E-commerce Customer Segmentation and Lifetime Value Analysis

Category: Data Science

Budget: 1525–1525 USDC

We are looking for a data analyst to analyze our Shopify store's historical transaction data from the past 24 months. The primary goal is to perform an RFM (Recency, Frequency, Monetary) analysis to segment our customer base into actionable groups such as 'Loyalists,' 'At-Risk,' and 'New Leads.' You will be responsible for cleaning the raw CSV export, identifying trends in churn rate, and calculating the projected Customer Lifetime Value (CLV). The final deliverables must include a cleaned dataset, a technical report detailing your methodology, and an interactive dashboard (Power BI or Tableau) that allows our marketing team to filter segments by geography and product category. We expect this project to be completed within three weeks, with a preliminary check-in after the first seven days to review initial data patterns and segmentation logic.

Skills required

• Python
• RFM Analysis
• Tableau
• SQL
• Data Cleaning

Milestones are funded into USDC escrow on Polygon before work begins.