired actions per impression, the significantly higher CPM means that you are going to pay 20X or more for each impression. So in the end, the sidebar could be most efficient.

And I have a feeling that’s what we’re going to see!

In order to analyze the results for checkouts and registrations, of course, I’ve been using Conversion Tracking. I hope you are, too!

Cost Per Checkout by Placement

Following is a chart showing the Cost Per Conversion of my campaigns that were run for the sole purpose of selling during the past 90 days. I spent a total of $520.29 on these ads.

Here are the actual costs per conversion:

• Desktop News Feed: $9.37
• Mobile: $24.39
• Sidebar: $5.78

You’ve probably heard my story about getting a 35X ROI during the first month of promotion of my Power Editor training course. Facebook ads are pretty darn powerful!

Of course, I haven’t been able to maintain that ROI. But keep in mind that the following CPA numbers are going to be for products that were sold at either $73.50 or $147.

In most cases, I’ve stopped ads that go to mobile devices. While a CPA of $24.39 isn’t a negative ROI by any stretch of the imagination, it’s nowhere near as efficient as desktop.

But sidebar is most efficient? I did not see this coming!

Cost Per Registration by Placement

I’ve also been running ads with the goal of driving registrations to my weekly webinar. I have spent $576.69 on these ads.

Here are the actual costs per registration by placement:

• Desktop News Feed: $.76
• Mobile News Feed: $1.41
• Desktop Sidebar: $.53

Once again, mobile is far more expensive than the other two placements, and the sidebar ends up being most efficient. Unfortunately, I was only putting $25.83 of my budget towards the sidebar (that will change!).

Page Likes

I am constantly running campaigns to increase Page Likes. I have spent $1,788.33 on such ads.

Here are the actual costs per Page Like:

• Desktop News Feed: $.34
• Mobile News Feed: $.53
• Desktop Sidebar: $.38

Mobile isn’t a complete waste this time, but these ads were still most expensive. The desktop News Feed ends up being the slight winner when it comes to generating Page Likes.

Once again, we’ve been led to believe that running Page Like ads on mobile devices is extremely effective. It’s not as effective as desktop for me.

The bulk of my spend here has been going towards Desktop News Feed. I may cut off mobile entirely going forward.

What Are You Seeing?

Clearly, I’m seeing results (sales, registrations and Page Likes) from the sidebar, and at a greater rate overall than I am on either the desktop or mobile News Feeds. But this does not apply universally, and you should check your own stats.

Are you seeing similar results? Let me know in the comments below!

The post Why You Shouldn’t Forget About Facebook’s Sidebar [Research] appeared first on Jon Loomer Digital.

I obsess over the ads that I run. I check them every morning, afternoon and night. And if you pay attention, you’ll spot some pretty darned interesting things.

The Example

I created a campaign that uses a Facebook video and a text call to action with a link to a product landing page. That landing page focuses on three different pricing options.

[Tweet “If you aren’t using Facebook’s Conversion Tracking, you may be throwing your money away.”]

The Surface Stats

Here are the final six columns of stats for the ads of a campaign within Ads Manager.

This shows the stats that the majority of marketers will focus on. So let’s break this down…

Clicks: You want clicks, so you’ll look at this. If this is how you measure success, here is how we break down the performance of these ads:

• Ad 5: 773
• Ad 1: 110
• Ad 2: 100
• Ad 3: 43
• Ad 6: 14
• Ad 4: 4
• Ad 7: 4

Of course, there’s no context to those clicks. But Ad 5 is pumping out a ton!

Click Through Rate: This will help us better understand the percentage of people clicking.

• Ad 1: 1.243%
• Ad 5: 1.073%
• Ad 6: .639%
• Ad 7: .163%
• Ad 4: .162%
• Ad 2: .132%
• Ad 3: .104%

Average CPM: Some marketers spend far too much time looking at this one. They think that a higher CPM means a waste of money. There’s quite a disparity here regarding average CPM (in reverse order from cheapest to most expensive):

• Ad 2: $.09
• Ad 3: $.14
• Ad 5: $.26
• Ad 4: $.26
• Ad 7: $.30
• Ad 6: $.56
• Ad 1: $3.59

Cost Per Click (Manually Determined): While Facebook doesn’t show this in all cases, a smart marketer will calculate it manually based on total spend over number of clicks. So let’s see how these ads stack up:

• Ad 5: $.02
• Ad 2: $.07
• Ad 6: $.09
• Ad 3: $.14
• Ad 4: $.16
• Ad 7: $.18
• Ad 1: $.29

The Assumptions

If I hadn’t used Conversion Tracking, I would assume that Ad 5 was phenomenal. In fact, there would be no reason to run any other ad. I was getting a ridiculous $.02 per click and that ad got me more than 7X the total clicks of any other ad.

