サブスクリプションアプリの現状 2026年版レポート

データセットの概要

This report draws on subscription app performance data from a wide range of apps that use RevenueCat’s platform. Our goal is to provide a comprehensive snapshot of how apps are performing under different scenarios, across iOS, Android and web ecosystems.

• Scope of apps included: we included apps that have active subscription revenue, meet a minimum threshold of installs or revenue (to ensure statistically meaningful findings), and have integrated RevenueCat for in-app subscription management.
• Time frame: the target time frame for metrics in this report is 2025. In some cases we have pulled older data to run certain calculations (we can’t calculate third renewal rate for annual subscriptions bought in 2024, for example)
• Size and composition: we analyzed over 115,000 apps across all app categories, covering more than $16 billion in revenue across more than a billion transactions. The apps vary in scale, from indie teams to mid-size organizations and large publishers.
• Revenue channels: the dataset includes both apps that primarily generate revenue from in-app subscriptions and those that generate a portion of revenue from subscriptions alongside other revenue channels.
• Anonymity: all data is anonymized and aggregated, ensuring that no single app’s performance metrics are individually identifiable. The findings are presented as aggregated performance benchmarks across segments, categories, and platforms.

匿名化とデータプライバシ

To preserve the confidentiality of individual apps, we apply controls to endeavor that if a segment has too few apps, results are either omitted from the report or combined with a larger segment to avoid the possibility of inferring any single app’s data. Throughout this report, numbers represent aggregated totals, averages, medians, quartiles, or other summary statistics. No app-specific or developer-specific details are ever disclosed.

統計上の定義

Throughout this report we aim to use clear, accessible language and minimize unnecessary jargon. However, some key statistical terminology is used at times.

The following measures of central tendency and spread have been used to illustrate app performance.

• Bottom quartile (Q1): The value below which 25% of the dataset falls. An app that falls into the bottom quartile is among the lower 25% of performers on that metric.
• Median (Q2): The middle value, with half of the data above and half below. When comparing your own metrics to the median, you can see if you are performing above or below the midpoint of the industry.
• Upper quartile (Q3): The value above which 25% of the dataset falls. An app in the upper quartile is among the top 25% of performers for that metric.
• P90: The 90th percentile. This indicates the point at or above which 10% of the dataset lies. An app at P90 is outperforming 90% of apps in that particular metric (aka, crushing it).

チャートの見方

We often use candlestick (box-and-whisker–style) charts to show the distribution of a given performance metric (e.g. RPI, LTV) across apps. The ‘box’ represents the bulk of distribution, the interquartile range, while the ‘whiskers’ represent the lower and upper bounds of performance.

Percentile mapping

• Lower ‘whisker’: marks P10 (10th percentile), the bottom 10% of app performance.
• Bottom of the ‘box’: marks P25 (25th percentile) and represents Q1 (bottom quartile) — apps below this make up the lowest 25% of performers.
• Marker inside the ‘box’: marks P50 (50th percentile) and represents Q2 (median) — this is the midpoint of app performance.
• Top of the ‘box’: marks P75 (75th percentile) and represents Q3 (upper quartile) — apps above this fall into the top 25% of performers.
• Upper ‘whisker’: marks P90 (90th percentile) — apps here or above are the highest performers.
• Any points outside P10–P90 indicate unusually low or high performance outliers.

RevenueCatに基づかないデータ

While the majority of the metrics in this report derive from aggregated app subscription data (via RevenueCat), two charts within the State of the market chapter feature data not from RevenueCat. This data has been provided by Appfigures, and is indicated on the relevant pages.

含まれる指標

Below are some of the included metrics in this report that benefit from some additional context. Each metric is calculated consistently across all apps to allow for accurate comparison.

