Glossary: Cohort Analysis

What is Cohort Analysis?

Cohort analysis groups users or wallets by a shared starting event, trait, or behavior, then compares how those groups change over a common elapsed-time horizon. Unlike a segment, which describes who matches a rule now, a cohort usually preserves membership so teams can measure retention, revenue, activity, or conversion over time.

Cohort Analysis Explained

Cohort analysis means studying groups of people who have something in common.

For example, one group might be users who signed up in January. Another group might be users who signed up in February.

Instead of mixing everyone together, cohort analysis compares each group separately.

This helps teams see whether newer users are staying longer, leaving faster, spending more, or behaving differently than older users.

What Cohort Analysis Means For

Examples

• A SaaS company compares users who signed up in January, February, and March to see which group has the best 30-day retention.

• A crypto protocol tracks wallets that made their first deposit during a rewards campaign and compares them with wallets acquired organically.

• A marketplace studies buyers who joined through referrals versus paid ads to see which group makes more repeat purchases.

• A product team analyzes users who adopted a new feature in their first week and checks whether they remain active longer than users who did not.

Using cohorts of wallet addresses in cohort analysis

In DeFi analytics, a cohort may be made up of wallet addresses, connected wallet accounts, or a documented cluster of addresses. Choose the unit before comparing groups: these are not interchangeable, and one address does not necessarily represent one person.

Define cohort membership with a clear entry rule, such as the week of a wallet’s first confirmed swap on a protocol, its first deposit into a lending market, or its first use of a feature. Keep membership fixed after that entry event, then measure later behavior such as repeat swaps, active weeks, deposits, or protocol fees attributed under a stated method.

For example, group distinct wallets by the week of their first successful swap on a specific chain and contract. Then compare the share that swaps again or remains active after one, four, and eight weeks. State how you treat reverted transactions, bots, contracts, chain coverage, and inferred wallet clusters so the cohorts can be compared consistently.

Cohorts and segments answer different questions

Use a cohort to compare groups from a shared origin across elapsed time. Use a segment to filter the current audience by properties or behavior. A useful analysis may start with a fixed acquisition cohort, then break its results down by wallet segment without changing who belongs to the original cohort.

Related reading

Explore retention analytics, Retention Rate, Cohort Retention Curve, and the DeFi cohort analysis guide.

FAQs

What is a cohort?

A cohort is a group of users who share a common trait, event, or time period.

What is cohort analysis used for?

Cohort analysis is used to compare how different user groups behave over time.

Why is cohort analysis important?

It helps teams see patterns that overall averages can hide.

What is an example of a cohort?

Users who signed up in the same month or came from the same campaign are examples of cohorts.

Is cohort analysis only for retention?

No. It can also study revenue, churn, feature adoption, purchases, and engagement.

How do cohorts of wallet addresses help analyze DeFi apps?

They let teams compare wallets that entered through the same event or period, then measure later activity such as repeat swaps, deposits, or retention using consistent chain and transaction rules.