This project delivers a comprehensive analysis of retail consumer behavior, designed to translate raw data into actionable strategic business intelligence. I developed an end-to-end data analytics workflow that identifies purchase drivers, customer segmentation, and loyalty trends to optimize marketing and product strategies.
Key Achievements:
- ✅ Data Architecture & Cleaning (Python): Engineered a robust data pipeline using Python to clean, transform, and prepare raw retail data for high-level analysis.
- ✅ Strategic Analysis (SQL): Developed complex SQL queries to extract deep insights regarding customer spending habits, frequency, and demographic segmentation.
- ✅ Visual Storytelling (Power BI): Designed an interactive, executive-level dashboard to visualize key performance indicators (KPIs) and trends, enabling data-driven decision-making.
- ✅ Business Impact: Formulated clear, evidence-based recommendations to improve customer engagement and sales performance.
git clone [https://github.com/mukuldhattarwal/Customer-Shopping-Behaviour.git](https://github.com/mukuldhattarwal/Customer-Shopping-Behaviour.git)
cd Customer-Shopping-BehaviourI used the Jupyter Notebook customer shopping behaviour.ipynb to handle the initial data ingestion and cleaning.
- Process: The notebook imports the raw
customer_shopping_behavior.csv, performs exploratory data analysis (EDA), and preprocesses the data for the database. - Action: Run the notebook to generate the clean dataset and establish the connection to the SQL server.
The pgsql.sql script contains the analytical core of the project.
- Database Setup: Initializes the schema and tables required for the analysis.
- Insights: Execute the queries within this file to reproduce my analysis on customer segments and purchasing patterns.
Open Customer Behaviour Dashboard.pbix to interact with the visualizations.
- Features: The dashboard connects to the processed data to display dynamic views of sales trends, customer demographics, and category performance.
| File Name | Description |
|---|---|
customer shopping behaviour.ipynb |
Python source code for EDA, data cleaning, and ETL pipeline. |
pgsql.sql |
SQL script containing schema creation and analytical queries. |
Customer Behaviour Dashboard.pbix |
Power BI dashboard file for data visualization. |
customer_shopping_behavior.csv |
Original dataset used for this project. |