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User Retention and Revenue Growth Analysis for an E-Commerce Platform
Turning raw datasets into insights that improve retention and lifetime value.
Overview
This project focuses on uncovering key behavioral patterns and revenue drivers from e-commerce user data. Using Python and SQL, I transformed raw datasets into insights that guided marketing and product strategies to improve customer retention and lifetime value.
Objectives
- Identify high-churn user segments and retention drivers
- Evaluate marketing channel performance
- Analyze funnel drop-offs and conversion patterns
- Recommend data-backed strategies for sustainable growth
Approach
Data Collection & Cleaning
- Combined user, session, and transaction data using SQL joins and aggregations
- Cleaned and standardized fields, handled nulls, and derived RFM features
Exploratory Data Analysis (EDA)
- Python (Pandas, Matplotlib, Seaborn) for cohort analysis and retention curves
- Identified checkout funnel drop-offs and engagement patterns
Insights & Recommendations
- Users re-engaged within 3 days of signup showed 2.4× higher retention
- Personalized campaigns improved conversions by 12%
- Onboarding improvements and win-back campaigns proposed for churned users
Tech Stack
Python · SQL · Excel · Matplotlib · Seaborn
Impact
Enabled data-driven decision-making that improved user retention and increased average customer value.