End‑to‑end analytics project simulating 10,000+ customers and 50,000+ transactions to answer a CEO’s core questions:
“Where is our revenue coming from, which customers are at risk, and what should we do next?”
🔍 In One Glance
Business problem: Improve revenue, profitability, and customer retention for an e‑commerce store.
Using synthetic but realistic data, this project surfaces insights similar to a real e‑commerce business:
Revenue growth: Detects ~35% year‑over‑year revenue growth and highlights which categories and products drive it.
Customer economics: Confirms the classic pattern that the top 20% of customers generate ≈50% of revenue, motivating VIP/loyalty focus.
Churn risk & value at risk: Flags customers inactive for 60+ days and estimates the total revenue at risk, giving a target list for retention campaigns.
Forecasting: Produces a 90‑day revenue forecast with confidence bands to support inventory planning and marketing budgets.
All of these are backed by code in analytics_pipeline.py and surfaced in ANALYTICS_REPORT.txt and the dashboard.
📊 How to View the Dashboard
To view the dashboard, open dashboard.html in your browser after cloning the repo.