Who is this for?
Isolate tutorial friction and early drops. Compare Day 1 retention to P50 benchmarks before scaling user acquisition.
Find out where players drop: onboarding, mid-term progression, or endgame decay. Model recovery paths with slider tools.
Compare different studios and games on standardized retention health metrics. Build business cases for WhisqAI integration.
What you'll get
Benchmark retention decay metrics against Q1-Q2 2026 data. Audit onboarding, ad frequency, and technical crash penalties to isolate monthly revenue leaks. Advanced diagnostic tools unlock after email.
How it works
To measure retention health, we approximate the cohort retention curve using a midpoint LT-50 method: LT = 0.5 + 3.5 × R1 + 14.5 × R7 + 31.5 × R30. The coefficients sum to 50 because Day 30 retention is treated as a flat habituation floor extending ~20 more days — this represents the total active player-days an average install will produce over the LTV-50 horizon, aligning with how most studios report lifetime value.
We establish steady-state metrics by calculating daily installs required to maintain your DAU: Installs = DAU / LT. By substituting your retention metrics with the genre baseline (P50) benchmarks, we identify the increase in player-days and calculate the monthly revenue delta: Leakage = DAU × (ΔLT / LT) × ARPDAU × 30.
The multi-factor risk score compares your baseline retention against the cohort percentiles, adding penalties for high crash rates, low tutorial completions, early first-session ad exposure, and heavy interstitial frequency.
Inputs explained
| Field | What it means | Why it matters |
|---|---|---|
| Genre | Selects the benchmark retention percentiles (D1, D7, D30) and default engagement levels. | Determines the baseline comparison cohort used to calculate relative risk score. |
| Platform Split | Percentage of active users running iOS vs. Android. | Android retention runs ~15% lower than iOS globally. The benchmark baseline is interpolated linearly. |
| DAU | Daily Active Users — unique players active in the game per day. | Scales the absolute revenue leakage dollar amounts. |
| Day 1 / 7 / 30 Retention | Percentage of installs that launch the game exactly 1, 7, or 30 days after install. | Constructs the retention survival curve used to integrate active player lifetime. |
| Crash Rate (%) | Percentage of game sessions that end in a crash or ANR (App Not Responding). | Crashes above 0.25% apply a penalty scaling up to +30% Churn Risk. |
| Tutorial Completion (%) | Percentage of installing users who complete the initial onboarding tutorial. | Completion below 85% flags onboarding friction and applies up to +15% Churn Risk. |
| Ad Grace Period (s) | Seconds of gameplay allowed before the first ad impression is served. | Serving ads before 180 seconds in early loops applies up to +15% Churn Risk. |
| Interstitials / Session | Average number of full-screen interstitial ads shown per user session. | Exceeding 3 interstitials per session applies up to +20% ad-fatigue risk penalty. |
Retention leaks
Our diagnostic engine attributes monthly leakage across these five common friction areas. Each is quantified in dollars to help your team prioritize sprint objectives.
Day 1 retention drops are heavily correlated with onboarding obstacles. Heavy initial assets downloads, long load times, confusing tutorials, and early ad pings are primary drivers. Our engine substitutes the industry benchmark Day 1 retention to isolate onboarding losses, showing that lifting Day 1 to the median baseline typically resolves 30–45% of total lifecycle revenue leakage.
Serving more than 3 interstitials per session causes rapid player drop-out. While interstitials yield good eCPM, the long-term compounding loss of players exceeds the short-term ad gain. Our model adds a risk penalty when ad density exceeds 3 per session, and recommends frequency capping to protect active player lifetimes.
Serving ads during the first 3 minutes (180s) of onboarding severely disrupts player habituation. Players require at least one complete core loop (e.g. finishing a level, equipping an item) to build engagement before facing commercial interruption. Delaying ad placements improves Day 1 retention by 4–8%.
Crash rates above 0.25% indicate system instability. A crash during a player's first session has a 70% correlation with permanent churn. Our scoring engine applies a steep penalty of up to 30% for high crash rates, helping engineering teams justify performance optimization sprints.
When Day 7 retention is healthy but Day 30 falls off, the game lacks long-term progression anchors. This usually indicates a weak endgame, static level design, or lack of live events. Deepening progression loops can recover substantial habituation-phase revenue.
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