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3 Critical Ad Optimisations Using Adobe Analytics Especially During Campaign Learning Phase


Community Advisor


Ad optimisation learning phase myth by Benjienen Toledo.png

Is Learning Phase a myth? I can’t find an ad platform that had a more detailed explanation on this phenomenon than Facebook. Each time one of your ads is shown, the delivery system learns more about the best people to target, times of day to show the ad, and placements and creatives to use. The more an ad is shown, the better the delivery system becomes at optimising the ad’s performance.

Stefan Johanson succinctly explained it that when you never allow your ad sets to pass the learning phase and then start delivering the real results, you’ll always be in the learning phase.

In other words, ads are not instantly smart, it must gather enough experiences or data and insights to learn and stabilize. Fear not, because aside from the native platforms, Adobe Analytics helps you identify opportunities to optimise your campaigns.

3 Critical Ad Optimisations Using Adobe Analytics

1. Eliminate Ads with High Drop Off Rate

Each ad asset has its unique tracking codes, using this dimension, you may opt to download the data into a sheet and do Vlookup function to match accordingly. Expect that there are discrepancies between the link clicks reported from the native ad platform with the entries reported on Adobe Analytics. Despite the discrepancy, Adobe Analytics data give an indication of your ads’ relevance to the target audience.

For social media campaigns for example, you need to find out what is the website drop off rate benchmark to decide which ads to eliminate. For instance, your usual drop off rate is up to 40%, this means that your entry rate is 60% out of all social media link clicks.



In the above example, ads with tracking codes #2 & #3 were below the benchmark. You can then recommend to your social media managers to pause these ads. You may apply the same steps to your other campaigns such as Display/Banners, Video, etc. This will avoid the machine to further spend your campaign budget on those ads.

2. Pause Ads With High Bounce Rate

High bounce rate such as >50% is not always bad. But if your ads’ media optimisation objective was website landing page views and the audiences were expected to engage, act, or trigger a website event/button after they entered the website, the bounce rate should be low or within your benchmark.


Based on this example, Consideration ads themes 2 & 3 have bounce rates above your benchmark. You can recommend to your media managers or social team to pause these ads. This will avoid further serving those ads which did not compel your audiences to act as expected. It also gives an opportunity to the team to relook at the messaging and improve such as clarity on what  the audiences should do or click upon landing on your website.

3. Discontinue Ads in Locations With High Drop Off and/or High Bounce Rate

Another way to segment your Adobe Analytics data is to breakdown the traffic according to country. The principles of the first two optimisation exercises apply the only difference is the dimension used.



This data suggest that your ads may not be targeting the right locations or may not be as relevant to some countries. It may have something to do with language, targeting, messaging, or all the above. Whatever that factor is, Adobe Analytics prompt you that there are opportunities where you can help the ads serving become more intelligent.