App Store Revenue & Downloads Estimator

Estimate App Store downloads and revenue for any iPhone app in 175 countries. Give the Actor app IDs, App Store URLs, bundle IDs or developers, or ask it to estimate a whole top chart. For each app and storefront it returns:

• the app's chart ranks: Top Free, Top Paid and Top Grossing, overall and in its genre
• price, currency and an in-app purchase signal
• rating count, and rating growth since your last run
• low / mid / high download and revenue ranges per day and per month, each with a method and a confidence field

Directional estimates, not actual data. Apple does not publish downloads or revenue for apps. Every figure in this Actor's output is a model estimate built from public chart ranks and rating counts. The model is calibrated against published third-party figures (median error about 1.6x for revenue and 1.3x for downloads in the US), but any single app can be several times off. Each row is labeled isEstimate: true and carries a disclaimer. Use the numbers to compare apps, markets and trends, not as accounting figures. This Actor is not affiliated with, endorsed by or sponsored by Apple Inc. App Store and iPhone are trademarks of Apple Inc.

What it does

1. It looks up each app in each storefront with Apple's official iTunes Lookup API. That gives price, genre, rating and rating count. Lookups are batched up to 100 per call and throttled to Apple's documented ~20 calls per minute.
2. It reads Apple's public RSS top charts for each storefront: Top Free, Top Paid and Top Grossing iPhone apps (top 100), plus the same charts for each app's genre. It finds each app's rank in them.
3. It converts ranks into downloads and revenue with a rank-to-demand power law. The curve is calibrated on published figures for about 30 US apps and scaled to each country's market size (see Methodology).
4. Apps outside the top 100 are estimated from their genre-chart rank or their rating velocity: new ratings per day, read from a history store of your earlier runs, or the lifetime average on the first run. These estimates are capped below chart rank #101 and get low confidence and wide ranges.
5. Optionally, it adds publisher rollups: summed bands per developer and country.

Use cases

• Competitor research: how big is a rival app in the US vs Germany vs Japan?
• Market sizing: estimate the whole Top Free or Top Paid chart of a country in one click.
• App acquisition / investing screens: spot apps whose rating growth is speeding up (schedule daily or weekly runs).
• ASO and UA planning: check how many downloads a chart position roughly represents in each storefront.
• AI agents: a cheap "how big is this app?" tool for research agents (see the MCP section).

Input example

{
  "apps": ["324684580", "https://apps.apple.com/us/app/duolingo-language-lessons/id570060128", "com.midasplayer.apps.candycrushsaga"],
  "countries": ["us", "gb", "de"],
  "estimateTopChart": "none",
  "developers": [],
  "publisherRollups": false,
  "trackRatingGrowth": true
}

• countries: two-letter storefront codes. top gives the 30 largest storefronts and all gives all 175.
• estimateTopChart: top-free, top-paid or top-grossing adds the first topChartLimit apps (1-100) of that chart in each country.
• scanGenreCharts (default on): also reads the genre charts of each app's genre, so apps outside the overall top 100 can still get a rank.
• developers: developer IDs or developer URLs. The Actor takes each developer's most-rated apps, up to maxAppsPerDeveloper.
• countryMultipliers: optional overrides of a storefront's size, e.g. {"de": {"downloads": 0.15, "revenue": 0.11}}.

Output example (one row, real run 2026-10-17, shortened)

{
  "type": "estimate", "appId": 570060128, "name": "Duolingo: Learn Languages", "country": "us",
  "primaryGenre": "Education", "price": 0.0, "currency": "USD", "isFree": true,
  "hasInAppPurchases": true, "rating": 4.723, "ratingCount": 5511319,
  "topFreeRank": 67, "topPaidRank": null, "topGrossingRank": 26, "genreTopGrossingRank": null,
  "downloadsMonthly": {"low": 340000, "mid": 870000, "high": 2100000},
  "revenueMonthlyUsd": {"low": 7200000, "mid": 17000000, "high": 38000000},
  "downloadsMethod": "top-free-overall-rank-power-law",
  "revenueMethod": "top-grossing-overall-rank-power-law",
  "confidence": "medium", "downloadsConfidence": "medium", "revenueConfidence": "medium",
  "countryMultiplier": {"downloads": 1.0, "revenue": 1.0},
  "revenueBasis": "gross consumer spend in USD before Apple's commission and taxes",
  "isEstimate": true, "disclaimer": "Directional model estimate, not actual Apple data. Not affiliated with Apple. ..."
}

Daily bands (downloadsDaily, revenueDailyUsd) are included too. All values are rounded to 2 significant figures on purpose. Error rows (type: "error": invalid ID, app not available in a storefront) are free. A run summary is saved to the OUTPUT record.

