---
title: Retention Curves: How to Read Drop and Plateau
description: A retention curve plots one cohort's retention against Day N. Read the early drop, the flattening point and smile curves, and why shape depends on definition.
url: https://retentioncohorts.com/learn/read-a-retention-curve/
---

[Home](https://retentioncohorts.com/) / [Guides](https://retentioncohorts.com/learn/) / How to read a retention curve

# How to read a retention curve

A retention curve plots one cohort's retention against Day N. Read the early drop, the flattening point and smile curves, and why shape depends on definition.

**Short answer**

A retention curve plots the share of an install cohort still counted as retained on each day after install. Read the early drop, then where it flattens, and only compare curves that use the same retention definition and have reached the same day.

## What is a retention curve?

A retention curve is one row of a cohort table plotted as a line, with Day N on the horizontal axis and the share of the cohort counted as retained on the vertical axis.

**Cohort:** users grouped by install day. The install day is Day 0, and Day 0 activity is not a return.

**Retention curve:** the cohort's Day 1, Day 2, Day 3 and later values drawn as one line, or several cohorts pooled into one line. There is a short definition in this [glossary entry on the retention curve](https://linkrunner.io/glossary/what-is-retention-curve?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-read-a-retention-curve&utm_content=inline).

If you are not yet comfortable with the table the curve comes from, start with [how to read a cohort table](https://retentioncohorts.com/read/), then come back.

## The three parts of a classic curve

Most [classic retention](https://linkrunner.io/glossary/what-is-classic-retention?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-read-a-retention-curve&utm_content=inline) curves have the same three parts.

1. **The early drop.** A large share of users never return after Day 0, so Day 1 is already well below 100%, and the next few days fall further.
2. **The bend.** The fall slows as the users who were never going to stay drop out.
3. **The flattening point.** The curve levels off. The users left are the ones who have built a habit, and this level is what drives long-term value.

In the sample app on the home page, a made-up app, pooled classic retention falls from about 35% on Day 1 to about 18% on Day 7 and about 16% on Day 14. Most of the loss happens in the first week, and the curve is close to flat by the second. The [flattening section of the reading guide](https://retentioncohorts.com/read/#flattening) shows how to spot this in the table itself.

## Why does a retention curve go up again?

A curve that rises after falling, often called a smile curve, usually means something happened on a calendar date, such as a release, a push notification or a reactivation campaign, rather than users suddenly liking the app more. Check the diagonal of the cohort table to confirm.

A calendar event touches every cohort on the same date, which is a different Day N for each cohort. In the cohort table it shows up as a diagonal stripe. In the sample app, the 3 Sep cohort shows about 38% classic retention on Day 7, against roughly 13% to 20% for its neighbours. That Day 7 falls on the same date as a made-up push, so the lift belongs to the push, not to a better cohort. The [diagonals section](https://retentioncohorts.com/read/#diagonals) walks through the same example.

A genuine, lasting rise in the tail is possible, for example when a product change makes old users come back. Treat it as a hypothesis until you have seen it across several cohorts and dates.

## The definition decides the shape

The same users produce three different curve shapes depending on which retention definition you plot.

| Definition | Shape as N grows | Sample app, Day 1 to Day 14 |
|---|---|---|
| Classic | Falls, then flattens or wobbles | About 35% to 18% to 16% |
| Cumulative | Rises, never falls | About 35% to 67% to 73% |
| Rolling | Falls, and its tail drops to meet classic | About 67% to 38% to 16% |

- **Classic** counts users active on exactly Day N, so it can fall or wobble.
- **Cumulative** counts users active at least once from Day 1 to Day N. Each day can only add users, so the line never falls. A rising cumulative curve is not a sign of improving engagement.
- **Rolling** counts users active on Day N or any later observed day. On the last observed day there is only one day left to count, so rolling equals classic there and the tail collapses onto the classic line.

If your curve looks wrong, check the definition first. The [cohort explorer](https://retentioncohorts.com/) lets you switch the same sample data between all three to see the shapes change.

## Comparing curves fairly

### Only compare mature days

A cohort has a Day N value only once Day N has fully passed for it. A cohort that installed last week has no Day 14, and its rolling values are still rising. Compare two curves only up to the last day both have reached.

