A retail company wants to understand its customers' shopping behavior to improve sales, satisfaction, and loyalty. I built this end-to-end analytics project to answer that question with a modern data-analyst stack: Python → PostgreSQL → Power BI.
The business problem
How can the company leverage consumer shopping data to identify trends, improve customer engagement, and optimize marketing and product strategies?
The analysis answers 10 business questions covering revenue by gender and age group, discount behavior, subscriber economics, shipping preferences, product ratings, and loyalty segmentation.
What the data looks like
- 3,900 synthetic retail purchase records
- 18 columns: purchase amount, review rating, gender, age, category, item purchased, payment method, subscription status, shipping type, etc.
Pipeline
CSV (3,900 rows)
↓ pandas — cleaning & feature engineering
PostgreSQL via SQLAlchemy
↓ 10 business questions solved in SQL
Power BI Desktop
↓ KPI cards, donut, 4 charts, 4 slicers, explicit DAX measures
Report + presentation
What I fixed vs. the tutorial
The tutorial this project was based on plotted some visuals using Sum(customer_id), which produced meaningless totals in the millions. I audited every binding and replaced those with explicit DAX measures like:
Number of Customers = COUNT('public customer'[customer_id])
Average Purchase Amount = AVERAGE('public customer'[purchase_amount])
Average Review Rating = AVERAGE('public customer'[review_rating])
Five key findings
| Finding | What the data shows |
|---|---|
| Clothing drives revenue | $104,264 of $233,081 total (44.7%) |
| The gender gap is audience size | Male $157,890 total, but $59.54 vs $60.25 per customer |
| Subscriptions don't raise basket size | $59.49 subscribed vs $59.87 non-subscribed |
| Discounts don't grow baskets either | $59.28 discounted vs $60.13 full-price |
| Loyal base, thin acquisition funnel | 3,116 loyal (79.9%) vs 83 first-time buyers (2.1%) |
Tech stack
| Layer | Tool |
|---|---|
| Cleaning & ETL | Python (pandas) |
| Storage & SQL | PostgreSQL |
| Visualization | Power BI Desktop (DAX) |
| Reporting | PDF + PowerPoint |
Repo, report, and reproduction
- GitHub: Customer-Shopping-Trends-Analysis
- Report: included as PDF and executive deck in the repo
- Reproduce: README has exact steps, including setting
PG_PASSWORDbefore running the notebook
This was my first end-to-end portfolio project. The biggest lesson was not the syntax — it was learning how to translate a business question into an ETL + SQL + dashboard chain that produces an actionable answer.
