- Enter the starting population. Use the number of active players at the beginning of period 1.
- Set retention per period. Enter the share of active players expected to remain active into the next period.
- Choose the forecast horizon. Keep the period unit consistent with the retention rate, such as weeks or months.
- Add recurring new players if needed. Enter a fixed number of new players added each period, or leave it at 0 for a pure retention forecast.
- Review ending actives. Use the breakdown to distinguish natural retention from growth supplied by new players.
Loot Drop Retention Forecast Estimator
This estimator projects active-player retention for loot drop over a chosen number of periods. It applies a constant retention rate to the existing active population and can optionally add the same number of new players each period.
The forecast is designed for fast scenario testing rather than detailed cohort modeling. The main result shows projected active players at the end of the horizon, while the breakdown separates retained players from cumulative additions and reports the ending population as a share of the starting base.
Inputs
players
%
periods
players
Result
—
Projected active players
Retained from starting cohort—
Ending vs. starting population—
Net active-player change—
Active players after each period = Previous active players × Retention rate + New players per period
The recurrence is applied once for every forecast period. For a no-acquisition scenario, set new players per period to 0, which reduces the forecast to Starting players × Retention^Periods. The model assumes one constant retention rate across the horizon and does not model separate cohorts.
What the result means
Use the headline result as a planning estimate based on the inputs and assumptions shown above.
Change one input at a time when comparing scenarios so you can see which assumption is driving the result.
Given: 10,000 starting players, 72% retention per month, 6 months, and no new players.
Calculation: 10,000 × 0.72^6 = 1,393.14.
Result: The forecast ends with about 1,393 active players, equal to 13.93% of the starting population.
A real cohort may retain differently by tenure, so use this as a planning scenario rather than a cohort-level prediction.
What period should I use for retention?
Use the same unit used to measure your retention rate. A weekly retention rate should be paired with weekly forecast periods, and a monthly rate with monthly periods.
Are newly added players retained in later periods?
Yes. Once added, they become part of the active population and are subject to the same retention rate in subsequent periods.
Can the forecast grow even when retention is below 100%?
Yes. A sufficiently large recurring inflow of new players can offset churn and increase the active population.
Why is this different from D1, D7, or D30 retention?
Those are cohort checkpoints at specific elapsed days. This estimator treats retention as a repeating per-period transition rate.
Should I use this for long-range forecasts?
Use caution. A constant rate is usually most useful for scenario comparisons; long horizons amplify any mismatch between the assumed rate and actual cohort behavior.