Cohort Retention &
LTV Simulator.
Fit a mathematical power-law decay curve R(t) = a · t−b to model user lifespan. Visualize retention decay alongside cumulative LTV buildup over time to evaluate monetization health.
Use this before a UA budget review or a monetization audit, when you need a defensible cohort LTV number without exporting raw retention data into a spreadsheet. It is built for operators who already have D1/D7/D30 (or a fuller retention curve) and want to see how sensitive LTV is to shifts in the decay rate.
Model Parameters
Simulator Outcomes
Anatomy of the decay curve.
Every cohort's lifetime value is decided across four distinct friction gates. When retention decays, the diagnostic begins by isolating the gate that is failing.
Onboarding Drop-off
The vertical drop on Day 1. Typically driven by tutorial friction, device performance, value-proposition mismatch, or broken initial user flows.
Habit Formation
Early-stage churn. Driven by content depth, early progression pace, session intervals, and failure to establish the secondary core loop.
Mid-Term Stability
The curve flattens. Users choose to make the product a regular habit. Influenced by feature depth, personalization, and social hooks.
Steady-State Tail
The loyal tail. Driven by live operations, elder game economy, communities, and high-affinity habits. This tail supports direct scale.
Is your curve decaying too fast?
A 5% shift in early retention can compound into a 25%+ increase in cohort LTV. If your curves drop off too steeply, optimizing monetization or spending on acquisition is leaking water. We diagnose these curves, rewrite progression systems, and audit ad networks to secure the tail.