Project Overview

This project centers around evaluating customer retention patterns over time by analyzing weekly cohort behavior. Using SQL, I conducted cohort analysis to reveal insights into customer retention trends and provided the Executive and Product Teams with a detailed view of user loyalty and engagement across different periods.

Learning and Implementation

This project allowed me to apply SQL techniques to group users into weekly cohorts and track their activity over time. By structuring the analysis around cohort-based retention metrics, I developed a deeper understanding of user behavior trends. This analysis included using conditional formatting and cumulative growth formulas to interpret cohort retention patterns visually, showing how users engage with the product over their lifecycle.

Insights Delivered

Through this analysis, the project addresses essential questions, such as the rate at which users return or churn week by week. By analyzing the retention rate of each cohort from the initial week to subsequent weeks, we identified patterns that reveal user loyalty, predict potential churn points, and highlight effective periods of engagement. This analysis provides actionable insights for the team to improve user retention and create targeted strategies for enhancing user satisfaction.

Progress and Growth

Completing this cohort analysis marks a significant step in my development as a data analyst. It transformed theoretical knowledge into practical, data-driven insights, reinforcing my SQL skills and my ability to interpret complex retention data. This project not only strengthened my technical capabilities but also underscored the importance of understanding user behavior over time to support strategic decision-making.

Goals

• Apply SQL skills to build a hands-on cohort analysis project.

• Track user retention patterns weekly to provide a longitudinal view of engagement.

• Offer actionable insights into user loyalty and churn trends, identifying areas for retention improvement.

• Gain practical experience in cohort analysis, transitioning from theoretical knowledge to practical application.

• Achieve a key milestone in my journey as a data analyst by translating data into meaningful insights.