Customer Segmentation Analysis

Project Overview

This project focused on segmenting a company's customer base to enable more targeted marketing strategies and improve customer retention rates.

Challenge

The client had a large customer database but lacked insights into different customer behaviors and preferences. They were using a one-size-fits-all approach to marketing, resulting in low engagement and conversion rates.

Solution

I implemented a machine learning approach using K-means clustering and hierarchical clustering algorithms to segment customers based on purchase history, browsing behavior, demographic information, and engagement metrics.

Results

The analysis identified five distinct customer segments with unique characteristics and purchasing patterns. Targeted marketing campaigns based on these segments resulted in a 23% increase in email open rates, a 17% increase in click-through rates, and a 12% increase in conversion rates.

Key Visualizations

Customer Segments Visualization

3D scatter plot showing the different customer clusters.

Segment Characteristics

Radar charts showing the key characteristics of each customer segment.