Conversion Rate Optimization

Overview

Analyzed e-commerce user behavior data across 300K+ sessions to identify the drivers of conversion and built a classification pipeline that ranks users by purchase probability. The model enables targeted marketing campaigns with measurable ROI — at a $1 outreach cost and $40 profit per conversion, only a 2.5% predicted probability is needed to break even.

Methods

• EDA across traffic source (Ads, SEO, Direct), country, age, and session engagement segments
• Logistic Regression with one-hot encoding of categorical features
• Random Forest (200 estimators) as a nonlinear comparison model
• Lift curve and decile analysis to quantify campaign targeting efficiency
• Feature importance via logistic coefficients and Random Forest impurity scores
• Secondary model excluding session engagement to isolate static user signals

Key Findings

• Session engagement (pages visited) accounts for ~90% of Random Forest feature importance — the single most predictive behavioral signal
• Returning users convert at 7.2% vs. 1.4% for new users — a 5× difference
• Top 10% of users scored by the model capture 95% of all conversions
• Germany and UK users convert at 6.3% and 5.3%, nearly double the US rate
• Without session engagement, model AUC drops from 0.986 to 0.82 — confirming it as the dominant predictor
• The decision problem is near-linearly separable in log-odds space, explaining why Logistic Regression outperforms Random Forest