Data driven analysis of 2.7M ecommerce events to identify conversion bottlenecks and quantify revenue opportunities.
Primary Issue: 97.3% Browse to Cart Drop off
Only 2.69% of viewers add items to cart (vs. 5 10% industry benchmark). This represents 1.36M users lost at the View → Cart stage. Interestingly, checkout performance is strong with 31.07% cart to purchase conversion, which is above the 25 35% industry benchmark.
Root Cause
The problem is not cart abandonment it's product page engagement. Users who add to cart convert well, but 97% never make it that far.
Revenue Opportunity
Improving View → Cart conversion from 2.69% to just 5% (industry standard) would generate: 32,000+ additional cart adds 10,000+ additional purchases 85% revenue increase with same traffic volume
Improve product imagery and descriptions Add social proof (reviews, ratings, "bestseller" badges) Implement urgency triggers (limited stock indicators, time limited offers) A/B test "Add to Cart" button placement and copy
The data shows median conversion time is 20 minutes from first view to purchase, with 50% of users adding to cart within 3 minutes of viewing. This suggests implementing retargeting campaigns for users who viewed but didn't add to cart within 1 hour could capture interested but hesitant shoppers.
Top 10 items convert at 8 15% (3 6x the overall average), while bottom 10 items convert at less than 0.1%. Analyzing top performers for common attributes and either fixing or removing underperformers could significantly improve overall conversion rates.
2,756,101 events across 1,407,580 unique users 137 day period (May September 2015) Event types: View, Add to Cart, Transaction
- Exploratory Data Analysis: Examined time patterns, validated data quality, analyzed event distribution
- Funnel Analysis: Calculated user level conversion rates and identified drop off points
- SQL Implementation: Replicated analysis in SQL for production scalability
- Timing Analysis: Measured time to conversion by funnel stage
- Item Performance: Benchmarked product level conversion rates
- Impact Modeling: Quantified revenue opportunity from optimization
| Metric | Current | Industry Benchmark | Status |
| View → Cart | 2.69% | 5-10% | Below |
| Cart → Purchase | 31.07% | 25-35% | Good |
| Overall Conversion | 0.83% | 2-3% | Below |
Python: pandas, numpy, matplotlib, seaborn, plotly
SQL: duckdb
Analysis: Statistical analysis, funnel metrics, conversion rate optimization
Visualization: Multi stage funnel charts, heatmaps, distribution analysis
ecommerce funnel analysis/
├── data/
│ ├── events.csv # Raw event data
│ └── processed/ # Cleaned data
├── notebooks/
│ ├── 01_data_exploration.ipynb
│ └── 02_funnel_analysis.ipynb
├── src/ # Reusable Python functions
├── sql/ # SQL queries
│ ├── funnel_queries.sql # 6 production-ready queries (funnel metrics, cohorts, retention
├── requirements.txt
└── README.md
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Clone the repository
git clone https://github.com/mjyang00001/ecommerce funnel analysis.git cd ecommerce funnel analysis -
Set up environment
python m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install r requirements.txt
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Run analysis notebooks
jupyter notebook
Start with
notebooks/01_data_exploration.ipynbThen runnotebooks/02_funnel_analysis.ipynb
This analysis demonstrates end to end analytical capabilities: identifying specific problems through data exploration, quantifying business impact, and delivering actionable recommendations prioritized by expected ROI. The findings pinpoint the exact stage of user drop off and calculate the specific revenue opportunity, providing clear direction for product and marketing teams.
Cohort analysis to track user behavior trends over time User segmentation using RFM analysis and clustering techniques Predictive modeling for purchase probability Interactive dashboard for stakeholder exploration
Author: Matthew Yang Date: November 2025