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Konstantin Khalyushev
ENRUESPT

Retention

12-month subscription retention

Of paid subscriptions started, the share still active twelve months later. Only meaningful alongside the billing period.

Always read alongside its billing period. A 12-month figure for weekly plans requires surviving ~52 renewals; for annual plans it requires surviving one. Quoting a blended number across billing periods is meaningless.

Denominator
paid subscriptions
Cohort
a subscription cohort
Time horizon
12 months
Sources
4
Observations
26

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Can these be compared?

Not directly comparable

These measure different quantities. Differencing them produces a number that means nothing.

These observations cannot be combined into one figure, so they are shown separately. Different billing periods (weekly vs monthly). For retention this is disqualifying, because surviving a fixed span costs a different number of renewal decisions on each plan: roughly 52 on a weekly plan, 12 on a monthly one, 1 on an annual one.

RevenueCat — State of Subscription Apps 2026

Published 2026-03-19 · 17 observations

Sample
Over 115,000 apps using RevenueCat for in-app subscription management, covering more than $16 billion in revenue and more than a billion transactions. Apps must have active subscription revenue and meet a minimum install or revenue threshold.
How it is aggregated
App-level metrics aggregated across apps and reported as medians and quartiles (Q1/P25, median/P50, Q3/P75, P90). Each app is a data point regardless of size.
Measurement period
Target time frame 2025; older data used where a metric needs it (e.g. third annual renewal rates).
Denominator
paid subscriptions

1%–2%

Median

  • Weekly

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time. A weekly plan must survive roughly 52 renewals to count as retained at 12 months.
As published
“Weekly Y1: median 1-2%, essentially total churn.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by plan duration

6%–14%

Median

  • Monthly

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time. Requires 12 monthly renewals.
As published
“Monthly Y: median 6-14%, top quartile 11-25%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by plan duration

20%–40%

Median

  • Annual

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time. Requires a single annual renewal, which is why annual figures sit an order of magnitude above weekly ones.
As published
“Yearly Y1: median 20-40%, top quartile 32-59%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by plan duration

39%

Median

  • Travel
  • Annual

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Travel leads yearly (39%); Productivity lags (23%).”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by plan duration

23%

Median

  • Productivity
  • Annual

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Travel leads yearly (39%); Productivity lags (23%).”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by plan duration

28%

Median

  • Annual

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
Comparability
The 2025 edition reported 44.1% for annual Y1 retention on a nominally identical metric. A 16-point gap between consecutive editions is a definition or population change, not a market move — do not draw a trend through the two.
As published
“Yearly: 31% → 28% median (-3%).”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Year 1 retention compared to prior period by plan duration

8%

Median

  • Monthly

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Monthly: 10% → 8% median (-2%).”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Year 1 retention compared to prior period by plan duration

1.2%

Median

  • Weekly

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Weekly: 1.7% → 1.2% (-0.5%).”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Year 1 retention compared to prior period by plan duration

36%

Median

  • Annual
  • Low-priced apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Low-priced yearly: 36% median, top quartile 53%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by pricepoint

26%

Median

  • Annual
  • Mid-priced apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Mid-priced yearly: 26% median, top quartile 41%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by pricepoint

23%

Median

  • Annual
  • High-priced apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“High-priced yearly: 23% median, top quartile 34%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by pricepoint

28%

Median

  • Annual
  • Freemium

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
Comparability
The report's own conclusion: access method is not a retention differentiator. The 1-point gap against hard paywall is noise.
As published
“Freemium yearly: 28% median, range 17-58%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by access methods

27%

Median

  • Annual
  • Hard paywall

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Hard paywall yearly: 27% median, range 17-54%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19Retention · Retained subscribers after 1 year by access methods

21.1%

Median

  • Annual
  • AI apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Annual: AI 21.1% vs. non-AI 30.7%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19AI vs. non-AI · Retained subscribers after 12 months

30.7%

Median

  • Annual
  • Non-AI apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Annual: AI 21.1% vs. non-AI 30.7%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19AI vs. non-AI · Retained subscribers after 12 months

6.1%

Median

  • Monthly
  • AI apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Monthly: AI 6.1% vs. non-AI 9.5%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19AI vs. non-AI · Retained subscribers after 12 months

9.5%

Median

  • Monthly
  • Non-AI apps

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Subscription-level, not user-level: a subscription is retained once it has accumulated enough paid renewals to cover the elapsed time.
As published
“Monthly: AI 6.1% vs. non-AI 9.5%.”

