This project represents an end-to-end customer buying behavior analysis designed to uncover purchasing patterns, product preferences, and category-level trends.
The insights generated from this analysis support better decision-making in sales strategy, inventory planning, and customer targeting.
- SQL: Data extraction, aggregation, and ranking analysis
- Power BI: Interactive dashboards for business insights
- Python (Pandas): Data cleaning and exploratory data analysis
- Reports & Presentations: Business-ready PDFs and PPTX
- Dataset:
customer_shopping_behavior.csv
Overview:
- Interactive dashboard showcasing customer purchasing trends
- Category-wise and product-wise sales performance
- KPI-level insights for quick business evaluation
Business Value:
Enables stakeholders to identify top-performing categories and products, supporting sales forecasting and strategic planning.
Business Question:
Which are the top 3 most purchased items in each product category?
Approach:
- Used Common Table Expressions (CTEs) for modular query design
- Applied aggregation to calculate total order volume
- Used window functions (
ROW_NUMBER) to rank products within each category
Outcome:
Identified high-demand products across categories to support inventory optimization and product-level decision-making.
Overview:
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA) to understand customer behavior
- Analysis of purchasing frequency and category trends
Insights Generated:
- Clear visibility into customer buying preferences
- Identification of repeat purchase patterns
- Support for marketing and customer segmentation strategies
customer.sql– SQL queries for analytical reportingcustomer.pbix– Power BI interactive dashboardCustomer_buying_behavior_analysis.ipynb– Python analysis using Pandascustomer_shopping_behavior.csv– Dataset used for analysis- PDF & PPT files – Business documentation and presentation
This project demonstrates practical, industry-relevant experience in transforming raw customer data into actionable business insights using SQL, Python, and Power BI, with a strong focus on stakeholder-ready reporting.