How to Find Churned Users with SQL: A Step-by-Step Guide

Churn analysis is critical for understanding why users stop engaging with your product or service. Knowing how to find churned users with SQL empowers businesses to take action, improve retention, and boost revenue. This guide will walk you through the process of identifying churned users in your database using SQL. We'll share practical examples and introduce how AI-powered tools like AI2sql can simplify this essential analysis.

What Is User Churn?

User churn, or customer churn, happens when users stop using your product or service over a specific period. Detecting churned users helps you uncover patterns and build strategies to win them back or prevent future churn. Common churn signals include:

• No logins or activity for a set duration (e.g., 30 days)

• Subscription cancellation or downgrade

• Declined payments or expired accounts

Preparing Your Data for Churn Analysis

Before building churn queries, ensure you have:

• A users table with a unique user ID and registration date

• A table recording user activity or logins

• Date columns for tracking the latest user actions

If you’re missing these, collaborate with your data team to get access or use AI2sql to help generate the necessary joins and columns.

Writing the SQL Query to Find Churned Users

Let's walk through a basic SQL query to identify churned users—those who haven’t logged in within the past 30 days.

Example Data Model

• users (id, email, registration_date)

• logins (user_id, login_date)

Sample SQL Query

SELECT u.id, u.email
FROM users u
LEFT JOIN (
    SELECT user_id, MAX(login_date) AS last_login
    FROM logins
    GROUP BY user_id
) l ON u.id = l.user_id
WHERE l.last_login < CURRENT_DATE - INTERVAL '30 days';

What this does:

• Finds each user's last login date

• Returns users who haven't logged in for the past 30 days

How AI2sql Makes Churn Analysis Effortless

If you’re new to SQL or want to save time, AI2sql can help you generate these queries instantly. Just type what you need, such as:

Show users who haven't logged in in the last 30 days.

You’ll receive ready-to-use SQL, customized for your database schema. This makes analyzing churn accessible to non-technical team members.

Best Practices & Next Steps

• Adjust the definition of "churn" to fit your product (e.g., try 60 days of inactivity)

• Visualize churn over time to spot trends

• Combine churn queries with user segmentation for deeper insights

• Explore retention and reactivation strategies based on your findings

For more tips, see our SQL blog or try AI2sql to automate your analysis.

Conclusion

Identifying churned users with SQL helps you take proactive steps toward user retention and business growth. Whether you're comfortable with SQL or exploring AI-powered tools like AI2sql, the process can be simple and highly effective. Try it on your data today!

Ready to cut churn? Sign up for AI2sql and start analyzing effortlessly!