Methodology

AinScope doesn't have access to Apple's or Google's private sales data — nobody outside those companies does, including Sensor Tower or Appfigures. What we do have is public store signals and a transparent, documented estimation model built on top of them. This page explains exactly what's real and what's modeled, so you can judge how much to trust any given number.

REAL DATA Android:Google Play publishes an installs-floor bucket for every app (e.g. "500M+"). We use that floor as a hard lower bound and multiply it (×1.7 for our mid-estimate, ×4 for the high end) to account for the gap between the published floor and the true count.

MODELED iOS:Apple does not expose an installs number at all. We estimate lifetime downloads from the app's rating count, using the long-standing ASO industry rule of thumb that roughly 1 rating is left per 100–200 downloads (we use 150 as the midpoint).

Monthly downloadsare derived by spreading lifetime downloads across the app's age in months, with a 1.2× recency factor to reflect that most apps see more activity in recent months than their full-lifetime average.

MODELED Monthly downloads × a category-specific average revenue-per-download (e.g. games monetize very differently from social apps), adjusted upward for paid apps (price × ~70%, after estimated store cut) and pulled toward near-zero for free apps confirmed to have no in-app purchases.

MODELEDThese are built from category-level statistical models (e.g. social apps have a higher assumed daily-active rate than utility apps), combined with a seed unique to each app so the numbers stay consistent across visits rather than changing randomly on every page load. They are directional estimates, not measurements — we don't have session-level telemetry from real devices.

Every app gets a confidence badge based on how much public signal is available:

• Medium — Android apps with a real Play Store installs floor, our strongest available signal.
• Low — estimates built from rating count alone (typically iOS, or Android apps without a usable installs bucket).
• Limited Data— apps with very few ratings or installs, where our formulas become unreliable. For these we show a conservative "< X" ceiling instead of a precise-looking number, rather than let a small-sample formula produce a falsely confident figure.

REAL DATAChart positions themselves (who's #1, #2, etc.) are real, live store rankings.

MODELEDDownloads/revenue shown per rank use a power-law decay model anchored to publicly reported figures for top-ranking apps, since exact per-rank figures aren't published by Apple or Google.

We record a daily snapshot (rank, rating, installs) for top-charting apps to build our own historical time-series — rank and rating movement over time is a materially better signal than a single point-in-time snapshot, and it's something we can only start collecting from today onward. Over the coming months this data will feed back into tightening the estimates above.

Honestly: less precise. Those tools license device-panel data — real usage and spend signals from millions of opted-in devices, built up over a decade of partnerships. That's a fundamentally different (and expensive) data source that public store signals can't replicate. AinScope is built for directional insight — comparing apps to each other, spotting trends, and getting a free, honest ballpark — not audit-grade precision.