Harness AI to boost ROI and long-term growth

In a nutshell, incrementality in marketing measures the actual impact that a marketing activity has on an app’s KPIs such as installs or IAPs.

Traditional incrementality measurement involves control groups and test groups. Using this methodology, the test group is exposed to the campaign while the control group is not. By comparing the performance of both groups, marketers can determine the incremental lift generated by the campaign.

However, A/B testing can be expensive and time-consuming for marketers to perform. Plus, it’s growing increasingly difficult to implement the level of detail needed for incrementality A/B tests with privacy regulations.

Our AI-driven incrementality is powered by predictive modeling and our proprietary synthetic control groups based on industry benchmarks. Adjust clients run marketing campaigns as normal while Adjust analyzes campaign performance against similar apps, which act as the control group. The resulting data predicts what would have happened if the client had not changed a test variable (budget, channel, etc.). This method of incrementality measurement is a more future-proof methodology that requires no user-specific segmentation.

Media mix modeling is one approach to analyzing marketing efforts by studying past sales to determine what contributed to those sales. The mix refers to a combined evaluation of product, price, place, and promotion. In essence, MMM tells marketers how effective their advertising was so that they can apply their learnings to future campaigns and get a better return on investment (ROI). Invented before the likes of cookies and attribution, MMM was one of the initial ways marketers were able to take the guesswork out of which channels were most effective for user acquisition (UA). We consider it an essential part of the post device ID mobile analytics landscape.

Predictive analytics in marketing is a data-driven approach to campaign optimization that leverages predictive modeling – a statistical and computational technique that uses historical digital marketing datasets to make informed predictions about future outcomes. Recently, predictive analytics tools often leverage machine learning techniques to build predictive models. Predictive analytics can improve performance measurement by helping app marketers set clear, data-driven performance metrics and KPIs. Accurately predicting customer behaviors and campaign outcomes facilitates a more precise measurement of marketing efforts and ROI.