How to Write SQL Queries for Retention Analysis by Cohort

Understanding customer retention is crucial for the success of any business, especially in SaaS and subscription-driven industries. Retention analysis by cohort lets analysts and product teams identify trends, user behaviors, and opportunities for growth. In this post, we’ll break down how to build a SQL query for retention analysis by cohort—even if you’re not a seasoned SQL developer—and explore how tools like AI2sql can make the process even easier.

What is Cohort Retention Analysis?

Cohort analysis groups users based on shared characteristics—such as their signup month—and then tracks how long they stay active over time. This approach reveals which user acquisitions efforts are most effective, and which cohorts may require intervention.

• Cohort: A group of users segmented by a common trait (e.g., signup date).

• Retention: The percentage of users that continue to use your product after a given period.

Why Cohort Analysis Matters

Retention by cohort helps you:

• Spot early signs of churn or strong engagement.

• Compare the effectiveness of product changes over time.

• Identify high-performing acquisition channels.

• Support data-driven decision making across your team.

Creating an SQL Query for Retention Analysis by Cohort

Let’s walk through a sample use case. Suppose you have a table user_activity with:

• user_id

• signup_date

• activity_date

Here’s a step-by-step approach to building a cohort retention query:

1. Identify User Cohorts

Group users by their signup month:

SELECT 
  user_id,
  DATE_TRUNC('month', signup_date) AS cohort_month
FROM user_activity
GROUP BY user_id, cohort_month;

2. Calculate Retention Events

Track the difference in months between signup and subsequent activity:

SELECT 
  user_id,
  DATE_TRUNC('month', signup_date) AS cohort_month,
  DATE_TRUNC('month', activity_date) AS activity_month,
  EXTRACT(MONTH FROM age(activity_date, signup_date)) AS months_since_signup
FROM user_activity
GROUP BY user_id, cohort_month, activity_month, months_since_signup;

3. Aggregate Retention Counts

Count users retained at each interval:

SELECT 
  cohort_month,
  months_since_signup,
  COUNT(DISTINCT user_id) AS retained_users
FROM (
  -- Inner query as above
) AS cohort_data
GROUP BY cohort_month, months_since_signup
ORDER BY cohort_month, months_since_signup;

These queries reveal the number of users from each signup cohort who returned after 1 month, 2 months, etc.

Making Retention Analysis Easy with AI2sql

Writing complex SQL queries for retention analysis by cohort can be daunting. AI2sql helps you:

• Convert plain English instructions into accurate SQL queries

• Reduce manual coding and error risk

• Accelerate data analysis for teams of any technical background

Example: With AI2sql, you could simply type:

"Show me monthly retention cohorts based on when users signed up and count how many are active each month after sign-up."

AI2sql will generate the right SQL for your database, tailored to your schema—saving time and ensuring best practices.

Tips for Better Cohort Retention Analysis

• Double-check date formats for consistency in your database.

• Visualize the results with heatmaps for clear insights.

• Iterate on your cohort definitions to answer new questions.

Next Steps

Retention analysis by cohort is a powerful tool for understanding user behavior. By leveraging SQL—and tools like AI2sql—you can unlock actionable insights faster, regardless of your SQL expertise. Ready to accelerate your analytics workflow? Try AI2sql today and let data drive your decisions!