Cohort Analysis: Definition & Detailed Guide

If you’re in eCommerce, you’ve probably asked yourself this more than once: is it better to invest in acquiring new customers or focus on retaining the ones you already have? One of the most useful tools to help you make smart business decisions is cohort analysis. It lets you track what people do after their first interaction with your brand and shows how effective your marketing efforts really are.

How to conduct cohort analysis? Let’s take a closer look at what cohort analysis actually is, how it works in marketing, and how it helps you better understand user behavior.

Cohort Analysis Definition

Cohort analysis is a type of behavioral analytics that groups users (cohorts) based on shared characteristics or experiences within a specific timeframe. Simply put, it lets you track how different groups of customers behave — for example, based on the month they signed up — and monitor how many return after 1, 2, or 3 months.

Why do you need cohort analysis? It helps you evaluate marketing effectiveness and pinpoint when users stop engaging. This kind of data shows how different user groups perform over time.

What kind of data does cohort analysis include? Typically: sign-up or first purchase dates, retention stats, traffic sources, and behavioral patterns.

You don’t have to be in eCommerce to use it. For example, in the HoReCa industry, a coffee shop owner might group customers by the month of their first visit, then analyze how many returned — and what influenced their decision: a holiday promo, discount, or special offer.

Want to go deeper into advanced analytics? Check out our post: “What Is End-To-End Analytics and Why Is It Important for Business”.

Cohort Analysis in Marketing: Key Questions

1. When and why do users drop off?

One of a marketer’s key goals is to retain customers. Customer cohort analysis helps identify exactly when people lose interest — whether that’s a few days after signing up or after their first purchase. For example, if users typically drop off around day 7 without contact, that’s your signal to send a timely offer.

2. Which marketing campaign delivers better results?

Basic analytics might tell you how many conversions you got. But cohort analysis takes it further. It reveals which user groups made more repeat purchases or which traffic sources brought in loyal customers.

3. How do updates or redesigns affect user engagement?

Launched a new version of your app or site? Use cohort analysis to compare retention before and after the update and see if engagement improved.

Cohort Analysis in Google Analytics

One of the easiest ways to get started with cohort analysis is through Google Analytics 4 (GA4), which has a built-in feature that allows you to create cohorts based on when users first visited your site or made a purchase.

Useful link: Detailed guide from Google on how to interpret cohort analysis in GA4.

If you are looking for something more flexible than what GA4 offers, check out BigQuery with raw data access. You will be able to group users not just by time but also by traffic source, product categories, or behavior-based criteria. You will need to carefully plan your data structure and queries to avoid drawing incorrect conclusions.

If you are looking for a comprehensive solution to bring solid analytics into your business, check out our end-to-end analytics setup service. We will help you build a complete data pipeline, from Google Ads to your CRM.

Cohort Analysis: Example for eCommerce and SaaS

eCommerce

Cohort analysis is a great way to track how customers behave after their first purchase. For example, you can see the percentage of users who return to the site within the next 30 days.

It also helps you identify which products are catching the eye of different cohorts and whether there’s potential for cross-sells.

SaaS

Cohort analysis for SaaS businesses should include key data like account activation, feature usage, and when users switch to a paid plan.

If you’re wondering about the cohort analysis meaning, the answer is simple — it helps you see which features work best for user retention.

Understanding Cohort Analysis in BigQuery: Advanced Features

BigQuery for Deeper Analysis

If you’re looking to go beyond basic reporting, BigQuery is a powerful tool for running advanced behavioral cohorts. You can export data from GA4, Facebook Ads, or your CRM directly into BigQuery.

Here’s what you can do:

• Customize your own SQL queries to track any event you care about.
• Combine data from different sources to get a complete picture of the user journey—from the first ad click to repeat purchases.
• Easily visualize your reports in Looker Studio or Power BI.

Example BigQuery SQL Snippet:

WITH first_touch AS (



  -- We define the first interaction date for each user



  SELECT



    user_pseudo_id,



    MIN(PARSE_DATE('%Y%m%d', event_date)) AS first_touch_date



  FROM `your_project_id.analytics_XXXXXX.events_*`



  WHERE event_name = 'session_start'  -- Or another "first touch" event



  GROUP BY user_pseudo_id



),



cohort_analysis AS (



  -- We match the first visit date with every follow-up action



  SELECT



    ft.user_pseudo_id,



    ft.first_touch_date,



    PARSE_DATE('%Y%m%d', e.event_date) AS event_date,



    DATE_DIFF(PARSE_DATE('%Y%m%d', e.event_date), ft.first_touch_date, MONTH) AS month_number



  FROM `your_project_id.analytics_XXXXXX.events_*` e



  JOIN first_touch ft



  ON e.user_pseudo_id = ft.user_pseudo_id



)



-- We count retained users in each cohort by month



SELECT



  first_touch_date,



  month_number,



  COUNT(DISTINCT user_pseudo_id) AS retained_users



FROM cohort_analysis



WHERE month_number BETWEEN 0 AND 6  -- We track retention for the first 6 months



GROUP BY first_touch_date, month_number



ORDER BY first_touch_date, month_number;

• user_pseudo_id – user ID;
• event_bundle_sequence_id – session number;
• event_name – event name;
• event_timestamp – event time;
• PARSE_DATE(‘%Y%m%d’, event_date) – event date;
• platform – user device.

Using similar SQL logic, you can track cohorts not just by user_id, but also by combining data from other ad platforms. You can simply upload those datasets to Google Cloud. Once everything is set up, you can visualize your reports in Looker Studio or Power BI.

Suitable for:

• Web analysts and data pros who want complete control over custom cohort reports.
• Businesses that have access to raw data and want to dig deep into performance.

Note: Working with raw data in BigQuery requires a solid understanding of SQL and how GA4 or your ad platforms structure their data. If your cohort retention analysis is inaccurate, it can lead to misleading insights — and ultimately impact business decisions.

But don’t worry. At Livepage, we handle the heavy lifting, from processing raw data to building custom cohorts and transforming them into clear visual reports in Looker Studio, Power BI, or any tool you prefer. That way, you get real insights without dealing with complex queries and setups.

Amplitude and Mixpanel for Mobile Analytics

Excel or Google Sheets for Simple, No-Platform Analysis

Here’s a quick breakdown to help you decide:

✅ Need a fast way to check user trends? → GA4
✅ Want deep insights using raw data? → BigQuery
✅ Analyzing a mobile app? → Amplitude / Mixpanel
✅ Looking to do a quick, simple analysis with no heavy platforms? → Google Sheets / Excel

At the end of the day, it all depends on your level of analytics and how deep you want to go with cohort analysis.

Pro Tips

• Use the right tools. Platforms like Google Analytics, Amplitude, and Mixpanel save you time and effort on cohort data collection and analysis.
• Make it a habit. Regular cohort analysis helps you spot changes in the customer lifecycle early and act on them fast.
• Test different criteria. Don’t stick to just one way of grouping users. Play around with various filters to uncover new insights.

Cohort analysis is a powerful way to understand how your audience behaves over time. It helps you make smarter business decisions, accelerate growth, and improve customer retention.