Customer Shopping Behaviour Analysis is a data-driven Power BI project that examines customer purchasing patterns, sales trends, product preferences, and market performance to generate actionable business insights.
The objective of this project is to identify high-value customers, top-performing products, seasonal trends, and revenue distribution across different markets to support strategic decision-making and targeted marketing efforts.
- Identify top revenue-generating customers
- Analyze product-wise sales performance
- Understand market and zone-level revenue contribution
- Track sales trends over time (yearly & monthly)
- Evaluate customer purchasing behavior
- Support data-driven business strategy decisions
The dataset consists of the following tables:
- customer_code
- customer_name
- customer_type
- date
- cy_date
- year
- month_name
- date_yy_mmm
- markets_code
- markets_name
- zone
- product_code
- product_type
- product_code
- customer_code
- market_code
- order_date
- sales_qty
- sales_amount
- currency
- Customers ↔ Transactions (customer_code)
- Products ↔ Transactions (product_code)
- Markets ↔ Transactions (market_code)
- Date ↔ Transactions (order_date)
Star schema model implemented for optimized performance.
Revenue = SUM('public transactions'[sales_amount])
Sales Qty = SUM('public transactions'[sales_qty])
Average Revenue per Customer =
DIVIDE([Revenue], DISTINCTCOUNT(customers[customer_code]))
Top 5 Customers Revenue =
TOPN(5, SUMMARIZE(customers, customers[customer_name], "Total Revenue", [Revenue]), [Revenue], DESC)
- KPI Cards (Total Revenue, Total Sales Quantity)
- Revenue by Market (Bar Chart)
- Sales Quantity by Market
- Revenue Trend Over Time (Line Chart)
- Top 5 Customers Analysis
- Top 5 Products Analysis
- Year & Month Slicers for filtering
- Dynamic filtering across all visuals
- Power BI Desktop
- PostgreSQL (Data Source)
- DAX (Data Analysis Expressions)
- Power Query (Data Transformation)
- Star Schema Data Modeling
- Identified highest revenue-generating markets
- Recognized top-performing customers contributing major revenue
- Observed seasonal sales trends across years
- Analyzed product performance distribution
- Evaluated sales quantity vs revenue patterns
- Helps management focus on high-value customers
- Supports inventory planning based on product performance
- Enables targeted marketing campaigns
- Improves regional sales strategy
- Enhances overall data-driven decision-making
- Open the
.pbixfile in Power BI Desktop - Refresh data connection if required
- Use slicers to filter by year or month
- Explore interactive visuals
Pratik Vishwas Salunkhe
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