How to Estimate Any App's Revenue (5 Methods Compared)

Estimating an app's revenue is a critical skill for investors, competitors, and developers alike. Whether you're evaluating an acquisition target, benchmarking your own performance, or simply curious about a competitor's financial health, a reliable revenue estimate provides invaluable insight. However, unlike traditional businesses, app revenue figures are rarely public. This guide outlines five practical methods, from basic observation to advanced machine learning, that we at bumetric use and recommend for estimating any iOS or Android app's monthly revenue. Each method offers a different balance of effort, data availability, and accuracy.

1. Public In-App Purchase (IAP) Screens and Conversion Rates

This method involves a direct examination of the app's monetization strategy and a calculated estimate of its user base. It's often the first step I take when analyzing an app because it provides a foundational understanding of its pricing.

How it works:

1. Identify IAP offerings: Open the app and navigate to its subscription page or purchase options. Screenshot the available plans (e.g., monthly premium, annual pro, one-time unlock). Note the prices for each.
2. Estimate subscriber count: This is the most challenging part. A common proxy is to use the app's total ratings as a base. Industry averages suggest that roughly 2% to 5% of an app's active users convert into paying subscribers or make an IAP. Furthermore, not every download results in a rating. A common ratio is 50 to 200 downloads per rating.
3. Calculate estimated revenue: Multiply the estimated number of paying users by their respective average purchase price. For example, if an app has 10,000 ratings, and we assume 100 downloads per rating, that's 1,000,000 downloads. If 3% convert to a $9.99/month subscription, that's (1,000,000 downloads / 100 downloads per rating) 10,000 ratings 0.03 conversion rate * $9.99/month = $2,997/month. This example is simplified; a more granular approach considers different IAP tiers.

Accuracy: This method can yield an estimate with an accuracy range of approximately ±30%. Its reliability depends heavily on the accuracy of your assumed conversion rates and the complexity of the app's IAP structure. Apps with a single, clear subscription model are easier to estimate than those with multiple one-time purchases and consumables.

Example: Consider an app like Headspace Meditation & Sleep. You can see its subscription options directly within the app. If it offers a $12.99/month subscription and you estimate 100,000 active subscribers, that's an estimated $1.299 million monthly revenue from that tier alone.

2. App Store Connect Leaks or Insider Disclosures

This is the gold standard for accuracy when available. Occasionally, founders, developers, or acquirers will publicly disclose an app's revenue figures.

How it works:

1. Monitor founder communities: Platforms like IndieHackers, Twitter (especially among the #buildinpublic crowd), and acquisition marketplaces such as Flippa or MicroAcquire are common places where founders share their Monthly Recurring Revenue (MRR) or total revenue figures.
2. Acquisition listings: When an app is listed for sale, detailed financial statements, including revenue, are often provided to serious buyers. These figures sometimes become public after a deal closes or if the listing itself is publicly viewable.
3. App Store Connect leaks: Less common, but sometimes screenshots of App Store Connect dashboards, which show granular revenue data, are inadvertently or intentionally shared.

Accuracy: When a verifiable disclosure is made, the revenue figure is exact. This method serves as a crucial anchor point for validating other estimation techniques. These disclosures are rare for large, private companies but more common for indie developers or smaller studios.

Example: A founder might post on IndieHackers, "My habit tracker app, Streaks, just hit $15,000 MRR this month!" This provides a precise data point for that specific app at that specific time.

3. Ratings Velocity as a Proxy for Downloads and Revenue

This method leverages the rate at which an app accumulates new ratings to infer its download volume, which can then be tied to revenue.

How it works:

1. Track daily new ratings: Monitor the number of new ratings an app receives each day or week. This data is often available through third-party app intelligence tools or by manually tracking the total rating count over time.
2. Estimate downloads per rating: As mentioned earlier, the industry average for downloads per rating typically falls between 50 and 200. This ratio can vary significantly based on app category, user experience, and even specific prompts within the app to encourage ratings.
3. Infer downloads: Multiply the daily new ratings by your estimated downloads-per-rating factor to get an approximate daily download count.
4. Connect downloads to revenue: This step requires an understanding of the app's monetization model and average revenue per download (ARPD) or average revenue per user (ARPU). If you know an app makes $0.50 per download, and you estimate 1,000 daily downloads, that's $500/day or $15,000/month.

Accuracy: This method has an accuracy range of approximately ±50%. The wide variability in the downloads-per-rating ratio and ARPD/ARPU makes it less precise than direct IAP analysis or insider data. However, it's particularly useful for tracking trends and identifying apps with rapidly growing user bases.

