The key question for the marketing manager is: How many purchases would have been made without advertising? Have I invested my budget wisely and how much have my efforts contributed to the sales growth of the product, or have I wasted time and money in addressing users who would have been aware of my offer without any action? In other words: What are my marketing measures really worth?
App marketing in particular has focused heavily on retargeting in recent years. The goal: to reactivate users who have already downloaded an app but then become inactive with certain offers and convert them to buyers. In order to determine the ROAS (Return on Advertising Spend) here, the usual measurement of conversions on the basis of last-touch attribution is not sufficient.
In order to get a complete picture, a measurement and analysis method called “incrementality testing” has recently become established in the ecosystem. In the incrementality test, users are randomly divided into a test group and a control group. The test group sees the advertising campaign, the control group does not – then the performance of the two groups is compared. In this experiment, the higher the conversion rate in the test group in relation to the control group, the more effective the marketing campaign was and the greater the so-called uplift. The method itself comes from science, for example, the effectiveness of the vaccine is checked in vaccination tests using Randomized Control Trials (RCT).
The concept itself is not new, but its application to advertising, especially in the mobile sector. When carrying out this type of experiment, the current problem is that there is no standardization or written rules – every advertiser must first answer a large number of questions for himself in order to use the measurement method successfully.
Pan Katsukis
is CEO and co-founder of Remerge, an app marketing platform. After studying media informatics in Munich, he worked for several mobile phone companies and ran the family business for a while. Katsukis has been an expert in app marketing and programmatic advertising in the Berlin start-up scene since 2008. He was a co-founder of madvertise media until he co-founded his current company in 2014. –
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Execution of the test and validation of the results
The concept may sound simple, but in practice it requires an intensive study of what is actually to be measured. For companies running these tests for the first time, the point is to find the best setup and define the users they want to reach. Is it about people who stopped opening the app a certain time ago (about X months / X days ago) or about people who are still active? How big is the test group and how big can the control group be in relation to the total target group? How long does the test have to run in order to achieve meaningful results?
In addition to segmentation, it should be noted that an advertiser often addresses users more than once in his marketing mix – he must therefore ensure that he randomizes at the user level so that the users remain in either the test or control group and results are not caused by overlapping be falsified from the outset. To evaluate the incrementality of a retargeting campaign, the control group includes all conversions and sales generated through organic installations and viral effects, but also those generated through other marketing activities, including UA campaigns, TV ads, and others offline – or out-of-home campaigns. The test group also sees the retargeting measures.
If the group allocation has been carried out properly, it must be clearly defined what a conversion is. For example, going to your home page, adding to cart, purchasing, and checking out are already different types of conversions. While everyone is part of the sales funnel, every single step could be a metric to consider. This makes it all the more important to focus on the KPIs that really need to be checked.
Why incrementality testing makes sense
The measurement carried out gives advertisers a new, holistic perspective for assessing the true value of their campaigns, and provides answers as to which campaigns were most effective and which generated more sales. The incrementality approach is therefore also a solution for the cannibalization worries of marketers, who always have doubts as to whether their retargeting campaigns could eat up their organic conversions.
But not only that: The insights gained from the test about the target group and the campaigns can represent valuable information in order to optimize the entire paid strategy. This analysis and optimization approach will be brand new right now, because in spring 2021 Apple – with a view to the data protection of its users – will actually override the tracking method via the device ID (IDFA) that is prevalent in the app business and thus also previous ones Attribution models severely limited. If the users cannot be tracked, it becomes difficult to assign the conversion to a specific channel. The attribution loses its accuracy and granular insights, so advertisers will focus their attention even more on what the actual value of their campaign is.
However, incrementality measurement using aggregated data and econometric models will still be possible. Understanding the relationship between ad spend and revenue is the only way to reduce the risk of ineffectively spent budgets in the mobile space. Attribution models do not scientifically prove causality. “Cum hoc ergo propter hoc”, or in other words, correlation does not imply causality. Incrementality testing can do just that: highlight scientifically sound causality and give mobile marketers the decisive advantage.
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