Analyzed 500K+ e-commerce transaction records, cleaned the data in Python, generated a customer-level RFM dataset, calculated RFM scores, and performed K-Means customer segmentation.
E-commerce businesses generate massive transactional data, but raw transactions don't directly tell you which customers are most valuable, which are slipping away, and how to prioritize retention efforts. This project transforms raw transactions into customer-level insights using RFM (Recency, Frequency, Monetary) analysis and K-Means clustering.
Click either card to open the actual dashboard screenshot in the repository.
RFM segmentation allows marketing and retention teams to move away from one-size-fits-all campaigns and instead target the right customers with the right strategy — reward VIP Champions, re-engage High-Value Loyal, nurture Regulars, and design win-back offers for Lost customers.
Note: Specific sales figures or segment percentages are not included here because they were not part of the verified project brief.