### Small cohorts wobble

A cohort of a few dozen users can swing several points from one day to the next because one or two users came back. Read small cohorts as directional, pool several days of cohorts where you can, and see the [sample size notes](https://retentioncohorts.com/read/#sample-size) before reading meaning into a bump.

### Use the same definition and day boundary

Published benchmarks usually use classic, exact-day retention, so compare like with like. Check that both curves use the same definition, the same time zone for day boundaries and the same event for "active".

For a longer walkthrough with more curve shapes, see [this guide to reading a retention curve](https://linkrunner.io/blog/how-to-read-a-retention-curve?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-read-a-retention-curve&utm_content=inline). If you are also tracking what users do on Day 0, this overview of [app onboarding metrics](https://attributionbenchmarks.com/blog/app-onboarding-metrics/) pairs well with the early part of the curve.

**Note:** Example numbers come from a made-up app to show the method, not a benchmark.

See this on your own app

Linkrunner shows retention by day since install for each of your install cohorts, so you can read the drop and the flattening point on your own curve.

- [How to Read a Mobile Retention Curve](https://linkrunner.io/blog/how-to-read-a-retention-curve?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-read-a-retention-curve&utm_content=panel)
- [Create a free account (25k installs free)](https://dashboard.linkrunner.io/sign-in?utm_source=retentioncohorts&utm_medium=referral&utm_campaign=learn-read-a-retention-curve&utm_content=panel)

## Questions

**What is a good shape for a retention curve?**

A curve that drops early and then flattens is the healthy pattern, because it means a group of users has formed a habit. A curve that keeps falling towards zero means no group is sticking. The level where it flattens matters more than the height of Day 1.

**Why does my cumulative retention curve go up?**

Cumulative retention counts users active at least once from Day 1 to Day N, so each extra day can only add users. It rises by design and never falls. Use classic if you want to see users dropping off.

**Why does my rolling curve suddenly drop at the end?**

Rolling counts returns on Day N or any later day observed so far. On the most recent days there is little or no time left for later returns, so rolling falls to match classic. The drop is a data artefact, not churn.

**How many days should a retention curve cover?**

Long enough to see where it flattens, which depends on how often people need the app. Daily-use apps can show this within a couple of weeks, while apps used weekly or monthly need longer windows.

**Can I compare my curve with another app's?**

Only if both use the same definition, day boundaries and cohort maturity. Published benchmarks usually use classic retention, so plot classic when you compare against them.

**What is the difference between classic, rolling and cumulative retention?**

Classic counts users active on exactly Day N. Rolling counts users active on Day N or any later day, and cumulative counts users active at least once from Day 1 to Day N. They use the same raw activity but answer different questions.

## More guides

- [01 **Classic vs rolling vs cumulative retention** Classic, rolling and cumulative retention count different users on the same day. See the definitions, a ten-user worked example and when to use each one.](https://retentioncohorts.com/learn/classic-vs-rolling-vs-cumulative/)
- [02 **Why paid D1 retention is lower than organic** Paid D1 retention below organic is partly normal, as organic users arrive with intent. See the causes, from targeting to measurement errors, and how to check.](https://retentioncohorts.com/learn/paid-d1-lower-than-organic/)
- [03 **Retention by channel and campaign** Blended retention hides what each channel does. Learn to cut a cohort table by channel, campaign and creative, keep naming clean and spot a channel mix shift.](https://retentioncohorts.com/learn/retention-by-channel/)
- [04 **Choosing the cohort start event: install or first purchase** How to define cohorts by choosing a start event: install, first open, sign-up or first purchase, what each answers, survivorship bias, day boundaries, channels.](https://retentioncohorts.com/learn/cohort-start-event/)
- [05 **Retention for weekly-use vs daily-use apps** Why classic Day N retention undercounts apps people use weekly, and how cumulative windows, weekly cohorts and DAU/MAU give a fairer read of weekly-use apps.](https://retentioncohorts.com/learn/weekly-vs-daily-use-apps/)
- [06 **Turning retention into decisions: LTV, payback and budget** How to turn app retention into decisions: which definition feeds LTV and forecasts, a simple LTV from a retention curve, cost per retained user and payback.](https://retentioncohorts.com/learn/retention-to-decisions/)