This project analyzes customer shopping behavior using transactional data from 3,900 purchases across various product categories. The goal is to uncover insights into spending patterns, customer segments, product preferences, and subscription behavior to guide strategic business decisions.
- Rows: 3,900 | Columns: 18
- Key Features: Customer demographics, purchase details, shopping behavior metrics
- Missing Data: 37 values in Review Rating column (handled via imputation)
- Python (Pandas, NumPy) – Data cleaning & EDA
- PostgreSQL – Business intelligence & querying
- Power BI – Interactive dashboard & visualization
- Loaded dataset, checked structure, and handled missing values
- Standardized column names to snake_case
- Feature engineering: age groups, purchase frequency days
- Data consistency checks and redundant column removal
- PostgreSQL integration for SQL analysis
10 key business questions were answered through SQL queries:
| # | Question |
|---|---|
| 1 | Revenue by gender |
| 2 | High-spending discount users |
| 3 | Top 5 products by rating |
| 4 | Shipping type comparison |
| 5 | Subscribers vs. non-subscribers |
| 6 | Discount-dependent products |
| 7 | Customer segmentation (New, Returning, Loyal) |
| 8 | Top 3 products per category |
| 9 | Repeat buyers & subscription correlation |
| 10 | Revenue by age group |
Built an intuitive Power BI dashboard for visual exploration of:
- Revenue trends by demographics
- Product performance
- Customer segmentation
- Subscription insights
- Female customers generated higher revenue than male customers
- Express shipping users spent slightly more on average
- Subscribers had slightly lower average spend but valuable lifetime value
- Age group 31–45 contributed most revenue
- Hats, Sneakers, Coats were most discount-dependent products
- Loyal customers represent ~80% of the customer base
- Boost Subscriptions – Promote exclusive benefits for subscribers
- Loyalty Programs – Reward repeat buyers to move them into "Loyal" segment
- Review Discount Policy – Balance sales boosts with margin control
- Product Positioning – Highlight top-rated and best-selling products
- Targeted Marketing – Focus on high-revenue age groups and express shipping users