RevenueCat — State of Subscription Apps 2026Published 2026-03-19AI vs. non-AI · Retained subscribers after 12 months

Adapty — State of In-App Subscriptions 2026

Published 2026-01-01 · 2 observations

Sample
Subscription data from more than 16,000 apps representing $3 billion in subscription revenue.
How it is aggregated
Conversion benchmarks are computed as app-level rates (trials or purchases divided by installs) within a segment, then aggregated as the AVERAGE of per-app rates. Medians and percentiles are reported alongside averages on some charts.
Measurement period
2025, with 2023 comparisons on selected charts.
Denominator
paid subscriptions

14%

Average

  • Productivity
  • All plan durations

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
Computed as app-level rates within the segment, then aggregated as the average of per-app rates. Averages of per-app rates are pulled upward by strong outliers, so this sits above a median of the same population. Blended across plan durations, unlike RevenueCat's figures which are always split by billing period.
Comparability
A blend across weekly, monthly and annual plans. Because 12-month retention differs by an order of magnitude between those durations, this number is not comparable with any duration-specific figure.
As published
“Productivity retains best overall, with an average of 14% of users staying after one year.”

Adapty — State of In-App Subscriptions 2026Published 2026-01-01Conversion highlights · Retention by plan duration and trial strategy

22.1%

Average

  • Utilities
  • Annual

Evidence Sample, statistic and metric definition all stated by the publisher.

Methodology
Computed as app-level rates within the segment, then aggregated as the average of per-app rates. Averages of per-app rates are pulled upward by strong outliers, so this sits above a median of the same population.
Comparability
Annual-only, so this one is comparable in kind with RevenueCat's annual 12-month retention (20-40% median across categories), allowing for the mean-vs-median difference.
As published
“On annual plans alone, Utilities lead in one-year retention at 22.1%.”

Adapty — State of In-App Subscriptions 2026Published 2026-01-01Conversion highlights · Retention by plan duration and trial strategy

RevenueCat — State of Subscription Apps 2025

Published 2025-04-01 · 3 observations

Sample
Apps using RevenueCat for in-app subscription management, segmented by category, platform, region, price point and engagement strategy.
How it is aggregated
App-level metrics reported as bottom quartile, median, upper quartile and top 10% of app performance.
Measurement period
Primarily 2024.
Denominator
paid subscriptions

44.1%

Median

  • Annual

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
Comparability
METHODOLOGY BREAK. The 2026 edition reports 28% median annual Year 1 retention against 44.1% here. A 16-point gap between consecutive editions of the same report indicates a definitional or population change. Do not draw a trend line through these two points.
As published
“Yearly plans maintain the highest retention rates, but retention has slightly declined from 47.1% in the previous period to 44.1% in this period.”

RevenueCat — State of Subscription Apps 2025Published 2025-04-01Retention · Retained subscribers after 1 year by plan duration

17%

Median

  • Monthly

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
Comparability
METHODOLOGY BREAK against the 2026 edition's 8% monthly figure. Same caveat as the annual series.
As published
“Monthly plan retention decreased from 18.8% to 17.0%”

RevenueCat — State of Subscription Apps 2025Published 2025-04-01Retention · Retained subscribers after 1 year by plan duration

3.4%

Median

  • Weekly

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
As published
“Weekly plan retention remains the lowest, declining from 4.2% to 3.4%”

RevenueCat — State of Subscription Apps 2025Published 2025-04-01Retention · Retained subscribers after 1 year by plan duration

RevenueCat — State of Subscription Apps 2024

Published 2024-04-01 · 4 observations

Sample
Apps using RevenueCat for in-app subscription management, broken down by pricing, packaging, localisation, conversion and retention.
How it is aggregated
App-level metrics reported as quartiles and medians across the app population.
Measurement period
Primarily 2023, with 2022 comparisons.
Denominator
paid subscriptions

