This project performs a Customer 360 Retail Sales Analysis to understand customer purchasing behavior, product performance, and sales trends using Python and Power BI.
The goal of this project is to transform raw retail transaction data into meaningful insights that support business decision making and sales strategy.
The analysis includes:
• Customer Segmentation using RFM Analysis
• Customer Retention Analysis using Cohort Analysis
• Product Association using Market Basket Analysis
• Interactive Sales Dashboard using Power BI
The dataset contains retail transaction records including:
- Customer ID
- Invoice Date
- Product Category
- Sales Amount
- Order Information
- Delivery Type
- Region
Total transactions analyzed: ~397,000
Python was used for advanced customer and sales analysis.
RFM stands for:
- Recency → How recently a customer purchased
- Frequency → How often a customer purchases
- Monetary → How much money the customer spends
Customers were scored from 1–5 for each metric and grouped into segments such as:
- Champions
- Loyal Customers
- Recent Customers
- At Risk
- Hibernating
This helps businesses identify high-value customers and design targeted marketing strategies.
Cohort analysis groups customers based on their first purchase month and tracks their activity over time.
This helps measure:
• Customer retention
• Customer lifetime engagement
• Loyalty patterns
Market Basket Analysis identifies products that customers frequently purchase together.
The Apriori algorithm was used to generate association rules.
Example rule:
Product A → Product B
Meaning customers who buy Product A are also likely to buy Product B.
This helps businesses improve:
• Cross-selling strategies
• Product bundling
• Recommendation systems
Power BI was used to build an interactive dashboard showing:
• Total Sales and Profit
• Sales by Category and Region
• Profit vs Sales Trends
• Customer Distribution
• Delivery Performance
• Pareto Sales Analysis (80/20 Rule)
These dashboards help businesses monitor performance and make data-driven decisions.
Python
Pandas
NumPy
MLxtend
PostgreSQL
Power BI
• A small number of product categories generate the majority of sales (Pareto principle).
• High-value customers contribute significantly to revenue.
• Customer retention decreases after the first few months.
• Certain products are frequently purchased together.
These insights help businesses improve sales strategies and marketing decisions.
1 Clone the repository
git clone https://github.com/yourusername/customer-360-retail-analysis.git
2 Install dependencies
pip install -r requirements.txt
3 Run the analysis script
python rfm_engine.py
4 Open the Power BI dashboard file
customer_360.pbix
Week 1 → Project Setup and Data Understanding
Week 2 → Data Cleaning and PostgreSQL Integration
Week 3 → Customer Analytics using Python (RFM, Cohort, Market Basket)
Week 4 → Power BI Dashboard Development
Detailed weekly progress can be found in the progress folder.
The project follows an end-to-end data analytics pipeline:
Retail Dataset → Python Data Processing → PostgreSQL Database → Power BI Dashboard
- Raw retail transaction data is processed using Python (Pandas).
- Customer analytics such as RFM segmentation, Cohort Analysis, and Market Basket Analysis are performed.
- The processed results are stored in a PostgreSQL database.
- Power BI connects to the database to create interactive dashboards for business insights.
Retail Dataset
⬇
PostgreSQL Database
⬇
Python Analytics (RFM, Cohort, Market Basket Analysis)
⬇
Power BI Dashboard
Data Analytics | Python | Power BI | SQL