User Growth Forecast Calculator
Users month by month from paid acquisition, referrals measured by a viral coefficient, and churn — and the coefficient that would make growth self-sustaining.
Each month the base loses a share to churn, gains a fixed number from acquisition, and gains a share of itself from referrals: the viral coefficient is the number of new users each existing user brings in per month.
How the user growth forecast calculator works
Each month the base loses a share to churn, gains a fixed number from acquisition, and gains a share of itself from referrals: the viral coefficient is the number of new users each existing user brings in per month.
When the coefficient exceeds the churn rate the base compounds without acquisition; below it, acquisition is what keeps the base growing and the ceiling is acquisition divided by the difference. The calculator runs the months and shows both.
Formula: each month: users = users × (1 − churn + K) + acquired; self-sustaining when K > churn
Worked examples
| Inputs | Users at the end | Note |
|---|---|---|
| Modest referral loop | 23,557 users | ×1.06 a month plus 800 |
| No loop, no churn | 14,600 users | plain addition |
| Referrals below churn | 11,633 users | ceiling about 26,700 |
FAQFrequently asked questions
What is a viral coefficient?
The number of new users each existing user brings in over a period — invitations sent × the share accepted. A coefficient of 0.1 means every ten users recruit one more a month. Above 1 the base doubles each period on referrals alone; most products sit well below that.
Why compare it with churn?
Because both are shares of the base: churn removes a percentage, referrals add one. If the coefficient exceeds the churn rate the base grows without any acquisition; if it does not, acquisition is doing the growing and the base tends toward a ceiling.
Where does the ceiling come from?
When referrals are below churn, each month the base loses a net share of itself and gains a fixed number. They balance at acquisition divided by the net loss rate: at 800 a month, 4% churn and a 0.01 coefficient, that is 800 ÷ 0.03, or about 26,700 users.
Is the coefficient really constant?
Rarely. It falls as a product saturates its natural audience and rises with referral incentives, and churn usually falls as the base matures. Treat the forecast as a scenario and rerun it as the measured figures change.
Where these figures come from
- Interactive Advertising Bureau — measurement guidelines — the impression and viewability definitions CPM depends on
- Australian Competition and Consumer Commission — the Australian consumer law regulator
Last checked: September 2026. These are standard industry definitions; where platforms disagree, the page says so.