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In Game Economy Retention Forecast Estimator

The In Game Economy Retention Forecast Estimator projects how many players from a starting cohort remain active after a chosen number of periods when a constant retention rate is applied repeatedly. It gives live-ops teams and analysts a fast way to translate a per-period retention assumption into an estimated retained population and churned population. The model is intentionally simple: the same retention percentage is used for every period. That makes it useful for scenario testing and quick capacity or audience planning, but real cohorts often have a changing retention curve, especially soon after acquisition or a major content update.

Retention forecast inputs

players
%
periods
Result
—
players retained after forecast
Retained share—
Estimated churned players—
Cumulative churn share—

1. Enter the starting cohort
Provide the number of players at the beginning of the forecast.

2. Set retention per period
Enter the percentage of players expected to remain active from one period to the next. Use a rate that matches the period you intend to forecast.

3. Choose the forecast length
Enter the number of periods to compound the retention assumption. A value of 0 returns the full starting cohort.

4. Review retained players
The main result estimates the player count remaining after the selected periods.

5. Check churn and retained share
Use the breakdown to see the corresponding retained percentage and the estimated number and percentage no longer retained.

Retained Players = Starting Cohort × (Retention Rate / 100)^Forecast PeriodsRetained Share = Retained Players / Starting Cohort × 100Churned Players = Starting Cohort − Retained Players

Where:

  • Starting Cohort = players present at the beginning of the forecast.
  • Retention Rate = percentage retained from one period to the next.
  • Forecast Periods = number of equal periods over which retention compounds.

Assumptions: The same retention rate applies independently in every period, with no new players added to the original cohort.

What the result means

Under a constant 80% period-to-period retention assumption, about 32.77% of the original cohort remains after five periods.

For production forecasting, compare this simple constant-rate scenario with observed cohort retention by player age.

Given:

  • Starting cohort: 12,000 players
  • Retention per period: 80%
  • Forecast length: 5 periods

Calculation:

Retained players = 12,000 × 0.80^5

0.80^5 = 0.32768

Retained players = 12,000 × 0.32768 = 3,932.16

Churned players = 12,000 − 3,932.16 = 8,067.84

Result: Estimated retained players after 5 periods: 3,932.

Interpretation: Under a constant 80% period-to-period retention assumption, about 32.77% of the original cohort remains after five periods.

How should I interpret a retained count with decimals?

The formula can produce a fractional expected count because it represents an average forecast. For planning, the displayed player count is rounded to a whole player.

What period should the retention rate use?

Use the same interval represented by one forecast period, such as day-to-day, week-to-week, or season-to-season retention. Do not combine a weekly rate with daily periods without converting the rate.

Why can a constant retention forecast differ from real cohort data?

Real retention often changes by player age, content cadence, acquisition source, and season. A constant-rate model smooths those changes into one repeated assumption.

What happens if I use 100% or 0% retention?

At 100%, the full cohort remains for every period. At 0%, no players remain after the first positive forecast period.

How is this different from a player lifetime value estimate?

Retention forecasting estimates how many players remain active. Lifetime value estimates the economic value generated by a player and may use retention or lifetime assumptions as an input.