An end-to-end data analytics project analyzing 3,900 customer transactions to uncover revenue drivers, customer segments, and purchasing behavior trends.
An e-commerce business wants to understand:
- Which customer segments drive the most revenue?
- How subscription status impacts spending behavior
- Which products are discount-dependent
- How age groups contribute to total revenue
- Whether repeat buyers are more likely to subscribe
The objective is to generate actionable insights to improve retention, marketing strategy, and revenue optimization.
📂 Repository Structure
Notebook → data cleaning
SQL file → business queries
PBIX → dashboard
PDF → detailed report
- Rows: 3,900 transactions
- Columns: 18 features
- Includes:
- Customer demographics (Age, Gender, Location, Subscription Status)
- Purchase details (Category, Amount, Season, Size, Color)
- Behavioral metrics (Discount Applied, Previous Purchases, Review Rating)
- 37 missing values handled using median imputation by category
- Python (pandas, NumPy) – Data cleaning & feature engineering
- MYSQL – Business query analysis
- Power BI – Interactive dashboard & storytelling
- Male customers generated $157,890
- Female customers generated $75,191
- Revenue heavily skewed toward male segment
- 73% customers are non-subscribers
- Subscribers: 1,053 customers
- Non-subscribers: 2,847 customers
- Avg spend nearly equal (~$59), but total revenue higher from non-subscribers due to volume
Top 5 discount-heavy products:
- Hat (50%)
- Sneakers (49.66%)
- Coat (49.07%)
Indicates certain products are highly promotion-driven.
- Loyal: 3,116 customers
- Returning: 701
- New: 83
Strong loyal base, opportunity to convert returning → loyal.
- Young Adults: $62,143 (highest contributor)
- Middle-aged: $59,197
- Adult: $55,978
- Senior: $55,763
Young Adults are highest revenue segment.
- Promote subscription benefits to increase recurring revenue
- Reward repeat buyers to strengthen loyalty segment
- Optimize discount strategy for high-dependency products
- Target high-revenue age groups in marketing campaigns
- Highlight top-rated products in promotions
- End-to-end analytics workflow
- Data cleaning & transformation
- SQL-based business query analysis
- Segmentation & behavioral analytics
- Business-driven dashboard storytelling