Example: If an app consistently gains 50 new ratings per day, and you assume 100 downloads per rating, that suggests 5,000 daily downloads. If you've previously established that similar apps in its category generate an ARPD of $0.20, then the daily revenue estimate would be $1,000, translating to $30,000 per month.

4. Top-Grossing Chart Position

For high-earning apps, their position on the top-grossing charts provides a strong indicator of revenue. App stores publicly display these charts, making this method accessible.

How it works:

1. Identify top-grossing position: Check the app's ranking on the "Top Grossing" charts in its respective category and country (e.g., US iOS Games Top Grossing).
2. Reference known benchmarks: App intelligence firms like Sensor Tower have historically reverse-engineered the revenue ranges associated with specific positions on these charts. For instance, an app consistently in the top 10 grossing US iOS might earn millions per month, while an app in the top 200 might earn hundreds of thousands. While specific thresholds fluctuate, generally:

* Top 10 US iOS Grossing: $1M+ / month

* Top 50 US iOS Grossing: $500K - $1M / month

* Top 200 US iOS Grossing: $100K - $500K / month

These figures are broad estimates and serve as a general guide.

1. Adjust for category and region: Revenue thresholds vary significantly by category (games typically have higher thresholds) and region (US, Japan, China are high-spending markets).

Accuracy: This method is relatively accurate for apps consistently in the top 200 grossing charts, with an accuracy range of approximately ±20%. For apps outside the top 200 (the "long tail"), the accuracy drops significantly, potentially to ±80%, as the revenue differences between adjacent ranks become much smaller and harder to distinguish.

Example: If an app like Roblox consistently ranks in the top 5 US iOS grossing, you can confidently estimate its monthly revenue in the multi-million dollar range, based on established industry benchmarks for those high-tier positions.

5. ML-Calibrated Forecasts (What bumetric does)

This is the most sophisticated method and represents the core of bumetric's approach to app revenue intelligence. It combines various data points with machine learning to generate highly accurate estimates.

How it works:

1. Anchor against verified data: The process begins by anchoring the model against known, founder-declared revenue figures (Method #2). These exact data points provide crucial ground truth for the machine learning algorithms.
2. Collect comprehensive data points: We ingest hundreds of data signals for each app. These include:

* App Store Optimization (ASO) metrics (keywords, descriptions, screenshots - see our ASO audit at /optimize)

* Ratings and review velocity (Method #3)

* Historical download trends (estimated)

* Top-grossing chart positions (Method #4)

* IAP pricing structures (Method #1)

* Category, publisher history, age of app

* Geographic availability and localization

* User engagement metrics (inferred from updates, reviews)

* And 234 other specific machine learning triggers.

1. Machine Learning Extrapolation: Our proprietary algorithms analyze these 234 triggers, identifying complex relationships and patterns that correlate with revenue. The model learns from the anchored data points and then extrapolates these learnings across the entire catalog of iOS (/analytics/ios) and Android (/analytics/android) apps. This allows us to estimate revenue for apps where no public disclosures exist.
2. Continuous refinement: The models are continuously updated and refined as new data becomes available and as app store dynamics evolve. Our methodology (/methodology) provides more detail on this process.

Accuracy: With anchored machine learning, this method achieves an accuracy range of approximately ±15%. This higher precision is due to the ability of ML models to process a vast array of subtle signals and identify non-obvious correlations that human analysis might miss.

Example: When you look up an app like Duolingo on bumetric.com, the estimated revenue displayed is the result of this ML-calibrated forecast. Our models have analyzed its ratings velocity, ASO keywords, IAP offerings, historical chart performance, and hundreds of other data points, all informed by a network of known revenue figures from similar apps.

Triangulation: The Key to Reliable Estimates

No single method is perfect, and relying on just one can lead to significant errors. The most robust approach to estimating app revenue is triangulation. This means using at least two, and ideally three or more, of these methods to cross-reference and validate your findings.

For instance, if Method #1 (Public IAP screens) suggests an app earns $50,000/month, and Method #3 (Ratings velocity) points to a similar figure, you can have higher confidence in that range. If one method provides a wildly different estimate, it signals a need for further investigation or adjustment of your assumptions.

At bumetric, we inherently triangulate by feeding multiple data sources into our ML models. For your own analysis, I strongly recommend comparing your findings across different methods. This iterative process of cross-validation will significantly improve the reliability of your app revenue estimates.