Retail analytics
Where a superstore makes — and loses — its money
Four years of orders, sliced by time, product, discount and geography to find the profit leaks hiding behind healthy revenue.
Original project · May 2026PythonPandasPlotlyReportLab
The question
Revenue is growing. Is profit keeping up, and if not, where is it leaking?
The original project
In my Retail Superstore project I analysed retail data across seven operational dimensions and wrote a formal, multi-page report with visualizations and recommendations. This page applies the same approach to the public Sample – Superstore dataset.
At a glance
Revenue$2.3M2014–2017
Profit$286.4K
Profit margin12.5%
Orders5,009
The analysis
Monthly revenue and profit
2014–2017, USD
- Revenue
- Profit
Profit by sub-category
Total profit, USD — red bars lose money
- Copiers$55.6K
- Phones$44.5K
- Accessories$41.9K
- Paper$34.1K
- Binders$30.2K
- Chairs$26.6K
- Storage$21.3K
- Appliances$18.1K
- Furnishings$13.1K
- Envelopes$6,964
- Art$6,528
- Labels$5,546
- Machines$3,385
- Fasteners$950
- Supplies-$1,189
- Bookcases-$3,473
- Tables-$17.7K
Profit margin by discount level
Profit ÷ revenue for order lines in each discount band
Profit margin by region and category
Teal = profitable, burgundy = losing money; stronger colour = further from zero
FurnitureOffice SuppliesTechnology
Central-1.8%5.3%19.8%
East1.5%20%17.9%
South5.8%15.9%13.4%
West4.6%23.8%17.6%
Top 10 states by revenue
Click a column to sort
| California | $457.7K | $76.4K | 16.7% | 1,021 |
| New York | $310.9K | $74K | 23.8% | 562 |
| Texas | $170.2K | -$25.7K | -15.1% | 487 |
| Washington | $138.6K | $33.4K | 24.1% | 256 |
| Pennsylvania | $116.5K | -$15.6K | -13.4% | 288 |
| Florida | $89.5K | -$3,399 | -3.8% | 200 |
| Illinois | $80.2K | -$12.6K | -15.7% | 276 |
| Ohio | $78.3K | -$17K | -21.7% | 236 |
| Michigan | $76.3K | $24.5K | 32.1% | 117 |
| Virginia | $70.6K | $18.6K | 26.3% | 115 |
What the data shows
- The business sold $2.3M over 2014–2017 at a 12.5% profit margin, and 2017 revenue grew 20.4% year over year.
- Seasonality is strong: Q4 (Oct–Dec) brings in 38.2% of all revenue.
- 3 sub-categories lose money. Tables is the worst at -$17.7K, while Copiers lead with $55.6K profit.
- Discounting is the main leak: order lines discounted by up to 20% still earn a 11.9% margin, but anything above 20% loses money on average (-37.3%).
- California is the top state by revenue, but 10 states run at a loss overall.
Recommendations
- Cap routine discounts at 20% and require approval above it — overall margins stay positive up to that line, though Storage, Supplies and Tables lose money even below it.
- Re-price or re-source Tables and the other loss-making sub-categories before pushing volume.
- Plan inventory, staffing and promotions around Q4, which carries 38.2% of revenue.
- Review the loss-making states for discount-heavy selling rather than cutting them outright.
How the analysis works
- Parsed 9,994 order lines and checked totals against the published dataset.
- Built a month-by-month revenue and profit series with empty months filled as zero.
- Ranked 17 sub-categories by total profit and banded discounts to test their effect on margin.
- Cross-tabulated margin by region and category, then ranked states by revenue.
Caveats
- The dataset is fictional teaching data, so treat the numbers as a demonstration of method.
- Profit is recorded per order line; overheads such as rent and wages are not included.
Methods
- Descriptive statistics
- Time-series trend
- Margin & discount analysis
- Geographic segmentation