Predictive analytics of MyTracker is advanced analytics tools that will expand your knowledge about the app audience, enriching gathered information with "data from the future".
Forecasts of various metrics, scoring, event correlation, user quality assessment, the event-important prediction of financial metrics and audience churn, etc. — these are all predictive analytics tools.
Prediction enables you to take a more informed decision on app promotion without having to wait for actual data to accumulate. For example, you can remove inefficient channels before they become a real problem, or rebalance your budget to reduce costs and improve user acquisition.
Predictive analytics is based on a large amount of data collected by MyTracker. A combination of several groups of continually evolving and updated predictive models works to give you the most accurate forecast possible.
Below are the common stages of predictive analysis in MyTracker:
The first several days provide a solid enough foundation to make a strategic decision on things like buying traffic. This is why we update our predictions during the first seven or eight days and give the final forecast on the eighth or ninth day (depending on prediction metrics).
MyTracker LTV forecasts are based on a combination of predictive models that can be distilled into three groups:
Any prediction is based on a combination of these three groups. This mixed model makes a prediction as early as the day after app installation, and it can work with little data and provide an accurate forecast based on a history of just 30 days.
For more details, see the LTV prediction section
SKAN LTV forecasts are based on a combination of the predictive model trained on SKAN postback data and the three groups of models for LTV Prediction listed above.
The prediction maintains user privacy, does not identify devices, and provides data only at the level of partners and ad campaigns.
For more details, see the SKAN LTV prediction section
Churn forecast is based on models, for which it is desirable to have an app history of the past six weeks.
These models make the first prediction one day after the app install, and provide an accurate forecast on the fifth and subsequent days after an install.
For more details, see the Churn prediction section