Ad 1, while it got me the second most clicks, also was easily the most expensive per click. It’s also the ad that was eating up most of my budget (nearly half). Some evidence here to stop Ad 1 based on surface stats.

Otherwise, Ad 2 is the one ad I may consider keeping alive. It brought me a $.07 CPC while netting 100 total clicks.

These are assumptions. But when you assume…

The Conversions

I actually did use Conversion Tracking for these ads. In fact, it was set up so that Conversion Value is returned based on any of the three packages a customer may have purchased.

Value of those packages: $19, $29 or $79.

So here are the results when we include Conversions and Conversion Value:

• Ad 1: 13 Conversions, $287 Conversion Value ($31.81 Spend)
• Ad 2: 0 Conversions, $0 Conversion Value ($6.59 Spend)
• Ad 3: 2 Conversions, $48 Conversion Value ($5.85 Spend)
• Ad 4: 0 Conversions, $0 Conversion Value ($.65 Spend)
• Ad 5: 0 Conversions, $0 Conversion Value ($18.41 Spend)
• Ad 6: 0 Conversions, $0 Conversion Value ($1.23 Spend)
• Ad 7: 0 Conversions, $0 Conversion Value ($.73 Spend)

Let’s backtrack…

We had assumed that the top performing ad would be Ad 5 based on a ridiculous $.02 CPC. It also led to 773 clicks, so we thought this would lead to a bunch of conversions. Ad 2 was the other ad we’d consider keeping alive.

Meanwhile, it looked like Ad 1 was a complete waste of money.

But…

Ad 1 was the ad that brought the most conversions and value. Easily. We’re talking a 9X ROI on that ad. Ad 3, another ad we would have stopped, was bringing a 8X ROI.

The ads we thought were performing? No ROI. Nothing. Potentially throwing money away, though an argument can be made for small sample size for Ad 2.

Use Conversion Tracking!

What did we learn here?

We learned that it’s very easy to get distracted by the wrong stats.

We learned that a high Optimized CPM may just be a sign that the ad is highly optimized (which is what it’s supposed to be!).

We learned that if you don’t use Conversion Tracking, you’re going to have a hard time determining which ad is actually leading to revenue. As a result, it’s very easy to make the wrong decisions when managing your ads.

So use Conversion Tracking! It’s really not that hard to do. Go here to learn more about how you can set up Conversion Tracking on your site and for your ads.

The post This is Why You Must Use Facebook Conversion Tracking [EXAMPLE] appeared first on Jon Loomer Digital.

[The following is a guest post by Emeric Ernoult of AgoraPulse.]

This morning I received another email from a client who doesn’t want her Facebook contest app to require app installation and permissions from her participants. She believes this will reduce her conversion rate.

I’m not alone in this situation. Our friends at Woobox face the same questions over and over again

I explained to her that the app installation requirement was, by far, the best option to create value for her Facebook Page and offer the best possible experience to her fans. Last May, I published an entire blog post on this very subject.

Just to give you a little recap, here are the 5 main reasons why a Facebook App install requirement is the best way to go for your campaign:

1. It’s the only way to offer a social context within your app. Examples include comparing your quiz score with your friends’, seeing what your friends have voted for in a fan vote contest, or checking your friends’ entries in a photo contest. Without an app install, none of this is possible. There will be no fun social context in your app. Your participants’ experience will be okay at best, boring at worst, but never entertaining or fun.
2. It’s the best way to reward fans for inviting their friends. More about Facebook Contests and virality in this post.
3. It is the best way to reduce cheating and ensure you are recruiting genuine fans. Trust my experience with thousands of Facebook contests, cheating IS a serious issue.
4. App install is the only way to include a fan gate on mobile devices. In some instances, mobile users can make up one third of the overall number of a campaign’s participants. That’s an opportunity you don’t want to miss! You cannot have a like gate for a Facebook contest (i.e. force participants to like your page in order to enter) unless your participants have installed your app first (asking them to install the app is the only way to know whether they have already liked your page or not).
5. App install is the only way to leverage each participant’s data for the long term, and build a valuable database of fans and potential ambassadors to leverage later. I am a big proponent of Facebook CRM. There’s little long-term value to any campaign without it. If you don’t require the installation of a Facebook app, you won’t be able to track each participant’s activity throughout all of your campaigns and on your timeline. The data you will collect will be essentially useless. Are you really ready to invest a lot of time and money into your next Facebook campaign to just throw away all the data you’ll collect?

