Retail Store Sales and Stock Dataset
Compare store sales across equivalent dates and inspect month-end category stock.
A practical workbook exercise for retail analysts and regional managers. Work from the underlying records, compare the relevant totals and explain how you reached your result.
About the dataset
This original synthetic dataset contains 804 source records across three related tables. The records are fictional and designed for descriptive business analysis.
Dataset details
| Industry | Retail chains |
| Formats | Excel workbook and CSV files |
| Source records | 804 |
| Related tables | 3 |
| Fields across table schemas | 16 |
| Reporting cutoff | September 30, 2026 |
| Currency | USD for monetary fields |
| Source | Original synthetic data created for Jolene.pro Workbook Studies |
What's included
- Stores: 12 records and 4 fields. One store.
- Sales: 720 records and 7 fields. One transaction line; returned=true denotes a full return of that line.
- Stock: 72 records and 5 fields. One store-category month-end stock snapshot; the full 12-store, 3-category, 2-date panel is supplied.
Recommended analysis
- Which stores have the highest net sales after returns?
- Are store comparisons based on equal day counts?
- Which observed store-category snapshots show zero stock?
- Can a stock balance be added across months?
How to work with the data
Use the following relationships to connect the source tables:
- Sales.store_id → Stores.store_id
- Stock.store_id → Stores.store_id
Check the row grain before joining. Aggregate repeated child records to the required level before combining totals, so parent amounts are not counted more than once.
You can explore the workbook in Excel or Jolene.pro, or use the CSV files with SQL, Power BI and other analysis tools.
Definitions and assumptions
- A returned line reverses its entire quantity and gross amount.
- Stock data is a complete store-category grid at August 31 and September 30. Never sum month-end snapshots as flow volume.
Use the field definitions and nullable-column guidance in the included data dictionary. Interpret blank values and reporting dates according to the scenario notes.
Downloads
The complete dataset ZIP includes the Excel workbook, three matching CSV files, a data dictionary, scenario notes, a source-row preview and license details.
Reuse and attribution
These are synthetic records, not customer data or industry benchmarks. Reuse is allowed under Creative Commons Attribution 4.0 International (CC BY 4.0). Credit Jolene.pro and identify any changes you make.