Implemented an RFM (Recency, Frequency, Monetary) segmentation model on a large online retail dataset to classify customers into actionable business segments. The analysis revealed that 3,630 customers were Dormant/Lost, while 879 were Champions/Loyal, providing clear insights for retention and reactivation strategies.

Retail businesses often struggle to identify which customers are most valuable and which are at risk of churn. The challenge was to preprocess a large transactional dataset and apply RFM analysis to generate meaningful customer segments for marketing and retention.

Data Cleaning: Removed invalid transactions, null values, and standardized formats for dates and IDs.

Metric Calculation: Computed Recency, Frequency, and Monetary values for each customer.

Segmentation: Applied quintile scoring (1–5) to RFM metrics and mapped customers into business segments (Champions, Potential, Dormant/Lost).

Visualization: Built distribution charts using Matplotlib/Seaborn to highlight segment proportions.

Delivered a segmentation pipeline that classified 5,878 unique customers into actionable categories. The model provided clear insights into customer behavior, enabling targeted marketing campaigns and loyalty programs.

Retention Strategy: Identified Champions/Loyal customers for VIP rewards and early product access.

Reactivation Campaigns: Highlighted Dormant/Lost customers for discount offers and surveys.

Business Value: Equipped stakeholders with actionable insights to reduce churn and maximize long‑term customer value.