Methodology (summary; full details and the calibration table in METHODOLOGY.md)

Rank to demand. Sales rank follows a power law: value(rank) = A x rank^-b. Chevalier & Goolsbee used this form for Amazon book ranks, and Garg & Telang (2013) used it for the App Store. The US curves (per day) are:

Curve (US iPhone)	low	mid	high	Calibration
Top Grossing revenue (USD, gross)	2.5M x r^-0.72	5.1M x r^-0.68	9.5M x r^-0.62	22 apps at ranks 1-90. Median error 1.6x, max 2.3x, 21 of 22 inside the band
Top Free downloads	75k x r^-0.45	150k x r^-0.39	300k x r^-0.35	12 apps and studies at ranks 1-100. Median 1.3x, max 2.1x, all inside the band
Top Paid downloads	5k x r^-1.0	12k x r^-0.946	25k x r^-0.90	Not calibrated (Garg & Telang shape). Confidence is at most low

The mid curves are least-squares fits to publicly reported US iPhone figures. Examples: Sensor Tower's published weekly revenue for MONOPOLY GO! ($13M/week, #8 today), Royal Match ($12M/week, #14), Tinder (~$9M/week, #25), Hinge, Bumble, Strava and Raya (#90, ~$0.8M/week). Appfigures' ChatGPT spend report gives the #1 point. For downloads, Sensor Tower's study puts the #1 app at about 94k/day and #10 at about 49k/day, and Reflection data puts the top-100 entry at about 11.7k/day. Each reference is matched to the app's US rank on 2026-10-17. METHODOLOGY.md lists every reference with its URL and date, and tests/calibration.py re-checks the tolerance.

Genre charts. A genre rank #r is converted to an overall-equivalent rank r x 100 / k, where k is the number of apps of that genre in the overall top 100. The estimate is capped below overall rank #101 and has confidence low.

Countries. The US curve is multiplied by a storefront factor for downloads and one for revenue. For example, Japan is 0.21 / 0.45, the UK 0.18 / 0.14, Germany 0.13 / 0.11, Brazil 0.21 / 0.03 and Turkey 0.06 / 0.015. Download factors come from AppTweak's 2025 downloads-by-country report. Revenue factors are rough tiers and are not validated app by app. Storefronts that are not listed default to 0.005 / 0.002. You can override any value with countryMultipliers.

Apps without a chart rank get low or very low confidence and wide ranges:

• Downloads = new ratings per day x 15 / 50 / 200 downloads per rating. The second and later runs use the actual rating growth (low); the first run uses the lifetime average (very low).
• Revenue = downloads x a revenue-per-download range of $0.01-$4 (games $0.02-$5), or price x downloads for paid apps (very low / low).
• Both are capped below the value at rank #101 of the scanned chart.

Confidence is medium, low or very low, never high.

• medium: a direct overall Top Free or Top Grossing rank in a top-15 storefront. Even then a single app can be 2-5x off, because ranks move and category matters (dating apps earn more per rank than the curve shows).
• The row-level confidence is the lower of the download and revenue confidences.

Sources

• Garg, R. & Telang, R. (2013). Inferring App Demand from Publicly Available Data. MIS Quarterly 37(4). https://aisel.aisnet.org/misq/vol37/iss4/14/
• Sensor Tower blog, quarterly US iOS top-5 revenue and download posts (Jul 2025 - Jan 2026), e.g. https://sensortower.com/blog/2025-q4-ios-top-5-free-to-play-games-revenue-us-620e5136afb48b4f71fc7c60 and https://sensortower.com/blog/2025-q3-ios-top-5-dating-revenue-us-64c9b6bbe1714cfff1c9d0e8
• Sensor Tower (Sep 2019), downloads needed to reach No. 1. https://sensortower.com/blog/app-downloads-to-number-one
• Appfigures (Dec 2025), ChatGPT crosses $3B in mobile revenue. https://appfigures.com/resources/insights/chatgpt-hits-3b-consumer-spend
• Statista (2022), daily downloads needed to rank in the US top 10. https://www.statista.com/statistics/1317770/us-daily-downloads-needed-rank-first-10-positions-ios-apps/
• PocketGamer.biz / Reflection (2017), top ranking download requirements. https://www.pocketgamer.biz/sponsored-app-stores-top-ranking-download-requirements/
• AppTweak (Dec 2025), App downloads by country. https://www.apptweak.com/en/reports/app-downloads-by-country
• Match Group, Grindr and Duolingo FY2025 10-K filings (cross-check, links in METHODOLOGY.md)

