Power BI.

Templatizing for Other Clients.

Top & & Lowest Performers.

From there, we pulled Facebook ad level efficiency information (week, Month, AND Lifetime for projects) to develop a Power BI control panel. These metrics included:.
Dates.
Project.
Ad Set (Audience).
Advertisement.
Amount spent.
Frequency.
Link clicks.
Impressions.
Conversions.
CTR.
Revealed below is the frequency vs. CTR for all timeframes and projects. Amongst the data, there is a clear curve of lessening return showing that once the frequency hits a specific point, the CTR will start to dip.

The best part of this control panel is that it can now be used across all clients, little or big, that are active on Facebook Ads. It is as simple as downloading the raw information and importing it into the Power BI file!
Dont Miss a Thing.
Our group is constantly bring out brand-new tools to envision data and solve real-world issues for our clients. Discover more about other cool analyses like this in our upcoming webinar:.

Overexposure to an ad will cause an audience to lose interest which will result in the click-through-rate dipping and for that reason triggering conversions to reduce also.

KEEP IN MIND: Bubble size reflects the number of outcomes were produced from that advertisement.

We simply developed this amazing analysis that didnt require any costly third party tool– what do we do next? Using this analysis we can do the following things:.
Because they are over the ad fatigue limit that is set by account, discover which ads to turn off/refresh.
Figure out how frequently to revitalize creatives for a specific advertisement set.
Look at imaginative fatigue for various types of creatives (static vs. animated).
Determine at what frequency do we start to see CTR drop off.
Typical CTR vs Frequency.

We analyzed the ad information at weekly, month-to-month, and life time timeframes to guarantee we saw the whole picture. From there, we determined the median CTR and frequency for each timeframe and highlighted which advertisements we would suggest pausing/refreshing.
For example, listed below is the weekly CTR vs frequency control panel for our client. We would recommend pausing/refreshing advertisements that have a little quantity of outcomes, are above the average of 2.07 frequency a week, and below the average of.31% CTR.

This post was written by: Kara Mehnert & & Rachel Danto.
As online marketers, we know the line in between reliable frequency and advertisement tiredness on Facebook is really thin. The Paid Social and Data Strategy groups here at Seer desired to establish an analysis to showcase how frequently after seeing a clients innovative does click-through-rate start to drop– without needing to count on expensive and extra 3rd-party management or imaginative analysis platforms.
Forming a Hypothesis
Ad Fatigue
Throughout among our customers campaigns, we were seeing click-through-rate dropping week over week. After doing some more digging, we formed the hypothesis that higher frequencies were starting to cause reduced CTR. Therefore, we wished to put a plan in location to ensure we refresh creative in a timely manner to avoid future issues of fatigue..
When the paid social team understood the problem at hand, we worked together with the data method team to find an option for the imaginative tiredness problems. We had a couple of questions we desired to respond to:.
What is the mean frequency by ad type, audience & & innovative
? What is the mean CTR by ad type, audience & & imaginative
? What is the average conversion volume by ad type, audience & & innovative
? The concerns noted above are important to us as marketers due to the fact that we wish to ensure we arent revealing our audience the very same ad a lot of times.

The innovative fatigue analysis can also be used to look at performance for a specific audience. For instance, for our customer we pulled the leading and worst performers based on CTR vs. frequency.
Shown listed below is the audience with the greatest frequency pulled by lifetime. This means that on average, a user saw the ad 35 times over the lifetime (8 months) and CTR was really low compared to the average of all campaigns.

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Across one of our clients projects, we were seeing click-through-rate dropping week over week. After doing some additional digging, we formed the hypothesis that greater frequencies were beginning to lead to decreased CTR. The concerns listed above are crucial to us as marketers because we desire to guarantee we arent showing our audience the exact same ad too many times.

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