How Much Do Top Apps Really Make? App Revenue Estimation Guide

Ever wondered how much your favorite app makes? While exact revenue figures are closely guarded secrets, we can make surprisingly accurate estimates using publicly available data. Here's how app revenue estimation works and what the numbers reveal.

The Data Points We Have

App stores provide several public data points that feed into revenue models:

From Google Play

• Install count — Exact minimum installs (e.g., "1,000,000+")
• Rating count — Total number of ratings
• Price — App purchase price
• In-app purchases — Whether the app offers IAP
• Ad-supported — Whether the app shows ads
• Category ranking — Position in category charts

From iOS App Store

• Rating count — Total number of ratings (no install count)
• Price — App purchase price
• In-app purchases — Price ranges listed
• Category ranking — Position in charts

Revenue Estimation Models

Model 1: Paid Apps

The simplest model. If an app costs money:

Revenue = Downloads x Price x 0.7

The 0.7 multiplier accounts for the 30% store commission (15% for small businesses under $1M).

Example: A $4.99 app with 500,000 downloads:

• Gross: 500,000 x $4.99 = $2,495,000
• Net: $2,495,000 x 0.7 = $1,746,500 lifetime revenue

Model 2: Freemium (In-App Purchases)

This is where most app revenue comes from. The key metrics:

• MAU (Monthly Active Users) — Typically 25-30% of total installs
• Conversion rate — Percentage of MAU that pays (typically 2-5% for top apps)
• ARPPU (Average Revenue Per Paying User) — Varies wildly by category

MRR = MAU x Conversion Rate x ARPPU x 0.7

Example: A social app with 10M installs:

• MAU: 10M x 0.27 = 2.7M
• Paying users: 2.7M x 3.5% = 94,500
• ARPPU: $7/month (typical subscription)
• MRR: 94,500 x $7 x 0.7 = $462,525/month
• ARR: $5.55M/year

Model 3: Ad-Supported

Ad revenue depends on engagement:

Monthly Ad Revenue = MAU x Sessions/Month x Ad Impressions/Session x eCPM / 1000

Simplified: MRR = MAU x $0.50-2.00 (depending on category and region)

Model 4: Hybrid (IAP + Ads)

Most top-grossing apps use both:

Total MRR = IAP MRR + Ad MRR

The key insight: whales (top 1% spenders) often generate 50%+ of IAP revenue, while ads provide a baseline from the remaining 95%+ of free users.

iOS Download Estimation

Since Apple doesn't share download counts, we estimate:

Downloads ≈ Rating Count x 40-70

The multiplier varies by category:

• Games: x 40-50 (gamers rate more often)
• Social: x 50-60
• Utilities: x 60-80 (utility users rarely rate)
• Enterprise: x 80-100 (business users almost never rate)

Industry average is around x 50 — roughly 2% of users leave a rating.

Revenue Benchmarks by Category (2026)

How to Estimate Any App's Revenue

You can get instant revenue estimates using Unstar.app Compare:

• Search for any app

• The comparison view shows:

- Estimated downloads (real data for Android, estimated for iOS)

- MAU (Monthly Active Users estimate)

- MRR (Monthly Recurring Revenue)

- ARR (Annual Recurring Revenue)

- Revenue model (Paid, Freemium, Ad-supported)

This is calculated using the models described above, with adjustments based on the app's category, pricing, and available metadata.

Common Revenue Estimation Mistakes

1. Ignoring Regional Differences

A download from the US is worth 5-10x more than a download from India in ad revenue. Apps with primarily US/EU users generate dramatically more per user.

2. Confusing Downloads with Active Users

An app with 10M downloads might only have 1-2M monthly active users. Retention drops off sharply — most apps lose 75% of users within the first week.

3. Overestimating Subscription Conversion

While some apps achieve 5%+ conversion, the median is closer to 2-3%. Premium apps in niche categories with loyal audiences (e.g., fitness trackers) do better than broad-appeal social apps.

4. Forgetting Store Commissions

Always multiply by 0.7 (or 0.85 for small businesses). That 30% commission is significant at scale — a $100M app pays $30M to Apple/Google.

What Negative Reviews Tell Us About Revenue

Here's a fascinating correlation: apps with more subscription-related complaints often have higher revenue. Why? Because users complaining about pricing are *paying* users. Free users don't complain about subscriptions.

Complaint patterns that indicate healthy revenue:

• "Too expensive" — Users recognize the value, just not the price point
• "Subscription should include X feature" — Paying users want more value
• "Free trial was too short" — Users wanted to convert but needed more time

Complaint patterns that indicate revenue problems:

• "Not worth any money" — Value proposition isn't landing
• "Switched to free alternative" — Losing users to competitors
• "Hidden charges" — Trust issues that lead to chargebacks

Conclusion

App revenue estimation isn't an exact science, but with public data and the right models, you can get within 20-30% accuracy for most apps. The key variables are downloads (or ratings as a proxy), active user ratio, monetization model, and conversion rates. Use Unstar.app Compare to instantly estimate and compare revenue for any app, and use negative review patterns to understand whether that revenue is sustainable.