• Realized lifetime value (RLTV) per payer: the net value of an average paying user over a specific period of time, including initial subscriptions, renewals, reactivations, expansion, and one-time purchases.
• Revenue per install (RPI): total revenue earned divided by total installs. This metric highlights how well an app monetizes each new user.
• Active renewal rate: the share of renewals that are done by subscribers that were active in the second half of the previous subscription period (in case of a monthly subscription, this asks if the user was seen in the app in the ~15 days prior to the renewal).
• Reactivation rate: the share of churned subscribers that become active in the 12 months following a churn event.
• MRR growth rate (year-on-year): the percentage change in an app’s monthly recurring revenue between two point-in-time snapshots (end of the previous year vs. end of the current year). MRR is normalized to a 30-day basis regardless of plan duration.
• Download-to-paid conversion rate (D35): the share of installs that result in at least one paid subscription within 35 days of the install date. This metric captures how effectively an app converts new users into paying customers in the first ~5 weeks.
• Download-to-trial conversion rate: the share of installs that start a free trial within 30 days of the download date. This metric is calculated for apps that use a trial-based acquisition strategy.
• Trial-to-paid conversion rate: the share of free trial starts that convert into a paid subscription.
• Retention rate: the share of paid subscriptions that remain active after a given time period (e.g. 6 months, 12 months). A subscription is considered ‘retained’ if it has accumulated enough paid renewals to cover the elapsed time — for example, 12 monthly renewals for 12-month retention, or 1 annual renewal for yearly plans. This is a subscription-level metric, not a user-level metric.
• Renewal rate (1st, 2nd, 3rd): the share of subscriptions that successfully renew at each sequential billing cycle. This differs from retention rate, which measures cumulative survival over time.
• Refund rate: the share of paid subscriptions that are refunded during their first billing period.
• Monthly trailing revenue (MTR): a 30-day rolling revenue figure at a specific point in time, used to measure an app’s revenue run-rate. This is the basis for the ‘monthly revenue 1 year after launch’ and ‘time to revenue milestones’ metrics in this report.

対象セグメント

To provide nuanced insights, we break down certain metrics by segments. These segments offer a closer look at how different development choices and distribution methods might impact subscription metrics.

• Access method: how users primarily access the app (freemium, via a hard paywall).
• App development framework: the primary technology stack used for app development (e.g., native iOS/Android, Flutter, React Native, Other).
• Pricepoint: how an app’s average pricepoint compares to the rest of the measured apps (below average, average, above average).
• Developer HQ: the (estimated) region the app developer team is based in.
  • Regions are bucketed together: Asia-Pacific, IN/SEA (India & Southeast Asia), Latin America, MEA (Middle East & Africa), North America, Western Europe, ROW (rest of world), all regions.
• Geography: the region users are based in.
• AI vs. non-AI app: whether an app uses AI/ML models for its primary value (e.g. generative AI, chatbots and AI assistants, predictive analytics, AI photo/video editing, AI writing tools).
  Note: apps not categorized as ‘AI apps’ may still use basic ML or have minor AI features.

地域の定義

• North America: United States, Canada
• Western Europe: United Kingdom, Germany, France, Spain, Italy, Netherlands, Sweden, Norway, Finland, Belgium, Austria, Switzerland, Denmark, Ireland, Portugal, Luxembourg, Liechtenstein, Iceland, Monaco, Malta
• Asia-Pacific: Japan, South Korea, Australia, New Zealand
• Latin America: Brazil, Mexico, Argentina, Chile, Colombia, Peru
• MEA (Middle East & Africa): Saudi Arabia, South Africa, United Arab Emirates, Egypt, Nigeria, Kenya, Turkey
• IN/SEA (India & Southeast Asia): India, Indonesia, Thailand, Philippines, Vietnam, Malaysia, Singapore

カテゴリ分類 (App Store and Google Play)

The App Store (iOS) and Google Play (Android) each have numerous categories. For clearer analysis, we’ve aggregated or ‘bucketed’ closely-related categories under common labels:

• Utilities: includes Weather, Reference, Utilities, Finance, Tools, and more.
• Health & Fitness: includes Health & Fitness, Medical.
• Media & Entertainment: includes Entertainment, Music, News, Magazines & Newspapers, Sports, and more.
• Education: includes Education, and Educational.
• Productivity: includes Graphics & Design, Art & Design, Developer tools.
• Social & Lifestyle: includes Lifestyle, Social Networking, Social, Dating.
• Gaming: includes Games, Puzzle, Casual, Word, Simulation, Board, and more.
• Photo & Video: Includes Photo & Video, Photography, Video Players & Editors.
• Business: Includes Business.
• Travel: Includes Travel, Travel & Local.
• Shopping: Includes Shopping.

We have done this to simplify comparisons — especially when certain official categories have too few apps to provide stable, anonymized benchmarks.