README

RFM Analysis

This project is designed to analyze customer purchase behaviors using RFM (Recency, Frequency, Monetary) analysis. By segmenting customers, businesses can gain actionable insights for personalized marketing strategies.

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Table of Contents

1. Introduction
2. Project Objectives
3. Dataset Requirements
4. Steps Overview
5. Results
6. Contact Information

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1. Introduction

RFM analysis is a powerful method used to classify customers into different segments based on their purchasing patterns. It evaluates:

• Recency: How recently a customer made a purchase.
• Frequency: How often a customer makes purchases.
• Monetary: How much money a customer spends.

The outcome of the analysis helps businesses identify high-value customers, understand customer loyalty, and improve overall engagement.

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2. Project Objectives

The main objectives of this project are:

1. To calculate RFM metrics for each customer.
2. To assign RFM scores and segment customers based on their purchase behavior.
3. To visualize customer segments for better understanding and actionable insights.

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3. Dataset Requirements

The dataset used for this analysis should contain the following:

• Customer ID: Unique identifier for each customer.
• Purchase Date: Date of purchase or transaction.
• Transaction Amount: The amount spent in each transaction.

Ensure the dataset is clean, with no missing or incorrect values in the key columns.

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4. Steps Overview

1. Data Cleaning and Preparation

   • Remove duplicates and handle missing values.
   • Convert date columns to appropriate datetime formats.

2. Calculate RFM Metrics

   • Recency: Calculate the number of days since the last transaction.
   • Frequency: Count the number of transactions for each customer.
   • Monetary: Sum the total transaction value for each customer.

3. Assign RFM Scores

   • Rank each metric on a scale (e.g., 1-5) to standardize scores.
   • Combine scores to form an overall RFM score.

4. Customer Segmentation

   • Group customers into segments such as Champions, Loyal Customers, At Risk, etc.

5. Visualization

   • Create charts and graphs to visualize segment distributions and insights.

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5. Results

The analysis delivers actionable insights, such as:

• Identifying high-value customers (Champions).
• Highlighting at-risk customers who need attention.
• Creating segments to optimize marketing campaigns and retention strategies.

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6. Contact Information

For any questions or suggestions regarding this project, feel free to contact:

Yogesh Dhaliya

• Email: [email protected]
• LinkedIn: https://www.linkedin.com/in/yogeshdhaliyaa/

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Thank you for exploring the RFM Analysis Project!