This project analyzes retail transaction data using Microsoft Excel to uncover sales trends, performance insights, and customer payment behavior. An interactive dashboard was built to support data-driven business decisions.
Columns included:
Date, Time, StoreID, Location, Product, Quantity, UnitPrice, PaymentType,
TransactionID, Cashier, StoreManager, TimeOfDay, DayOfWeek, TotalPrice
- Analyze hourly sales trends
- Identify top-performing store managers
- Understand product-wise revenue contribution
- Segment transactions by payment type
- Ensure transaction-level auditability using ledger keys
- Microsoft Excel
- Pivot Tables & Pivot Charts
- Slicers (Year-based filtering)
- CONCAT-based Ledger Keys
- KPI Metrics
- Sales peak during evening hours
- Revenue contribution varies significantly across store managers
- A few products drive the majority of total sales
- Digital payments dominate over cash transactions
- Interactive year slicer enables trend comparison
- Total Revenue
- Total Transactions (using Ledger Key)
- Average Transaction Value
- Top Product by Revenue
- Top Store Manager
Created a CONCAT-based ledger key to uniquely identify transactions, helping detect duplicates and support audit and reconciliation checks.
