This project analyzes transactional data from a retail Superstore that sells products across categories such as furniture, office supplies, and technology. The focus of this project is to transform raw data into a clean and structured dataset, enabling meaningful analysis of sales performance and operational efficiency using Microsoft Excel.
The objective of this project is to prepare and enrich raw transactional data by integrating multiple data sources, standardizing formats, and creating calculated features to support data-driven insights.
Microsoft Excel
- VLOOKUP / XLOOKUP
- IF functions
- Date functions
- Conditional formatting
The dataset consists of three main tables:
- Transactions: Order-level data including sales, dates, customer ID, and product ID
- Customers: Customer details such as name, segment, and region
- Products: Product information including product name and category
- Integrated customer and product data into the Transactions dataset using lookup functions
- Cleaned and standardized data formats, especially date fields
- Prepared a structured dataset for analysis
New features were created to enhance analysis:
- Process Length → Measures delivery time
- Sale Size → Segments transactions into Cheap, Regular, and Expensive
- Year → Extracts order year for time-based analysis
Example :
=IFS(Sales>=100;"Expensive";Sales<2 ;"Cheap";AND(Sales>=20;Sales<100);"Reguler")
- Most transactions fall under the regular sales category
- Sales performance varies across regions
- Different product categories show varying levels of demand
- Delivery times are inconsistent, indicating potential inefficiencies
- Improve shipping efficiency to reduce delivery time
- Focus on high-performing regions to maximize revenue
- Promote high-demand product categories
- Apply targeted strategies based on sales segmentation
Check the full presentation deck here