24%

Median

  • Shopping
  • Monthly

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
As published
“Shopping apps have the highest Year 1 retention for monthly subscriptions at 24%. That's 4x the 6% Year 1 retention rate of the Social & Lifestyle category”

RevenueCat — State of Subscription Apps 2024Published 2024-04-01Retention · Year 1 retention by category

6%

Median

  • Social & Lifestyle
  • Monthly

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
As published
“That's 4x the 6% Year 1 retention rate of the Social & Lifestyle category”

RevenueCat — State of Subscription Apps 2024Published 2024-04-01Retention · Year 1 retention by category

27%

Median

  • North America
  • Annual

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
As published
“While North America accounts for the majority of revenue for subscription apps, retention is middling at 27% for annual, 15% for monthly, and 4% for weekly”

RevenueCat — State of Subscription Apps 2024Published 2024-04-01Retention · Year 1 retention by region

42%

Median

  • Japan
  • Annual

Evidence One of sample, statistic or definition is missing or ambiguous.

Methodology
The edition does not restate its retention definition, so it is not certain this is built the same way as the 2026 edition's subscription retention.
As published
“With a whopping 42% for annual, 26% for monthly, and 10% for weekly subscriptions, Japan leads the world in Year 1 retention”

RevenueCat — State of Subscription Apps 2024Published 2024-04-01Retention · Year 1 retention by region

Pairwise comparability

The verdict below is produced by a fixed set of rules, not a model. It looks at the metric definition, the denominator, the cohort, the statistic, the segment, the dataset and the publication dates, and reports every reason it found.

RevenueCat 2026 1%–2%Adapty 2026 14%

Roughly comparable

The same thing is being measured, but at least one condition differs. Read the direction, not the gap.

  • One is a median, the other a average. In a skewed distribution — and app performance is heavily skewed — a mean sits above the median, so the gap between these numbers partly reflects the statistic rather than the products.
  • Different category scope: not broken out vs Productivity. An all-categories figure is not a substitute for the category you are in.
  • Different billing periods: weekly vs not applicable.
  • Different sub-segments: not broken out vs all plan durations.
  • Different datasets: RevenueCat (115,000+ apps, $16bn in revenue) versus Adapty (16,000+ apps, $3bn in subscription revenue). Each publisher sees only its own customers, so the app populations differ.
  • Different aggregation methods. RevenueCat 2026: medians and quartiles across apps. Adapty 2026: the average of per-app rates.

RevenueCat 2026 1%–2%Adapty 2026 22.1%

Not directly comparable

These measure different quantities. Differencing them produces a number that means nothing.

  • Different billing periods (weekly vs annual). For retention this is disqualifying, because surviving a fixed span costs a different number of renewal decisions on each plan: roughly 52 on a weekly plan, 12 on a monthly one, 1 on an annual one.

RevenueCat 2026 1%–2%RevenueCat 2026 1.2%

Directly comparable

Same definition, same statistic, same segment, same dataset. These numbers can be differenced.

  • Same metric definition: 12-month subscription retention, measured over paid subscriptions.
  • Both are the median.
  • Both from RevenueCat State of Subscription Apps 2026, so the dataset and method are identical.

RevenueCat 2026 6%–14%Adapty 2026 14%

Roughly comparable

The same thing is being measured, but at least one condition differs. Read the direction, not the gap.

  • One is a median, the other a average. In a skewed distribution — and app performance is heavily skewed — a mean sits above the median, so the gap between these numbers partly reflects the statistic rather than the products.
  • Different category scope: not broken out vs Productivity. An all-categories figure is not a substitute for the category you are in.
  • Different billing periods: monthly vs not applicable.
  • Different sub-segments: not broken out vs all plan durations.
  • Different datasets: RevenueCat (115,000+ apps, $16bn in revenue) versus Adapty (16,000+ apps, $3bn in subscription revenue). Each publisher sees only its own customers, so the app populations differ.
  • Different aggregation methods. RevenueCat 2026: medians and quartiles across apps. Adapty 2026: the average of per-app rates.