Data sources

The Actor uses only Apple's public endpoints. These are the same endpoints, with the same throttling and retries, as our live App Store ASO Keyword Rank Tracker & Top Charts Actor:

• iTunes Lookup API (https://itunes.apple.com/lookup): throttled to Apple's documented ~20 calls per minute.
• Apple iTunes RSS top charts (https://itunes.apple.com/{cc}/rss/topfreeapplications|toppaidapplications|topgrossingapplications/limit=100[/genre=ID]/json): limited to 30 calls per minute, with up to 4 tries and backoff.
• Apple Marketing Tools RSS (https://rss.marketingtools.apple.com/api/v2/{cc}/apps/top-free|top-paid/100/apps.json): fallback for the overall Top Free and Top Paid charts when the feed above fails.

It does not use the Search API, apps.apple.com pages, Google Play, proxies, a browser or a login.

Pricing

	This Actor	seibs.co revenue estimator	apivault_labs / codebyte
1 app, 1 country, ranks + estimate bands	$0.004	$0.01 record + $0.02 estimate enrichment	$0.002 per result
Each extra country	$0.001	per record	per result
Publisher rollup	$0.005	$0.03	-
Error rows	free	-	-

Competitor prices were taken from their Store pages in October 2026.

A 3-app x 3-country check costs $0.018. Estimating a US top-25 chart (Top Free, Paid or Grossing) costs $0.10. Set Maximum cost per run and the Actor stops cleanly when it is reached.

FAQ

How accurate is it? In the US, the calibrated curves land within about 2x of published third-party figures for most chart apps. The median error is 1.6x for revenue and 1.3x for downloads, and the worst reference is 2.3x off. Those references are themselves estimates by Sensor Tower or Appfigures. Individual apps can still be 2-5x off: ranks move, events cause spikes, and subscription and dating apps earn more per rank than games. Off-chart apps and small storefronts are less certain, and their bands are 10x or more wide. Use the output for ranking and comparing apps, not for valuation or reporting.

Why is revenue for some free apps so uncertain? An app that is not on the Top Grossing chart, overall or in its genre, gets revenue from a wide revenue-per-download assumption, capped below Top Grossing rank #101. Apps that sell through the web (for example Spotify in the US) are not ranked by Apple's grossing chart, so their App Store revenue is low by design.

Does it show in-app purchases? Apple's public Lookup API does not expose in-app purchases. hasInAppPurchases is true for a free app that ranks on a Top Grossing chart, and null (unknown) otherwise.

Why do rating growth fields start empty? Growth needs an earlier snapshot. Schedule the Actor (for example daily) with the same historyStoreName. From the second run on you get ratingCountChange and ratingsPerDay, and downloads for off-chart apps switch to the rating-velocity method.

iPad, Mac, Google Play? No. This Actor covers iPhone App Store storefronts only.

Why is it sometimes slow? It reads 3 overall charts plus a few genre charts per storefront. RSS calls are limited to 30 per minute and Lookups to Apple's documented ~20 calls per minute. Failed calls are retried with backoff.

Use with AI agents / Apify MCP

Add this Actor to an MCP client (Claude, Cursor, ChatGPT and others) through the Apify MCP server (https://mcp.apify.com), using the tool gazidev/app-revenue-estimator. An agent can call it with {"apps": ["<id or URL>"], "countries": ["us"]} and read downloadsMonthly, revenueMonthlyUsd, confidence and method. Tell the agent to present the values as estimated ranges. The disclaimer and isEstimate fields are there so it can do that automatically.

Related: App Store ASO Keyword Rank Tracker & Top Charts for daily chart and keyword rank tracking.