SQL project focused on measuring customer retention across monthly cohorts and identifying the periods in which user activity declined most significantly.

Tools: SQL · CTEs · Conditional Aggregation · Date Functions · Cohort Analysis · Retention Metrics

Project Context

Customer retention is a key indicator of whether a business is successfully maintaining user engagement after acquisition. Analyzing only the total number of active customers can hide important differences between groups that joined at different times.

This project uses cohort analysis to group customers according to their registration month and evaluate how their activity changes during the first six months of the customer lifecycle. The analysis helps reveal when users are most likely to disengage and whether retention performance improves or declines across cohorts.

Business Objective

The objective was to measure and compare customer retention across monthly cohorts in order to:

• Calculate the initial number of customers in each cohort.
• Measure how many customers remained active after 1, 3, and 6 months.
• Convert retained-customer counts into comparable retention percentages.
• Identify the periods with the greatest decline in customer activity.
• Generate insights that could support onboarding, engagement, and retention strategies.

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🔎 Key analytical question

At what point during the first six months do customers show the largest decline in activity, and how does this behavior vary across monthly cohorts?

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Dataset & Methodology

Dataset

The dataset contained customer-level information used to track registration and subsequent activity over time. The main fields required for the analysis included:

• Customer identifier
• Signup or registration date