App retention benchmarks by category
Benchmarks vary dramatically by category. A day-30 retention of 5% is terrible for a fintech app and exceptional for a casual game. Always compare against your own category, not the all-app average.
| App Category | Day 1 Retention | Day 7 Retention | Day 30 Retention |
|---|
| Mobile Gaming | 28-32% | 8-12% | 3-5% |
| Finance / Fintech | 35-40% | 20-25% | 15-25% |
| Health and Fitness | 30-35% | 12-16% | 6-8% |
| E-Commerce | 32-38% | 14-18% | 7-11% |
| Productivity / Utilities | 40-45% | 22-28% | 13-18% |
These are the ranges used in ApsteQ's app retention benchmarks post, which cites its own sources. They are the assumption this tool uses, not a live dataset. Your app may perform differently based on acquisition channel (organic vs paid), device type (iOS vs Android) and user segment.
Diagnosing retention leaks
Your retention curve tells you exactly where your app is losing users. Match your pattern to one of these profiles and use the fix list.
- D1 problem (Day 1 retention is low): Your acquisition is bringing users who are not your target or your first-time app open is unclear. Users install but never open the app. Fixes: audit your paid ads and keywords (wrong intent audience), simplify first-app-open experience (remove login walls or tutorials), or measure if this is an organic vs paid channel issue.
- D7 problem (Day 1 to D7 is steep): Onboarding is the leak. Users open the app once but do not see its core value quickly enough. Fixes: map the activation moment (the in-app action that proves value), restructure onboarding to reach it in session one, use personalization (ask why they downloaded, show relevant content immediately).
- D30 problem (D7 to D30 is steep): Early adoption is fine but the app does not create habit. Fixes: push notification strategy (send behavioural, not scheduled blasts), create reasons to return (streaks, challenges, social accountability), or reduce friction (remove walls between core features).
A worked example: health and fitness app
Your health app has day 1 = 32%, day 7 = 14%, day 30 = 8%.
- Benchmark: 30-35% day 1, 12-16% day 7, 6-8% day 30.
- Analysis: Day 1 is at benchmark (32% vs 30-35%). Day 7 is at benchmark (14% vs 12-16%). Day 30 is at benchmark (8% vs 6-8%). Overall: you are at benchmark.
- Retention curve: D1 to D7 drops 56% (14/32). D7 to D30 drops 43% (8/14). The curve is typical for health apps.
- Next step: If you want to beat your category, focus on reducing D7 drop. A 1 percentage point gain in day 7 (from 14% to 15%) with the same D30 drop improves overall retention by 8%. This typically comes from improving onboarding (faster activation) and push opt-in (more retention levers post-day-7).
How to improve retention by leak point
If D1 is your problem (acquisition quality):
- Audit paid channels: are your ads targeting the right keywords and audiences?
- Check organic channels: are your app store listing or search keywords attracting low-intent users?
- Measure channel quality: build a cohort analysis by channel in AppsFlyer or Adjust. Organic users often retain 2x to 3x better than paid users at the same install volume.
- Consider lowering CPI: if paid installs retain poorly, organic growth or partnerships may be higher-ROI than scaling paid ads.
If D7 is your problem (onboarding):
- Map the activation moment: in which session and in which flow does a user realize the app's core value?
- Restructure onboarding to reach activation faster: remove information that is not essential to day-one value, move advanced options to later screens.
- Personalize early: ask the user why they downloaded and surface content relevant to that reason immediately.
- Enable push notifications early: push opt-in users retain at 2x the rate of non-opt-in users. Ask for push permission after a positive action, not on screen one.
If D30 is your problem (long-term stickiness):
- Audit your push notification strategy: are you sending behavioural triggers or scheduled blasts? Triggered notifications outperform scheduled ones by 3x to 5x.
- Build habit loops: streaks (fitness), social features (leaderboards), or loss aversion (penalties for missing a day) drive return engagement.
- Reduce friction between sessions: can users access the core feature in 2 taps or 10 taps? Every additional friction point erodes day-30 retention by 1% to 3%.
Questions people ask
What is a good day-30 retention rate?
It depends on category. Top-quartile apps in fintech achieve 15% to 25% day-30 retention, while casual games consider 5% exceptional. Compare your app to category-specific benchmarks, not to all-app averages. An app in the top quartile of its category is in the top 25% of apps generally.
How do I measure retention correctly?
Retention is the share of users from a cohort (installed on day zero) who open the app at least once on a specific day or day range. Do not confuse this with DAU/MAU (daily/monthly active users), which includes all users regardless of install date. Most analytics platforms (AppsFlyer, Adjust, Firebase, Amplitude) calculate retention cohorts automatically.
Should I compare my retention to web SaaS retention benchmarks?
No. Web SaaS (subscription software) retention is typically much higher than mobile app retention because users have deeper investment and often paid upfront. A web app with 80% month-1 retention is normal; a mobile app with 80% day-30 retention is exceptional. Use mobile-specific benchmarks.
If my retention is below benchmark, does that mean my app will fail?
No. Below-benchmark retention is a fixable problem, not a death sentence. Most high-retention apps were below benchmark at launch and improved through iterations on onboarding and engagement. The key is identifying the exact leak point (D1, D7 or D30) and fixing it systematically.
How much can retention improve with optimization?
Significant improvements are typical. Rewriting onboarding to reach the activation moment faster can improve D7 retention by 20% to 30%. Adding personalization can improve D7 by another 10% to 20%. Push notification strategy optimization improves D30 by 15% to 25%. Cumulative: many apps achieve 40% to 60% retention improvement across the D1-D30 curve.
Should I optimize for D1, D7 or D30 first?
Start with D7. If your D7 is weak, optimizing D1 acquisition wastes money on users who will not stick around. If D7 is healthy but D30 is low, focus on retention levers (push, notifications, features). If all three are weak, focus on onboarding first (D7 is the highest-leverage target).
Sources
Last reviewed 11 September 2026. Formulas and benchmarks are published on the page so you can check them. This tool gives estimates, not quotes.
