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ROAS coef prediction for a mobile game

Predictions help make decisions about changes in advertising campaigns or products based on data

More effectively allocate the budget

Challenge

calculating the ROAS coefficient in terms of platforms and types of monetization (ad revenue, in-app, and total) to adjust the KPIs

Issue

The old coefficients did not accurately reflect the real income picture, thus affecting the ROAS KPIs. With the old coefficients, for target ROAS values of 52% on day 7, breakeven (100% return) was achieved on day 90 of the cohort.

Our analysis resulted in new coefficients. Consequently, with these new coefficients, the day 7 ROAS should be at least 36% to achieve ROAS 100% on the 90th day.

This finding facilitates quicker achievement of one of the project’s key goals –– cost-effective scaling.

Methodology

1st

Took data from Appsflyer and divided the sample into two different monetization models, as we assume they will have different retention

2nd

Collected data on the total revenue and number of purchases, in order to calculate the average ticket size and retention

3rd

Looked at the shape of the retention curve by revenue count for the two different monetization models and for different OS

4th

Calculated the average ticket size for each of them.

5th

Calculated the LTV for a user + expected LTV with an “extended” curve

6th

Derived the coefficients per user (and per cohort) — dividing LTV from day 90-540 by LTV on the 7th day.

It turns out that the parameters for the model are the day 0 retention rate and day 0 average ticket size. If the average ticket size increases significantly, it is worth changing it for the entire cohort so that the predicted LTV is based on updated data.

The smarter way to achieve a higher ROAS

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