MONETIZATION UTILITY · SHEET 14

Retention Curve
Benchmark Generator.

Compare your cohort retention curves against upper-quartile (Top 25%), median, and trailing benchmarks. Fit custom data points to project active user half-life and average cohort lifespan.

This is for teams deciding whether a retention curve can support planned UA spend before scaling further. Use it to check whether your Day 1, Day 7, and Day 30 numbers sit above or below genre peers before committing marketing budget to a cohort that may not retain long enough to pay back.

FILE · RETENTION_14

Cohort Inputs

01 · Custom Day 1 Retention %
02 · Custom Day 7 Retention %
03 · Custom Day 30 Retention %
04 · Custom Day 90 Retention %

Retention Benchmarks Outcomes

User Half-Life
Day 0.0
The day when exactly 50% of Day 1 active users have churned
Projected D360 Churn
0.0%
Estimated percentage of users lost after 1 full year
Avg User Lifespan
0.0 Days
Average active days generated per install in the first 360 days
COHORT RETENTION DECAY: CUSTOM VS. INDUSTRY BENCHMARKS OVER 360 DAYS
Custom Curve
Top 25% (Upper Quartile)
Top 50% (Median)
Bottom 25% (Lower Quartile)
DAY 30 RETENTION GAUGE: POSITION RELATIVE TO CATEGORY QUARTILES
Lagging Zone (< Median)
On Par Zone (Median → Top 25%)
Outperforming Zone (> Top 25%)
02Retention Dynamics

The mechanics of cohort decay curves in-depth.

Retention is the core validation of product-market fit. Programmatic marketing models fail if the product lacks organic lifetime retention depth.

INFLECTION POINT · D1-D7

Onboarding Churn

The transition from Day 1 to Day 7 represents onboarding friction. A steep drop here indicates bad tutorial layouts, poor performance, or misaligned acquisition traffic targeting.

Friction Phase: Initial Activation
INFLECTION POINT · D7-D30

Habit Formation Loop

Day 7 to Day 30 retention tracks weekly recurring engagement. Teams must establish progression loops (e.g., daily quests, battle passes) to pull users back into the core gameplay.

Friction Phase: Engagement Habit
INFLECTION POINT · D30-D90

Core Meta Sinks

Day 30 to Day 90 decay is governed by meta systems. Products with shallow item sinks, repetitive cycles, or no end-game content suffer rapid churn, limiting spend ceilings.

Friction Phase: System Depth
COHORT VALUE · LIFETIME

The Power-Law Tail

A small percentage of highly retained users (the "tail") generates the majority of revenue. Optimizing the curve's long-term power-law slope ($b$) increases average cohort lifespan.

Friction Phase: Long-term Tail
RETENTION DESIGN & COHORT AUDIT

Flatten your cohort retention curves and scale LTV.

Mobile apps scaling marketing campaigns often hit a ceiling because their cohort retention decays too rapidly. We audit your onboarding steps, review your system pacing, build cohort analysis models, and optimize progression loops to extend active player lifetimes.

Book a Retention Strategy Sprint
FAQ

Common questions.

What does this calculator do?

It compares your app's D1, D7, D30, and D90 retention against top-quartile, median, and bottom-quartile benchmarks across six genre categories, then fits a projected 360-day retention curve to estimate user half-life, projected Day 360 churn, and average active lifespan per install.

What inputs do I need?

An Industry Benchmark Category (Casual Match-3 Puzzle, Midcore RPG/Strategy, Social Casino, Hypercasual, Utility/Health Subscription, or Hybridcasual Action), plus your own Day 1, Day 7, Day 30, and Day 90 retention percentages.

How are the benchmark curves generated?

The generator fits a piecewise power-law decay curve (retention = a × day^-b) separately across the Day 1-7, Day 7-30, and Day 30-90 segments of your four input points, then extrapolates that trend out to Day 360. Half-life is the day your curve crosses 50% of your Day 1 value; average lifespan is the area under the fitted curve; and Day 360 churn is the projected drop from Day 1 to Day 360. The same fitting method is applied to each genre's top-quartile, median, and bottom-quartile figures for comparison.