Project Title: E-commerce Customer Segmentation using RFM Analysis
Tools Used: Microsoft Excel, Power BI
Data Size: 501 customers, 3,939 transactions over 2 years (2023-2024)
Key Techniques: RFM Analysis, Customer Lifetime Value (CLV) Calculation, Data Segmentation

• The e-commerce business had 501 customers generating about $1 million in revenue over 2 years.
• They were selling across 6 product categories: Books, Electronics, Beauty, Clothing, Sports, and Home & Garden.
• Management needed to understand which customers were most valuable and how to prioritize marketing resources.
• The data included transaction-level details with customer IDs, purchase amounts, dates, and product categories.

• The analysis revealed that 42% of customers (Champions + Loyal) drive 63% of revenue.
• Identified 74 'At Risk' customers worth $188K who need immediate retention efforts.
• Found that Champions purchase every 7.6 days on average vs 168 days for 'About to Sleep.
• Provided a data-driven framework for allocating marketing budgets across segments.
• The dashboard enables ongoing monitoring without manual analysis.

• This segmentation can help prioritize customer retention efforts.
• Marketing can target win-back campaigns to the $188K in at-risk revenue.
• Sales team can focus on high-CLV Champions for upselling opportunities.
• Could improve marketing ROI by 20-30% through better targeting.