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Churn-Risk-Analysis-with-RFM

Overview

In this project I used RFM (Recency, Frequency, Monetary) analysis for customer segmentation and to identify customers at risk of churning. By analyzing customer purchase behavior, I was able to segment customers, uncover demographic insights, and develop strategies to re-engage those at risk of churn, ultimately aiming to enhance customer retention and drive business growth.

Methodology

  • Data Gathering
  • Data Cleaning
  • RFM analysis ( RFM score, segment creation, customer segmentation)
  • Customer Segmentation analysis
  • Demographic and Behavioral analysis of churn customers

Tools Used

  • Postgresql
  • Looker studio for visualization

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This project focuses on maximizing customer retention through RFM analysis using SQL

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