Customer Segmentation with RFM Analysis & K-Means Clustering
Group customers into segments such as Champions, At Risk and Lost using RFM scores and K-Means, with a marketing plan for each.
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Problem Statement & Abstract
Treating every customer the same wastes marketing money. This project segments customers by how recently, how often and how much they buy, so a business can target each group differently.
Using the public Online Retail dataset of over 500,000 transactions, the project cleans cancellations and missing customer IDs and calculates Recency, Frequency and Monetary (RFM) values for each customer. Customers are scored on each dimension and grouped into named segments such as Champions, Loyal, Potential Loyalists, At Risk and Lost.
K-Means clustering is then applied to the scaled RFM values, with the number of clusters chosen by the elbow method and silhouette score, and compared with the rule-based segments. A Power BI dashboard shows segment sizes, revenue share and movement between segments over time, and the report proposes a specific campaign for each segment, such as win-back offers for At Risk customers.
Key Features
- Cleaning of 500,000+ retail transactions
- Recency, Frequency and Monetary calculation
- Rule-based RFM segments (Champions to Lost)
- K-Means clustering on scaled RFM values
- Elbow and silhouette analysis to choose k
- Segment profiles and revenue share
- Segment migration over time in Power BI
- Targeted marketing plan per segment
Suitable For
Everything included
+ GST • about $54 USD
Added to your cart
- Delivered in 10 to 12 working days
- Free revisions for 30 days while your guide reviews
- Report written to your university’s format