Offering a social context is one of Facebook’s unique advantages. In a Facebook Quiz, the social context offered by the app install will allow each participant to see how she is stacking up against her friends. This type of entertaining context would not be possible if she had not installed a Facebook app.

All right, enough of the pros and cons. Let’s get to the hard facts. At the end of the day, what’s really on your mind is whether or not requiring your Facebook contest participants to install your app will damage your conversion rate, right?

So, let’s look at the conversion rates for 6 of our most popular apps over thousands of campaigns run by thousands of different brands from September 1st, 2012 until August 1st, 2013.

Here’s the executive summary:

In order to be more transparent, I’m copying screenshots of the Facebook insights for each of these applications below.

In a nutshell, the average conversion rate is around 70%. The lowest average conversion rates belong to apps where there is usually nothing to win (Fan Vote). So, when it’s just for fun and there’s no benefit attached to giving away their profile information, fans will be more reluctant to do so. Duly noted.

The variations between campaigns can be very significant. The best get conversion rates close to 100%, whereas the worst can get as low as 30%.

On average, over an 11 month period, 78% of our Facebook quiz participants have installed the app. Median conversion being between 70 and 85%.

On average, over an 11 month period, 71% of our Facebook Personality Test participants have installed the app. Median conversion being between 60 and 80%.

On average, over an 11 month period, 71% of our Facebook instant win participants have installed the app. Median conversion being between 65 and 80%.

On average, over an 11 month period, 70% of our Facebook photo contest participants have installed the app. Median conversion being between 60 and 75%.

On average, over an 11 month period, 74% of our Facebook sweepstakes participants have installed the app. Median conversion being between 70 and 80%.

On average, over an 11 month period, 58% of our Facebook fan vote participants have installed the app. Median conversion being between 55 and 70%. This is the lowest score among all of our benchmarked apps, but this is also the only app for which there are generally no prizes offered.

First Takeaway

There can be a HUGE difference in terms of conversion rate from one campaign to another. So, what are the criteria separating the great campaigns from the not-so-great? The answer is simple, and can be summarized in two words: Trust and Reward.

The more your participants trust you, the more likely they are to give away their information. The more attractive your reward, the more likely they are to enter your campaign. Attractive doesn’t necessarily mean expensive. You only need to ensure they’ll be considered highly valuable by your audience.

The trust factor is a little trickier and is made of several different components:

• Your brand’s intrinsic credibility,
• Your campaign’s credibility, and
• Your transparency on how your participants’ data will be used

There’s not much you can do for your brand’s credibility in the short term. If you’re already a well-known and trusted brand, people will be more likely to trust you with their data. If you’re not well known and trusted yet, you’ll need to do well in the following two criteria.

Your campaign’s credibility will address one legitimate question on your participants’ minds, especially if you are not a globally trusted brand: Are these guys serious, or are they just trying to get my email address and Facebook credentials so they can spam me for the next 5 years?

Looking serious is the first thing you need to concern yourself with. Don’t be too cheap on your campaign’s design. If your visuals look like they’ve been created by some kid for $5 on Fiverr, it’ll be hard for your campaign to inspire trust. Good looking visuals should not cost you more than $100 if you find the right freelancer. This is a small investment that will make a big difference.

Concerning transparency, it’s pretty simple: Draft clear (and well written) rules, feature the winners of your previous campaigns and comply with all applicable laws. This should all go without saying, but surprisingly, many Facebook contests and sweepstakes don’t.

Your brand’s credibility, your campaign’s transparency and the attractiveness of your prizes will have a HUGE impact on the conversion rate. A well-known and trusted brand will achieve higher conversion rates, lesser known brands will have to work harder. Trips to the Caribbean will attract and convert more users than a bunch of coffee mugs. Makes sense.

Second Takeaway

An average conversion of 70 to 80% is not bad at all. If you look at Facebook’s own internal data, they consider 80% to be very high. In fact, only the best apps have conversion rates in the 80% range according to Facebook (Foursquare and songpop are cited as examples).

So, on average, your humble sweepstakes may get the same conversion rate as a giant brand like Foursquare. Not bad.

