Customer Segmentation for Retail Strategy

Overview

Ran a customer-segmentation analysis for a national supermarket chain to find purchasing patterns and inform marketing. Started with exploratory analysis of 2,000 customer profiles across age, gender and annual income to check data quality and find trends, then applied Python clustering (K-means) to group customers by behaviour and demographics.

Translated the technical findings into a 1,000-word report with tables and diagrams so executives could prioritise high-value cohorts, and presented the segments with a focus on where they could sharpen marketing spend and personalise promotions.