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PythonSQLStrategyData
Customer Segmentation Analysis
Used clustering algorithms to identify 6 distinct customer segments, enabling personalized marketing strategies.
6Segments identified
47%Email engagement lift
+22%Repeat purchases
Project visual
Overview
A data-driven segmentation model that replaced intuition-based marketing with targeted, evidence-based campaigns.
The Problem
Marketing was treating all customers the same, resulting in generic messaging and declining engagement rates.
Approach
Applied K-means clustering on purchase history, engagement data, and demographics. Validated segments with business stakeholders and created actionable personas.
Tools Used
PythonScikit-learnSQLTableauPandas
Outcomes
- Identified 6 actionable customer segments
- Increased email engagement by 47%
- Boosted repeat purchase rate by 22%
Learnings
The gap between data science and business action is where most projects fail. Translating clusters into relatable personas was the key to adoption.