Enhanced RFM Analysis for Customer Segmentation using K-Prototypes

📰 Medium · Machine Learning

Learn how to enhance RFM analysis for customer segmentation using K-Prototypes, a clustering algorithm that handles categorical and numerical data, to improve marketing strategies and customer targeting.

intermediate Published 30 Jun 2026

Action Steps

Collect customer data, including recency, frequency, and monetary values, as well as categorical data such as demographics and purchase history.
Preprocess the data by scaling and encoding categorical variables.
Apply the K-Prototypes algorithm to cluster customers based on their RFM values and categorical data.
Evaluate the clusters using metrics such as silhouette score and calinski-harabasz index.
Use the insights gained from the clustering analysis to develop targeted marketing strategies and improve customer segmentation.

Who Needs to Know This

Data analysts and marketers can benefit from this technique to better understand customer behavior and preferences, and create targeted marketing campaigns. The team can use this method to identify high-value customers and develop strategies to retain them.

Key Insight

💡 K-Prototypes can handle both categorical and numerical data, making it a powerful tool for enhancing RFM analysis and improving customer segmentation.

Key Takeaways

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Title: Enhanced RFM Analysis for Customer Segmentation using K-Prototypes

URL Source: https://medium.com/@mrobith95/enhanced-rfm-analysis-for-customer-segmentation-using-k-prototypes-c0e96df08557?source=rss------machine_learning-5

Published Time: 2026-06-30T04:46:15Z

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# Enhanced RFM Analysis for Customer Segmentation using K-Prototypes

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Enhanced RFM Analysis for Customer Segmentation using K-Prototypes

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