Predictive analytics

One of the key weaknesses of predictive analytics is the poor ability to factor in qualitative shifts or fluctuations after the bifurcation points. As all these algorithms are based on quantitative probability methods, they face difficulties when it comes to predicting user behaviour after sudden and significant changes.

Say you announced a big discount or promo, resulting in more customers buying your products. Is this only a temporary effect? If yes, when will it be reversed? Will it lead to a higher or lower LTV of an average user?

Predicting such effects is a challenging task for an algorithm, which may produce a less accurate LTV prediction after a successful promo. Another factor likely to impact the analysis is a change in ad monetization or overall app development strategy. The system detects unexpected activity and makes relevant adjustments to models by flattening the peaks. Yet, 100% accuracy cannot be guaranteed in this case.

One of the greatest strengths of MyTracker’s model is its ability to predict revenue generation from any group of users. Thanks to the model mix, we are less dependent on historical data for a similar cohort. Even if no historical data is available, the system makes calculations based on user behavior in similar apps.

This means that you can have an accurate estimate for any cross section (by age, sex, country or add campaign).

Different types of payments (in-app payments, subscriptions, ad monetization) may vary substantially. Subscription payments can drop, while ad revenues may increase. Their trends may often move independently of each other.

This is why MyTracker employs different approaches to tackle each type of payment. Some of them would need a regression tree model, while for others a simple approximation of the logarithm function works better.

MyTracker can predict Lifetime Value (total revenue from a user since app installation) with an accuracy of up to 90% on the 30th, 60th, 90th, 180th, 365th and 730th day of the app installation.

The more data we collect by app, revenue type or other metrics, the more accurate MyTracker's prediction will be. All methods that we use in MyTracker are regularly validated. On top of that, the prediction quality is controlled on an ongoing basis, with some models discarded automatically and others becoming more dominant.

LTV prediction is a valuable marketing tool to analyze apps with in-app purchases or subscriptions and assess revenues by advertising channel and campaign before you spend money on them.

Relying on billions of actions, MyTracker models are capable of making up-to-date predictions from the first days of use. As your app data accumulates and the models are trained to adapt to a specific audience, predictions become increasingly accurate.

Integrate MyTracker SDK into your app. More instructions on how to do this are available here.

Go to our Report Builder and select the prediction period in the Metrics → LTV Prediction section.

Yes, they do. We have created a machine-learning model that predicts LTV using SKAN conversions received from Apple. The model generates a forecast based on the payments of organic users and those who gave the ATT consent, while also factoring in CVs. You can learn more about this method and get a step-by-step guidance in our article.

MyTracker does not limit itself to LTV predictions: with MyTracker, you can predict user churn and export user IDs with high predicted churn rates. This is a powerful tool to manage churn effectively and with more precision. For example, you can offer premium days to users who are about to quit. For those less likely to leave, a couple of free in-game coins or a simple push notification would probably do the trick.