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Retention Curves

Every retention curve falls. The only question that matters is whether it flattens, and how high.

Analytics SQL · Intermediate · ~5 min

In plain English

New members at a gym. Most stop coming in weeks; the question is whether the line ever goes flat, and how many people are on it when it does.

Why it's worth your time

The height of the plateau decides whether acquisition compounds or whether you're renting users forever.

If you remember three things

  • Every curve falls; only the flattening and its height matter
  • The initial drop is onboarding; the plateau is product-market fit
  • N-day, unbounded and bracket retention give very different curves

Overview

Plot the share of a cohort still active against days since signup and you get a curve that drops steeply, then bends. The steep part is people who were never going to stay. The bend is the product finding its actual users, and the height of the plateau is the fraction for whom the product works. That plateau is the single most predictive number in a consumer business, because growth compounds only when the curve flattens above zero — a curve that decays to nothing means every new user must be replaced forever, and acquisition becomes a treadmill rather than an investment.

In an interview

A retention curve is the share of a cohort still active by age. It always falls; what matters is whether it flattens and at what level. A flat tail means a real core of users, so acquisition compounds. A curve decaying to zero means you are renting users. Improving the plateau beats improving day-1, because the plateau multiplies every future cohort.

Production defaults

Definition
pick one per product and label it on every chart
Axis
plot by age, never by calendar date
Headline
track the plateau; day-1 is a diagnostic, not a goal

What breaks

  • Curve oscillates wildly — N-day retention on a weekly-use product. Switch to bracket or unbounded retention.
  • Curve looks flat but LTV is falling — Retention flat, revenue per active falling. Plot the revenue version alongside.

Watch it explained

#Tableau - Calculate Customer Retention & Cohort Analysis — Andy Kriebel, 5:20

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