2025年订阅应用行业现状

RevenueCat’s annual overview of in-app subscriptionperformance benchmarks, based on the world’slargest subscription app data set. Table ofContents $0.63+ 30% 35%+ Subscriptions aren’t enough anymore– 35% of appsnow mix subscriptions with consumables or lifetimepurchases. Gaming (61.7%) and Social & Lifestyle(39.4%) are leading the charge. AI apps print money- but only if they stand out –Most AI apps see revenue per install above $0.63after 60 days, matching only Health and Fitness($0.63), at double the overall median of $0.31. But AIalone won’t drive success, differentiation does. Churn hits hard and fast– Nearly 30% of annualsubscriptions are canceled in the first month. If youdon’t win them back over, at the end of that first year,they’re gone. Retention starts on day one. 36% 400x+ Low prices keep users locked in– Most apps withcheap annual plans keep up to 36.0% of userssubscribed after a year. High-priced monthly plans?Just 6.7% stick around. The gap between winners and the rest is growing– At$8,888 the top 5% of newly launched apps make over400x as much money after their first year, comparedto the bottom 25%, who make no more than $19. Thisgap has grown significantly since last year’s 200x. Twelve months ago, I joked about how AI was goingto change everything for subscription apps. It did,but in ways I didn’t fully appreciate at the time. Jacob EitingBig Boss & Co-Founder Building a successful subscription app is easier saidthan done—even in the era of vibe-coded, one-shottedapps. My hope is that you’ll find the data you need inthese pages to handle your next big challenge, whetherthat’s retention, pricing, or cracking into a new market. AtRevenueCat, giving away information is one of our top 7favorite activities, and we’re excited to keep pushing theenvelope on what we can learn—and share—about thisthing called apps. AI-powered apps are outperforming a bunch of legacycategories already, and on top of that, AI-assisted developmenthas made launching (and iterating) an app feel more like aweekend hobby project. Running a subscription business isstill hard, but AI is letting us do more, faster and has created anincredible amount of disruption that now is a better time thanever to build and invest. Of course, it wouldn’t be a State of Subscription Apps report withoutus going completely overboard on data. Last year, I warned you it was“dense.” So this time around, we doubled down—literally. Twice thepage count, more charts, and more slicing and dicing: we broke downmetrics by category, platform, region, price point, engagement strategy,and probably a few other segments you never knew you needed. Theresult is a monstrous trove of insights that should keep you busy untilnext year’s version which will probably come in a 3 volume set. Overview of the Data Set Anonymization and Data Privacy METHODOLOGY This report draws on subscription app performancedata from a wide range of apps that use RevenueCat’splatform. Our goal is to provide a comprehensivesnapshot of how apps are performing under differentscenarios, across both the iOS and Android ecosystems. To preserve the confidentiality of individual apps, we applystrict controls to ensure that if a segment has too few apps,results are either omitted from the report or combined witha larger segment to avoid the possibility of inferring anysingle app’s data. Throughout this report, numbers representaggregated totals, averages, medians, quartiles, or othersummary statistics. No app-specific or developer-specificdetails are ever disclosed To achieve this: →Scope of Apps Included:We included apps that have activesubscription revenue, meet a minimum threshold of installsor revenue (to ensure statistically meaningful findings),and have integrated RevenueCat for in-app subscriptionmanagement →Time Frame:The target time frame for metrics in this reportis 2024. In some cases, we had to pull older data to runcertain calculations (we can’t calculate third renewal ratefor annual subscriptions bought in 2024, for example)→Size and Composition:We analyzed over 75,000 appsacross all categories (e.g., Health & Fitness, Productivity,Education), covering more than $10 billion in revenue acrossmore than a billion transactions. They vary in scale, fromindie teams to mid-size organizations to large publishers.The set includes both apps that primarily generate revenuefrom in-app subscriptions and those that generate aportion of revenue from subscriptions alongside otherrevenue channels. Statistical Definitions(Bottom Quartile, Median, Upper Quartile, P90) We use several measures of central tendency and spreadto illustrate app performance: →Bottom Quartile (Q1):The value below which 25% of thedataset falls. An app that falls into the bottom quartile isamong the lower 25% of performers on that metric→Median (Q2):The middle value, with half of the data aboveand half below. When comparing your own metrics to themedian, you can see if you are performing above or b