Third Takeaway

According to Hubspot, Sweepstakes overall entry conversion rates are typically between 20 and 40%.

When you see a 70 or 80% conversion for your Facebook sweepstakes or contest app, you may think, “I’m losing 20 to 30% of my potential participants!” Look at the numbers and think twice!

No sweepstakes or contests run on the web have a 100% conversion rate. According to our own internal data, simply asking for a first name and email address can drop your conversion rate by 20%. And each additional field you add (date of birth, Address, phone number, etc.) will add to the loss of conversion. A form with too many fields may make your conversion go as low as 30 to 35%.

On the other hand, once you’ve asked for a Facebook app install, the conversion rate on a simple form with less than 3 fields is between 90 and 100%. Once participants have installed a Facebook app to enter your campaign, they are very likely to fill-in a form as well, as long as it’s not unreasonably long.

The bottom line? Facebook app install conversions are generally no lower than that of a typical web form, and many times, your rate may be higher.

I’m convinced that the value behind Facebook is in building a stronger connection with fans you know. If you want to engage with your customers, you’re going to have to provide them with the best social experience Facebook can offer, and you just can’t do that without requiring an app install.

What do you think? Have your experiences with app installations and conversions been positive or negative?

The post The Truth About Facebook App Conversion Rates appeared first on Jon Loomer Digital.

It’s important to take a break every now and then to challenge conventional wisdom.

Conventional wisdom says that to optimize the effectiveness of links, you should either include them within text connected to a Photo or within a text post (Status Update).

Conventional wisdom says that the reasons for this are lower Reach and Engagement rates from the standard Link share (update with title, description and thumbnail).

We already know that Reach numbers need to be questioned. But what about Engagement?

The purpose of this post is to prove why you should go back to sharing links the way Facebook had intended. I’ll do that with a little help from my data.

Why You Share Links

We marketers are so easily distracted. We’re always looking for the latest shiny object. The newest quick fix. The weakness that can be exploited.

As a result, we have also lost track of why we share links in the first place: To drive traffic.

Sure, you want to start a conversation as well. Likes, comments and shares are good (with shares being great).

But particularly if you’re sharing your own content, the main goal when sharing a link is to drive traffic to your website.

Guess what? Facebook tracks link clicks. They track link clicks either within a typical Link Share, within a Status Update, within the text connected to Photos and within comments.

So if you wanted, you could actually compare the effectiveness of driving link clicks with each post type.

I have a feeling we’re about to do just that!

My Data

It’s always important to open with this: My data is a very small sample size. I represent only one Facebook Page (Jon Loomer Digital). It may not represent your data.

But what I found is convincing enough that I’d be surprised if you’re seeing something significantly different.

I took more than a year’s worth of data on my Page from February 2012 through February 2013. I then cleaned up the data by doing the following:

• Removed any posts that received promotion;
• Focused only on Status Updates, Link Shares and Photos;
• Removed all posts that were targeted by location or language;
• Focused only on the Photos and Status Updates that included a link within the text.

The result is a pool of 588 pieces of content from which I can compare, broken down as follows:

• Link Shares: 410
• Photo Shares: 158
• Status Updates: 20

Obviously, the Status Updates are a very small sample size. But you’ll quickly notice that plenty can be learned from those 20 updates.

Facebook provides number of Link Clicks within the Consumers and Consumptions tabs of the Post Level Insights export. I will use Consumers since the focus here is on unique users for all data.

Note that the Link Clicks that Facebook tracks include clicks on links within the comments of posts as well. While I went through my data line-by-line to only focus on the posts that had links within the main text, I can’t separate the clicks within comments of the posts included in my final data. But I will point out a couple of scenarios where there is a link shared in the comments of a post that also includes a link.

Since my number of Fans increased significantly (from 1,889 to 9,586) during this period of time, I will be focusing on ratios over number of Fans.

[NOTE: Watch the video at the bottom of this post to learn how to find this data.]

Difference in Reach

A hot topic of late has been the high Reach of Status Updates when compared to Photos and Links. Here’s an example of my Reach from May 1, 2012 through January 31, 2013 (from yesterday’s blog post):

As you can see above, the Reach of my posts in general has favored Status Updates.

The Total Reach for the data used in today’s experiment is broken down as follows (Total Reach / Total Fans):

• Status Updates: 39.44%
• Photos: 28.34%
• Links: 26.69%

It’s important to remember that the numbers above don’t represent the Total Reach of ALL Photos and Status Updates shared from my Facebook Page. It only includes those qualified posts (as explained earlier) that include a link within the text.

