A leading retail company noticed shifts in purchasing patterns across demographics, product categories, and sales channels (online vs. offline). Management needed clarity on what drives consumer decisions and repeat purchases.
Overarching business question:
"How can the company leverage consumer shopping data to identify trends, improve customer engagement, and optimize marketing and product strategies?"
- Identify which factors (discounts, reviews, seasons, payment methods) drive purchase decisions
- Segment customers based on shopping behavior and demographics
- Compare online vs. offline channel performance
- Deliver actionable recommendations to improve loyalty and revenue
| # | Insight | Impact |
|---|---|---|
| 1 | Discounts are the strongest purchase trigger | 82% of impulse buys linked to promotions |
| 2 | Q4 seasonal spike across all categories | 71% revenue increase Oct–Dec |
| 3 | Digital wallet users show higher loyalty | 40% higher lifetime value vs. cash |
| 4 | Online channel outperforms offline for electronics | 2.3x better conversion rate |
| 5 | Review scores directly correlate with repeat purchase | 4★+ items have 60% return rate |
| Tool | Purpose |
|---|---|
| Python (Pandas, NumPy) | Data cleaning & feature engineering |
| SQL (MySQL) | Structured queries & customer segmentation |
| Power BI | Interactive dashboard & visualization |
| Jupyter Notebook | Exploratory data analysis |
| GitHub | Version control & project showcase |
📸
git clone https://github.com/YOUR-USERNAME/consumer-behavior-analysis.git
cd consumer-behavior-analysispip install pandas numpy matplotlib seaborn jupyterpython python/data_prep.pyjupyter notebook notebooks/Open sql/queries.sql in any SQL editor (MySQL, DBeaver, etc.) and run against the processed dataset.
Open dashboard/consumer_behavior.pbix in Power BI Desktop.
- Data Preparation & Cleaning (Python)
- SQL Queries & Customer Segmentation
- Power BI Interactive Dashboard
- Project Report (PDF)
- Stakeholder Presentation (PPTX)
- Run targeted discount campaigns in Q3 to capitalize on Q4 buying momentum
- Incentivize digital wallet adoption — these customers have significantly higher LTV
- Prioritize online channel for electronics — invest in UX and targeted ads
- Build a review-collection system — higher review scores directly drive repeat purchases
- Segment email marketing by demographic + purchase history for better conversion
Rajat Saini 📧 rajat9526@gmail.com 🔗 LinkedIn 🌐 GitHub Profile
This project was completed as part of a data analytics portfolio to demonstrate end-to-end data analysis skills.