This project analyzes coffee sales data to uncover actionable insights related to product performance, customer behavior, and regional demand. The objective was to transform raw transactional data into an interactive Excel dashboard that enables stakeholders to monitor performance and make data-driven decisions.
The dataset consists of:
- Order details (Order ID, Order Date)
- Product attributes (Coffee Type, Roast Type, Size)
- Customer data (Customer ID, Loyalty Card status)
- Geographic data (Country)
- Sales metrics (Sales Amount)
- Removed duplicates and handled missing values
- Standardized categorical fields (roast type, size, country)
- Structured data into organized tables
- Created calculated metrics for analysis
- The United States contributes ~65–70% of total sales
- Top 5 customers contribute ~20–25% of total revenue
- Arabica coffee shows consistent sales performance
- Sales show seasonal demand patterns
- Loyalty customers have higher average order value
- Medium and Dark roast categories account for ~60–70% of sales
- 0.2 kg packages have the highest purchase frequency
- Sales trends over time
- Country-wise performance analysis
- Top 5 customers analysis
- Interactive filters (Roast Type, Size, Loyalty Card, Time)
- Excel → Data cleaning and dashboard creation
- Pivot Tables & Charts → Data aggregation
- Slicers & Timeline Filters → Interactivity
- Analytical techniques → Trend analysis, segmentation
- Identifies high-performing regions and products
- Highlights customer concentration and loyalty impact
- Supports data-driven marketing and inventory decisions
For complete project details, including business problem, recommendations, and full analysis refer Detailed_Documentation.