💼  Retention

Retention#

The share of users who remain active over a given period.

Important

✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.

What it is#

Retention measures how well a business keeps its customers or users over time — the complement of churn, and a direct signal of product value: if people keep coming back, the product is working.

The metrics#

The customer retention rate (CRR) strips out new sign-ups to measure how many existing customers stayed:

\[\text{CRR} = \frac{E - N}{S} \times 100,\]

with \(S\) customers at the start, \(E\) at the end, and \(N\) newly acquired. Cohort retention tracks a starting group over time (100 sign up in week 1, 40 still active in week 4 → 40% week-4 retention), and revenue retention comes in two forms: gross (GRR), which ignores expansion, and net (NRR), which adds upsells and cross-sells.

Example#

Start with 100 paying customers, lose 10 and gain 20: the CRR counts only the kept ones, \((110 - 20)/100 = 90\%\). A mobile app with 300 of 1,000 installs still active at 30 days has 30% retention.

Why it matters#

Retention is cheaper than acquisition, drives predictable recurring revenue, and is the clearest indicator of product value — which is why it sits at the heart of lifetime value and growth. Teams raise it with onboarding, A/B tests and recommendations, and predict who is about to drop with the same models used for churn.


Theme: Business & Growth Analytics  ·  All terminology



See also

Source article Adapted (context, re-expressed) in our own words from: Retention (insightful-data-lab.com).

Tags: purpose: reference topic: terminology level: intermediate