K Means Clustering & RFM Analysis SECRET to Boost Customer Segmentation Insights in Power BI

Learn how to perform Customer Segmentation in Power BI using K-Means Clustering and RFM Analysis (Recency, Frequency, Monetary) in a simple, practical, end-to-end project.

In this full Power BI dashboard project, I’ll show you how to segment customers using machine learning (K-Means) and business logic (RFM) to identify high-value customers, loyal customers, churn risks, and inactive customers.

PBIX + Data Files: https://drive.google.com/drive/folder...

This tutorial walks you through data cleaning in Power Query, Python implementation inside Power BI, and building a complete customer segmentation dashboard from scratch.
If you’ve ever been confused about how K-Means clustering works or how to apply RFM analysis in a real Power BI project, this video will make it clear and practical.

Related Playlists
▶ Power BI Analytics Playlist:    • Power BI Analytics  
▶ Excel Analytics Playlist:    • Excel Analytics  

Timestamps
00:00 – Intro
01:00 – Why Customer Segmentation Matters?
01:35 – Techniques
03:10 – Data Cleaning in Power Query
05:20 – K-Means & RFM Using Python
15:40 – RFM scoring and customer segments
12:55 – Building the Power BI dashboard
16:40 – Final insights and key takeaways

If this helped, like the video, subscribe, and let me know in the comments what Power BI or data analytics project you’d like to see next!

#PowerBI #CustomerSegmentation #KMeansClustering #RFMAnalysis #DataAnalytics #MachineLearning #BusinessIntelligence #Python #CustomerAnalytics #PowerBIProjects