Proper estimation and prediction of a Lifetime Value is not an easy task. But when it comes to understanding the returns on retargeting investments, things become even more complicated.
Generally, the solution seems clear: you need to compare those who received retargeting ads with those who didn’t, and then calculate the difference between revenue from those two groups. And you had better not to forget about ad costs because, in the beginning, you’ve invested some money in order to get additional revenue.
But a lot of details will appear when somebody will decide to build the system or model for calculating the additional profit.
The most frequent question that appears is: «Can I run a test just once, make sure that retargeting works well and continue buying ads with the same approach?» And before answering yourself, the following point should be taken into consideration:
Three things influence on Incremental Effect:
Usually, users’ behavior changes slowly and mostly depend on a product (monetization, seasonality, new features) and also it depends on changes in your opponents’ products.
The traffic auction changes quickly and it’s hard to predict.
The conversion into paying users depends on creatives, the offer you make to a customer and some technical nuances such as: what type of links do you use in your ads.
So, what you can do are:
One detail not to miss here is:
This test will compare
It means, previously (before the test started) control group may saw retargeting ads. And in order to compare the behavior of the test and control group during the whole lifetime period, you need to exclude the control group from serving ads for the lifetime.
You can run AB-test on historical data. It’s the easiest way of building regular incrementality testing. There’s one hidden obstacle — you need to understand correctly, which users have received retargeting ads and which have not. So if you have full data about your retargeting ads’ exposure, then it’s like classic AB-test:
Analyzing past performance you can adjust your future strategy.
You may not have all the data about — who has seen the ads, who hasn’t. For example, Facebook or Google will not share such data on a user level. In that case, you may build ongoing testing on your end. This doesn’t sound easy, especially if your resources are really limited or you don’t have internal expertise for building such tools.
Another possible way is to use already existing solutions from Data Management Platforms. DMP will split users in real-time, but you will not be able to influence on a splitting mechanism (eg. you may need stratified sampling instead of random sampling in order to neutralize the influence of whales), because it’s already designed by DMP developer. Is it a pitfall? In most cases — no.
The main problem with the solutions described above is: if you work with more than one media source, you will not be able to translate the results on a traffic source level. Which makes it hard to optimize your campaigns or compare traffic sources.
If you went so deep in your stirring about incremental profit from retargeting, then there’s a solution here. Some DSPs that work specifically with retargeting have already build incremental testing on their end. Some classic UA DSPs are in the process of building such a feature. In that case, incrementality testing works on a source/campaign level, so you’ll be able to make decisions and maximize your incremental profit. Here again, we have one hidden obstacle — you’ll be probably not able to compare different sources because their in-built incrementality tests will have different designs. But still you can work on a campaign level and it’s good news.
As you might have noticed, all the approaches are not about predicting the future, they are about making decisions according to previous observations. The main difficulty in building a predicting model for retargeting is that you need to define the lifetime. It’s rather simple when we talk about installs:
But when it comes to retargeting you need to define the churn. And the user may have several “lives”: he/she becomes inactive -> receives retargeting ads -> becomes active again -> then may not make any purchases -> again become inactive -> then see ads again -> become active and make some purchases. There are a lot of different scenarios that you need to take into consideration in order to make any predictions. This is why at the moment we are only aware of approaches that estimate past values.