The percentages above show exactly why so many marketers have been using Status Updates lately to share links (meaning the link is shared in plain text without a thumbnail, title and description).

The thought is that this significantly higher Reach leads to better results.

Difference in Engagement

Of course, before the latest trend was using Status Updates to increase Reach, the hot thing was to attach links within the text of a Photo.

The thought here was that using a photo brought more attention to the eye. Additionally, such a post would take up more room in the News Feed. These things, we have been told over and over again, would lead to more Engagement (clicks within posts).

So now let’s take a look at how Engagement is broken down within my data (Engaged Users / Total Fans).

• Photos: 1.38%
• Status Updates: 1.01%
• Links: .92%

This supports everything we’ve been hearing. If you want engagement, share Photos!

WHOA! Difference in Link Clicks

Here’s the problem with this line of thinking: Focusing on Reach and general Engagement doesn’t help us measure whether we are reaching the intended goal of the post (in this case, driving traffic with the link).

So how about those link clicks?

Even the raw data is eye-opening. I sorted all of my qualifying 588 posts in order of link clicks. The top 356 were all Link shares (this is not a typo).

The first non-Link share that showed up was a Status Update with a grand total of four link clicks (for comparison’s sake, there were 58 Link Shares that had at least 50 link clicks). But guess what? That post also had a link within the comments, so it’s not clear how many clicks there actually were on the link within the Status Update.

[NOTE: I know what you’re thinking because I was thinking it, too. Is Facebook not tracking the links within the text of Photos and Status Updates, tracking only those within comments? Nope. There are several such posts that don’t have links within comments that do show link clicks. And there are also such posts that I promoted that got far more link clicks.]

Even the second Status Update on the list had a link shared in the comments. While my standard Link shares were generating an average of about 25 link clicks, the Status Updates and Photos averaged fewer than 1.0.

Let’s take a look at the average number of link clicks by post type (Link Clicks / Total Fans):

• Link Shares: .455%
• Status Updates: .006%
• Photos: .003%

I had to go to a third decimal place, otherwise Photos would be rounded to .00%. Link Shares resulted in 159 times more link clicks than Photos with attached links and 73 times more link clicks than such a Status Update.

These numbers are ridiculous. Obvious. I shouldn’t need to waste more than a few words making my argument, but I’m so shocked by the disparities that I’m going to waste those words on expressing my shock.

If I am looking to drive traffic to my website with a post, there is absolutely no reason to share that post as a Status Update or Photo. None.

Status Updates generate more than 50% the Reach than Link Shares. Photos similarly result in more than 50% the Engagement. But neither of those post types come close to satisfying the number of link clicks of a good, old fashioned Link Share.

Quite frankly, I hate myself for taking as long as I did to move entirely to Link Shares for driving traffic. I wasted countless opportunities to drive traffic to my website.

The Science

So the immediate question is Why? Why would users be so much more likely to click on the normal Link Share as opposed to a link that’s in the text of a Status Update or Photo?

When you include a link within the Status Update or Photo text, you are wanting someone to click that small area to be redirected to your site. Like this…

But a link? Click anywhere within a box that is approximately 379 x 116 pixels and you’ll be directed to my site…

Which link would you be more likely to click?

Your Turn

As convincing as my numbers are, they of course don’t necessarily mean you are seeing the same thing.

I encourage you to do your own research. Pull several Post Level Exports. Remove the promoted content. Find those Status Updates and Photos that you created to share links. Then find which posts generated the most link clicks.

What are you seeing? Do you plan on changing your posting habits when it comes to sharing links?

Watch the tutorial below to learn how you can find your link click data!

The post Stop Using Facebook Text Updates and Photos to Share Links appeared first on Jon Loomer Digital.

I’ve never been happier to read an article by TechCrunch…

Killing Rumors With Facts: No, Facebook Didn’t Decrease Page Feed Reach To Sell More Promoted Posts

Josh Constine, you’re my hero.

If you’ve been reading my blog (and of course you have been!), you know that I’ve become increasingly grumpy about this subject. I’ve felt like the old man screaming in an empty room.

It seems that everyone has made up their minds that 1) Reach is down, and 2) Facebook is forcing us to pay for ads.

My data doesn’t support it (read this or this). In fact, I’m entirely convinced that Reach means very little right now. Everyone is fixated on this number while very few people talk about metrics that matter.