RevenueCat 2026 6%–14%Adapty 2026 22.1%

Not directly comparable

These measure different quantities. Differencing them produces a number that means nothing.

  • Different billing periods (monthly vs annual). For retention this is disqualifying, because surviving a fixed span costs a different number of renewal decisions on each plan: roughly 52 on a weekly plan, 12 on a monthly one, 1 on an annual one.

RevenueCat 2026 20%–40%Adapty 2026 14%

Roughly comparable

The same thing is being measured, but at least one condition differs. Read the direction, not the gap.

  • One is a median, the other a average. In a skewed distribution — and app performance is heavily skewed — a mean sits above the median, so the gap between these numbers partly reflects the statistic rather than the products.
  • Different category scope: not broken out vs Productivity. An all-categories figure is not a substitute for the category you are in.
  • Different billing periods: annual vs not applicable.
  • Different sub-segments: not broken out vs all plan durations.
  • Different datasets: RevenueCat (115,000+ apps, $16bn in revenue) versus Adapty (16,000+ apps, $3bn in subscription revenue). Each publisher sees only its own customers, so the app populations differ.
  • Different aggregation methods. RevenueCat 2026: medians and quartiles across apps. Adapty 2026: the average of per-app rates.

RevenueCat 2026 1%–2%RevenueCat 2025 17%

Not directly comparable

These measure different quantities. Differencing them produces a number that means nothing.

  • Different billing periods (weekly vs monthly). For retention this is disqualifying, because surviving a fixed span costs a different number of renewal decisions on each plan: roughly 52 on a weekly plan, 12 on a monthly one, 1 on an annual one.

RevenueCat 2026 20%–40%Adapty 2026 22.1%

Roughly comparable

The same thing is being measured, but at least one condition differs. Read the direction, not the gap.

  • One is a median, the other a average. In a skewed distribution — and app performance is heavily skewed — a mean sits above the median, so the gap between these numbers partly reflects the statistic rather than the products.
  • Different category scope: not broken out vs Utilities. An all-categories figure is not a substitute for the category you are in.
  • Different datasets: RevenueCat (115,000+ apps, $16bn in revenue) versus Adapty (16,000+ apps, $3bn in subscription revenue). Each publisher sees only its own customers, so the app populations differ.
  • Different aggregation methods. RevenueCat 2026: medians and quartiles across apps. Adapty 2026: the average of per-app rates.
Editions over time

Each edition of a report is stored separately, and older observations are never overwritten. A series is only shown as a trend when the underlying method held still.

Methodology break

The publisher's figure moved further between editions than the market plausibly did, which points to a change in how the number is built.

Not shown as a trend: the method changed between these editions.

RevenueCat — State of Subscription Apps

  1. Edition 2024

    2024-04-01

    • 24%Medianmonthly · Shopping
    • 6%Medianmonthly · Social & Lifestyle
    • 27%Medianannual · North America
    • 42%Medianannual · Japan

  2. Edition 2025

    2025-04-01

    • 44.1%MedianannualMethodology break
    • 17%MedianmonthlyMethodology break
    • 3.4%Medianweekly

  3. Edition 2026

    2026-03-19

    • 1%–2%Medianweekly
    • 6%–14%Medianmonthly
    • 20%–40%Medianannual
    • 39%Medianannual · Travel
    • 23%Medianannual · Productivity
    • 28%Medianannual
    • 8%Medianmonthly
    • 1.2%Medianweekly
    • 36%Medianannual · Low-priced apps
    • 26%Medianannual · Mid-priced apps
    • 23%Medianannual · High-priced apps
    • 28%Medianannual
    • 27%Medianannual
    • 21.1%Medianannual · AI apps
    • 30.7%Medianannual · Non-AI apps
    • 6.1%Medianmonthly · AI apps
    • 9.5%Medianmonthly · Non-AI apps

Related metrics

  • Day 30 install retention

    Of everyone who installed, the share still opening the app on day 30. This counts app opens, not payments — it is not subscriber retention.

  • Retention after the Nth renewal

    Of subscribers who converted to paid, the share still subscribed after the Nth renewal, counted cumulatively.

RetentionCompare your productMethodology and sourcesAll benchmarks