Customer Segmentation & Clustering for Targeted Marketing

1. Introduction

Project Overview

An analysis of customer segmentation using RFM metrics and clustering algorithms for targeted marketing strategies.

Objective

Segment customers based on purchasing behavior to provide insights for effective marketing strategies.

2. Libraries

Required Libraries

• pandas, numpy, matplotlib, seaborn
• sklearn, yellowbrick, squarify, openpyxl

Helper Functions

• Global Cleaning, RFM Level, Elbow Method, K-Means, Visualizing Clusters, ARI Calculation

3. Exploratory Analysis

Data Cleaning and Exploration

Initial data cleaning, handling missing values, and basic statistics exploration.

Features Engineering and RFM Segmentation

Calculation of RFM metrics for customer segmentation.

4. Experimentation

K-means

DBSCAN

CAH

GMM

5. Simulation

K-means on All Data

Cluster profiling and snake plots for visualizing and comparing RFM metrics.

Cluster Profiling

Computation of summary statistics for each cluster.

Snake Plot

Visual comparison of RFM metrics for each cluster.

Cluster Description

Bar plots showing customer distribution across clusters.

6. ARI Evaluation

Calculation of Adjusted Rand Index (ARI) for clustering results stability.

7. Conclusion

Summary of insights for targeted marketing strategies and customer relationship management.