Anyway, this blog post isn’t about that. It’s about two things. First, Josh Constine’s awesome mic drop article. Second, some instructions on another “silent killer of Facebook Page Reach” that you need to research.

Cause of Reach Issues: Spam Filters

Each news feed post has a drop-down arrow next to it that lets users hide it from their news feed or mark it as spam. Facebook made these controls more visible and easy to use in September. That let people who thought a Page was spammy report it to Facebook or remove it from their feed.

At the same time, Facebook updated EdgeRank to more aggressively punish spammy Pages, the way Google updates PageRank occasionally to push down the search result rank of spammy sites.

When the change was made, these spam reports went way up. But as Facebook collected this data along with information about Pages you engage with, users began seeing fewer and fewer of this spammy content. As a result, fewer spam reports were made.

Here is an image from PageLever (also from the TechCrunch article) that visualizes this initial spike and then slowdown of spam reports:

Image courtesy of PageLever and TechCrunch

So what about the non-spammy Pages, right? They benefitted. Page Reach has remained relatively constant dating back to the beginning of July. Here is another chart from PageLever and TechCrunch:

Graph courtesy of PageLever and TechCrunch

What does this mean? Once again, I’ll let Josh explain…

The amount of fans Pages were reaching has stayed relatively stable since July. However, the standard deviation of reach did shoot up. That’s because the few especially spammy Pages and those affected by an increase in news feed competition had their reach drop significantly, while the reach of Pages that almost never get spam reports got a boost. That’s the impact of Facebook’s changes to the spam reporting UI and the EdgeRank algorithm.

There’s more to Josh’s article, and I beg you to read it. Very well done.

Is Your Page Spammy?

I don’t know about you, but this was my first question. Whether or not you think your Page is spammy is irrelevant. Facebook users will tell you.

How do you figure this out? I’m glad you asked. There’s a video that walks you through this at the bottom. But here are the basic steps for finding out how many people have reported your Page for spam during the past five months…

1) Go to your Admin Panel.

2) Within the Insights widget, click See All.

3) Click Export Data.

4) Select a period of time lasting five months, leaving it on Page Level Data.

5a) Within cell A1 of the first tab (or really anywhere), type in this formula:

=SUM(‘Daily Negative Feedback User…’!F:F)

That should spit out the number of people who have reported you for spam during the past five months.

5b) Or you can run a search of the workbook for “spam_” and the first result should take you to the correct tab. You’ll know you’re on the right tab if the A2 cell reads…

Daily The number of people who have given negative feedback to your Page, by type. (Unique Users)

Then simply type the following formula anywhere within that tab, other than in the F column…

=SUM(F:F)

The number I get is 34, which is about .5% (that’s a half of one percent) of my total Fans.

Is that bad? I don’t have a point of reference. But I do know that my numbers haven’t suffered, so I am going to assume it’s acceptable.

How many people have reported your Page for spam? Are you seeing a corresponding drop in Reach and Engagement? Let me know below!

6YNBDPZECMK9

The post TechCrunch: Your Facebook Page Reach is Down Because You’re Spammy appeared first on Jon Loomer Digital.

Now that you can create Sponsored Results that appear in the Facebook search typehead, the overwhelming question is this: So, do the ads work?

According to early results reported by InsideFacebook, the clickthrough rate is spectacular. Ampush Social is seeing CTR between .5% and 1% while PageLever is reporting between .8% and 2%. These rates are higher than that of standard Facebook advertising by a factor of 10 (or more).

Sounds great, right? The problem is that the click doesn’t tell the entire story. When I tested Sponsored Results (for a mere $10), I also saw great peripheral stats. But the bottom line? Read on…

My Test

Ad #1: I targeted 18 countries outside of the US. I targeted users who weren’t already fans of Jon Loomer Digital who were searching for the following Pages:

• Facebook Pages
• Social media marketing
• Marketing
• Facebook For Small Business
• Facebook Advertising Tips
• Facebook Marketing Bootcamp UK
• Social media marketing
• The Social Media Monthly Magazine
• Social Media Examiner
• Facebook Marketing Bootcamp
• Boost Social Media
• Social Media Marketing Best Practices
• Mashable – Social Media
• HubSpot
• AllFacebook.com
• Facebook Marketing
• InsideFacebook.com

Note that some of these are well-followed Pages and some are not. In some cases, I wanted to reach people who were searching for a topic, but there was a Page under the same name.

