What is Cohort Analysis and How to Conduct It?

Cohort analysis is dividing users by a common characteristic into groups (cohorts) and tracking these groups' behavior over time to measure performance. It particularly reveals problems hidden by general averages in product analytics, growth, marketing, and retention work: Users from different periods within the same overall metric can behave very differently.

In this guide, we'll clarify "what is cohort analysis?" and explain the "how to conduct cohort analysis?" steps, metrics, and applications with examples. For measuring customer loyalty, the NPS and for growth optimization, the Pareto principle are also very useful.

What is Cohort Analysis?

Cohort analysis is examining the performance of users (or customers) associated with a specific time period or specific event separately.

Example cohorts:

Cohort by signup date: Users who signed up in January, February, etc.
Cohort by first purchase date: First time payers by week/month…
Cohort by campaign source: Users from Instagram ads, organic users, etc.
Cohort by product behavior: Users who used feature X in first 24 hours vs. didn't use it…

Thanks to cohort analysis, you understand:

Is retention improving or declining?
Are new users higher quality than old ones?
After a product change (release), did user behavior really change?
Which channel/campaign generates better LTV?

What is Cohort Analysis Used For?

Cohort analysis often solves these problems:

1) Prevents Average Metric Deception

For example: "Monthly active users increased" but new users drop on day 2, meaning there's no sustainable growth.

2) Gets Retention and Churn to Root Cause

Which week/month cohort failed? After which release? From which traffic source?

3) Enables Product and Marketing Optimization

Did onboarding improvement work?
Did price change increase churn?
Did new channel raise LTV?

Cohort Types: Which Cohort Analysis is Used?

Acquisition (Acquisition) Cohort

Groups users by system entry date (signup/install). Most common in mobile apps.

Behavioral Cohort

Groups users who perform a specific behavior:

"Users who browsed 3+ times on day 1"
"Users who saw payment screen in first week"

Behavioral cohorts generally provide insights closer to causality.

Revenue Cohort

Groups by revenue events like first purchase/subscription start. Ideal for LTV analysis.

How to Conduct Cohort Analysis? (Step by Step)

Step 1: Define goal and question

Clear question examples:

"Did new onboarding improve retention?"
"Which channel's 60-day LTV is higher?"
"Why does January cohort drop in week 2?"

Step 2: Choose cohort definition

Most practical start: First activity date (signup/install)
Time unit: day / week / month (based on product cycle)

Step 3: Choose the metric to track

Most common cohort metrics:

Retention Rate: Percentage of users who returned on day X/week X
Churn Rate: Percentage of lost users
Revenue Retention: Revenue preservation (especially B2B/SaaS)
LTV: Lifetime revenue of user
Repeat Purchase Rate: Repurchase rate
Activation Rate: Percentage reaching "aha moment"

Step 4: Create cohort table (cohort matrix)

Logic:

Rows: cohorts (e.g., week of 2026-01)
Columns: elapsed time (D0, D1, D7, D30, etc.)
Cells: your chosen metric (e.g., retention %)

Retention formula example:

D7 Retention = (Users active on day 7 from cohort) / (Cohort size)

Step 5: Apply normalization and segmentation

Cohorts come in different sizes; for comparison:

Convert to percentage (retention %)
Add segments like channel / country / device / plan

Step 6: Extract insights and take action

Typical patterns:

If new cohorts are worse: Traffic quality dropped or onboarding broke
If there's a breakpoint on a specific date: Release, price change, payment issue, bug
If there's channel difference: Shift budget to higher LTV channel

Cohort Analysis Example (Simple Retention Scenario)

Let's say "January week 1" cohort has 1,000 users:

D1 active: 350 → D1 retention 35%
D7 active: 120 → D7 retention 12%
D30 active: 45 → D30 retention 4.5%

"January week 2" cohort:

D1 38%, D7 16%, D30 7%

Here, the significant improvement of the second cohort on D7 and D30 suggests that in that week:

onboarding improved,
higher quality traffic,
better activation flow

could have happened. Then you segment: "Did this improvement come from which channel?"

Most Common Mistakes in Cohort Analysis

Wrong cohort definition: "Signup date" vs. "first active use" - the latter might be better.
Unclear definition of "active": What is an active user? Login or transaction?
Ignoring seasonality: Holidays, campaign periods, price increases.
Looking only at retention: Retention might be good but ARPU down, so total LTV stays flat.
Making decisions on small samples: Small cohorts generate noise.

What Tools are Used for Cohort Analysis?

Excel / Google Sheets: Good enough for starting
SQL + BI (Looker, Metabase, Power BI): Scalable analysis
Product analytics tools (Mixpanel, Amplitude): Ready cohort reports + segment
GA4: Offers some cohort reports but product-focused depth may be limited

Frequently Asked Questions

Is cohort analysis the same as retention?

No. Retention is a metric; cohort analysis is the method of examining retention (and other metrics) across cohorts over time.

Which businesses use cohort analysis more?

Subscription (SaaS), e-commerce repeat purchase, mobile apps, marketplaces, and any business with "user lifecycle."

How often should cohort analysis be done?

Depends on product:

Mobile app: D1, D7, D30 is very standard
B2B SaaS: weekly/monthly cohort + 3/6/12 months

Conclusion: See Growth Clearly with Cohort Analysis

The summary of "what is cohort analysis and how to conduct it?" is: Divide users into meaningful groups, track them over time, compare with metrics like retention/LTV, and find the reason for changes. This approach moves growth decisions from "guessing" to measurable.

If you want to integrate cohort analysis into your product and strategy, optimize your growth engine, understand your retention problem; let's talk. Schedule a meeting.