How we estimate downloads & revenue
Short version: we combine two public signals — chart rank and review velocity — and express the result as a range with a confidence label. Point estimates would be dishonest without an SDK panel; ranges with caveats are not.
Signal A — Chart-position decay curve
An app's position on the Top Free / Top Paid / Top Grossing charts maps to a downloads-per-day range, empirically calibrated from published benchmarks (Sensor Tower blog posts, AppFigures research, historical leaks). Our default curve for a category like Productivity looks roughly like:
| Rank band | Est. downloads / day |
|---|---|
| Top 1–10 | 12K – 60K |
| 11–50 | 2.5K – 15K |
| 51–100 | 800 – 8K |
| 101–200 | 150 – 1.5K |
| Outside top 200 | 30 – 400 |
We use only Top Freerank — Top Paid and Top Grossing describe revenue density, not download volume, so feeding them into a download curve produces misleading numbers. Top rank is taken from the best of US / UK / CA / AU. Categories with different volume dynamics (Finance vs. Photo & Video) get tuned curves.
Sustained-presence adjustment: apps with fewer than 100 ratings orfewer than 7 days of momentum history get their chart-band estimate scaled down by half. These are usually newly-charting apps that briefly hit a high rank but haven't accumulated the typical volume of an app that lives at that rank. Without this, a 3-rating app at #10 would look like a unicorn.
Signal B — Rating-count velocity
Apps accumulate reviews proportionally to downloads. If an app gained 120 reviews in the last 7 days, that's ~17 reviews/day. Multiply by the inverse of category review-rate (e.g., 1 review per 500 downloads = 500× downloads per review) to get downloads/day.
Category-specific review-to-download ratios (low–high):
| Category | Reviews : downloads |
|---|---|
| Productivity | 1 : 300 – 1000 |
| Finance | 1 : 500 – 1500 |
| Utilities | 1 : 500 – 2000 |
| Health & Fitness | 1 : 200 – 500 |
| Photo & Video | 1 : 300 – 800 |
| Lifestyle | 1 : 400 – 1200 |
Combining the two
We compute both signals and return a range that brackets them. If they broadly agree (within 1.5×) and the app charts in the top 50 and we have 14+ days of momentum history, the estimate gets a high confidence label. Within 2× and 7+ days of history → medium. Anything else → low.
Revenue
Paid apps: downloads × price × 0.7 (Apple takes 30%).
Free with IAP/subscription:downloads × category ARPU × 0.7. We can't tell which free apps actually monetize via IAP from public data, so this number assumes monetization exists. When you see "(IAP-driven)" next to a free app's revenue, that assumption is in play — treat it as an upper bound, not a guarantee.
We do notattempt subscription LTV modelling — the required retention data isn't public.
What we cannot compute
- Per-country revenue breakdowns (US/UK/CA/AU only, combined)
- Subscription LTV or churn-adjusted ARPU
- Ad-network revenue with precision
- Apps that have never charted and have no review history
Plain-language guarantee
We'll never quote a single number with two decimal places like "$43,247.12/month" — that would be theater. You get a band and a confidence label, with the math behind it documented here. If something looks off for a specific app, the most likely cause is either (a) we don't have enough history yet, or (b) the category multiplier needs tuning. Both improve over time.
v1.1-2026-05. We publish a new model version when the calibration tables or signal weights change.