Other than the targeting mentioned, I cast a wide net. I didn’t care about interests because I figured that if they were searching for these Pages, they were within my target audience.

This ad drove people to the landing tab for my Facebook marketing eBook. In order to access the eBook, they’d first need to Like my Page.

Ad #2: Same as Ad #1, but targeting only users in the US.

Ad #3: I targeted the same Pages and countries as in Ad #1, but this time I drove users directly to my Facebook Page. No carrot this time, just looking to get people to Like my Page.

Ad #4: Same as Ad #3, but targeted inside the US.

The Results

Ad #1 (non-US, eBook): 747 Impressions, 7 Clicks, .937% Clickthrough
Ad #2 (US, eBook): 5,847 Impressions, 16 Clicks, .274% Clickthrough
Ad #3 (non-US, Page): 732 Impressions, 7 Clicks, .956% Clickthrough
Ad #4 (US, Page): 5,682 Impressions, 19 Clicks, .334% Clickthrough

As you can see here, my numbers are consistent with what others have found. That said, my Clickthrough is far better outside of the US than inside, which is no surprise. However, I found it odd that so many more of my impressions were inside the US than outside. Also, there was no noticeable difference between my two ad variations.

But the flaw with the other reports is that they don’t go beyond the click. We know there are Facebook bots out there. We know they are click happy. Do these ads inspire action beyond the click?

For me, the answer is a resounding “No.” I won’t even show you the stats. I’ll just tell you that of the 49 clicks I received, only one resulted in an Action (a Page Like). Since all four ads were looking for the Like, I’d consider spending $9.55 for one Like (and no eBook downloads) a massive failure.

The Open Questions

Admittedly, the sample size of my test is as small as it gets. But the CTR is consistent with prior reports. It’s just not clear if my other results are also consistent.

So that leaves me with some open questions:

1) Are brands seeing that these ads are not driving Actions?
It’s possible that I am the exception here. But if brands are seeing a high Clickthrough rate and very few Actions, why? Is it a flaw with the ad unit? Is it because many of the clicks aren’t from humans? Is it because we as advertisers haven’t yet figured out how best to follow through on what we promise?

2) Are the other incredible results focused outside of the US?
We need to train ourselves to start asking this question when we see Facebook ad results that are too good to be true. There is a high concentration of bots and spam accounts outside of the US. If you want to focus on specific countries, you can get some eye-popping — though hollow — numbers.

3) Are these ads prone to bot clicks?
Yes, this is related to #2, but I’m curious about this. I realize that bot activity may be more sophisticated than I understand, but I had expected that this ad would be less prone to bot clicks due to the fact that users would first need to type a specific Page name into search. Granted, bot programmers would eventually work that into the code, but my expectation is that clicks on these ads would be somewhat clean out of the gate.

My theory is that bots will inflate clicks and they’ll inflate Likes if there is a Like link within ads. But these ads are different because there is no such link. Users are redirected and then must perform the action. That’s why my suspicion is that these click numbers are inflated by bots, and those bots drop off after that first click.

Am I wrong about this? Are these ads as susceptible to bot clicks? Maybe even more so?

4) Are my results an indication of a missed opportunity?
When I see 49 Clicks ad only one Action, I can’t help but wonder if I did something wrong. It’s certainly encouraging that the ads at least got clicks. If not, I’d be more willing to point my finger at the ad platform itself. But since it got clicks and only one Action, I wonder if the problem is me.

With only four different ads and two variations, I should split test more. There may be another angle that I need to take that would lead to success. It’s far too early for me to indict Sponsored Results in general.

What Are You Seeing?

The post Facebook Sponsored Results: More Click than Action? appeared first on Jon Loomer Digital.

Facebook Insights is a wonderful tool when used properly. But the data is so extensive (and the updates often unreliable) that many page managers ignore their Facebook Insights completely.

The purpose of this tutorial is to not only explain why you should use your Facebook Insights but to show how you can use them to determine what and when you should post on your page for maximum effectiveness.

The Data Available in Facebook Insights

Most page managers look through what is available in the web version of Facebook Insights and stop there. The truth is that the power of Facebook Insights can be found in the downloadable spreadsheets.

Granted, this data is a bit overwhelming. Everything you can possibly want is in there. Anywhere within your Facebook Insights you can export either Page Level or Post Level data to Excel or a CSV file.

There is so much information that it’s difficult to list it all here. But I’ll try to provide an overview.

