Analytics systems track three standard types of revenue from mobile apps and games on Android and iOS – in-app payments, subscriptions, and ads.
But e-commerce stores have other sources of revenue, such as offline payments or purchases via PayPal, which are not captured this way.
These e-commerce apps can analyze their revenue with the help of our S2S API that enables them to upload payment data from third-party services, internal CRMs and spreadsheets to MyTracker’s user-friendly interface.
Server to server (S2S) data transfer has been in place for a while now. We are taking a step further and introducing unique LTV forecasting for e-commerce stores. It empowers you to:
MyTracker employs machine learning models that analyze in-app user behavior to identify patterns and forecast potential purchases with up to 90% accuracy
With MyTracker, you can pick out the highest LTV users and then focus on retaining them and attracting similar customers.
Predictive analytics gives you an opportunity to assess the efficiency of new functions. For instance, checking how likely a new promo pop-up window is to improve your revenue from a certain user group in the long run.
Users often choose and buy things online via several devices – phones, tablets, you name it. To get more accurate data on your revenue, it’s important to distinguish users from their devices.
MyTracker can track all user actions, and hence, revenues, from various devices using an ID assigned to every authorized user (phone number, email or login).
Don’t limit your revenue management to conventional practices – forecast your revenue, select more profitable users and assess the ROI of your new app features with MyTracker predictive analytics.