Forecast how many players from a cloud gaming cohort may remain active after several monthly retention cycles. Compare the current retention path with an improved scenario and quantify the additional retained players. This simple cohort model is useful for planning, but it assumes the same retention rate every month rather than a changing survival curve.
The main result is the forecasted active share under the current retention rate. The comparison shows the cumulative effect of a sustained percentage-point improvement.
Real retention often changes by player age, season, content release, and platform. Replace this constant-rate forecast with a cohort survival curve when detailed data is available.
Starting with 10,000 players at 80% monthly retention for 6 months leaves about 2,621 players, or 26.21% of the cohort. Raising retention to 83% leaves about 3,270 players, approximately 649 more.
It is entered as percentage points. For example, 80% plus 3 points becomes 83%.
No. It follows only the starting cohort and does not add future acquisitions.
Yes. In that case the model keeps the full cohort active throughout the forecast.
A constant-rate model may miss early churn spikes, seasonality, content changes, or differences between acquisition cohorts.
Include them only if your source retention metric treats reactivation as retained activity; otherwise track them separately.
| Variable | Meaning |
|---|---|
| Starting cohort | Players active at month zero |
| Current retention | Constant month-to-month survival assumption |
| Improved retention | Current rate plus the entered point uplift, capped at 100% |
| Forecast horizon | Number of compounding monthly cycles |