The Post Level data includes the following tabs of information:

• Key Metrics
• Talking About This Per Post, by Action Type
• Stories Created Per Post, by Action Type
• Number of People Who Click Within a Post, by Type
• Number of Clicks Within Each Post, by Type
• Number of People who Give Negative Feedback Per Post, by Type
• Number of Times Negative Feedback Given Per Post, by Type

And the Page Level data includes the following tabs of information:

• Key Metrics
• Daily Like Sources
• Daily Viral Reach by Story Type
• Weekly Viral Reach by Story Type
• 28 Days Viral Reach by Story Type
• Daily Viral Impressions by Story Type
• Weekly Viral Impressions by Story Type
• 28 Days Viral Impressions by Story Type
• Daily Total Frequency Distribution
• Weekly Total Frequency Distribution
• 28 Days Total Frequency Distribution
• And a bunch more…

There are 51 more tabs. Yeah, there’s a lot of information in there. So trust me that you can waste an entire weekend looking through it.

Determine the Best Day to Post on Facebook

Determine a period of time that you want to compare and export both the Post Level and Page Level data. I used two months, but it’s up to you.

From the Post Level data, let’s stay on the Key Metrics tab and focus on the following columns (feel free to delete the rest):

• Post ID (A)
• Message (B)
• Posted (C)
• Lifetime Post Total Reach (D)

The Page Level data provides Lifetime Total Likes in Column H of Key Metrics. This will allow you to find the percentage of Total Reach over Total Likes. This is important since the number of people you reach is significantly impacted by number of fans.

Once you find the percentage of Reach vs. Likes, you can then start sorting out what days provided the highest reach.

For me, it turns out that the best day to post is Thursday (25.1% reach) and the worst day to post is Saturday (21.6%). Not a huge discrepancy, but that 5% can make a difference.

It should be noted that I posted far more often on Thursday (26) than Saturday (7) or Sunday (9). So the weekend data can be easily skewed. Otherwise, my worst days to post are Tuesday (22.6%) and Friday (23.2%).

Determine the Best Time to Post on Facebook

What’s interesting is that 25% of my posts were between the times of 7:00 and 7:59 AM. This is above the overall average of 23.7%. So it’s not necessarily a bad decision to concentrate my posts at that time.

That said, there were certainly better times to post. I experimented recently when I heard it was best to post at less busy times, particularly at night. And that is reflected here. The best two times were from 8:00-8:59 PM (27.2%) and 10:00-10:59 PM (26.3%). These results should be taken with a grain of salt since I’ve only posted twice during each time range. But something to watch.

The two next best times to post were 12:00-12:59 PM (26.1% for eight posts) and 2:00-2:59 PM (25.7% for six posts). It looks like I should otherwise avoid posting between 8:00 AM and Noon as well as between 5:00 and 8:00 PM. These are the pockets where the lowest percentages of reach are concentrated.

I wouldn’t suggest using my data as gospel, but keep in mind that all times are Mountain if you want to take anything from this.

Determine the Best Content to Post on Facebook

The types of content I shared in this analysis are as follows:

• My Own Links (68)
• Links of Others or Guest Posts of Others (27)
• Photos (3)
• Videos (7)
• Status Updates (26)

I could have gotten lazy here and stuck with Total Reach vs. Likes. But it seems we’re trying to find something different here: The content people are most likely to engage with.

You see, if we stuck with Reach the type of content wouldn’t hold much relevance. What doesn’t matter here is whether people see it. It’s that they interact with it.

And so, in place of reach I took the total number of people who “consumed” the piece of content. A consumption includes Talking About This (which is anything that creates a story) as well as any people who register a click that doesn’t count as Talking About This (link clicks, video clicks, photo view, and other clicks).

What I found is that people engaged with photos (4.0%) much more than any other type of content (0.9% for links, 0.7% for videos and 0.6% for both status updates and links of others).

There’s an enormous caveat here. As mentioned earlier, only three photos are included in this analysis. So while those three photos were extremely successful, you can’t extrapolate these results over all photos.

What Can Be Learned

But there is still plenty to learn here. I should post more photos and see what comes of it. I should also continue the experiment of posting later at night (and this is going to be part of that experiment!). I should avoid posting in the middle of the morning and evening, and I should lighten up on status updates and links of others.

That’s what I see. What can you learn from your Facebook Insights?

The post How Facebook Insights Tells What and When to Post appeared first on Jon Loomer Digital.