Nine real business questions answered step-by-step in Power BI. Each question is followed by a detailed instruction slide showing exactly what to click, drag, and configure.
Superstore sells 17 different subcategories of products. The marketing team needs to know where to focus investment — but the raw data table has nearly 10,000 rows and is impossible to read. You need to summarise and rank subcategories visually.
Superstore Dataset.csv → click Loadsubcategory → drop into X-axis field wellsales → drop into Y-axis field well. Power BI defaults to Sum — keep it.The CFO suspects Technology sells less than Furniture but may be more profitable per unit. She wants a single chart comparing both total quantity sold and total profit across the three main product categories side by side.
category → X-axis well. You will see three bars: Furniture · Office Supplies · Technology.quantity → Y-axis well. Keep as Sum.profit → Y-axis well (below quantity). Now each category has two side-by-side bars.The operations manager is planning staffing levels for the four US regions: West, East, Central, and South. She needs to understand the proportional share of orders each region handles — not the absolute numbers, but what percentage of the total.
region → Legend well. Four regions appear as different colours.sales → Values well. Click the dropdown arrow → choose Count (not Sum). This counts orders, not dollar values.Superstore serves three segments: Consumer, Corporate, and Home Office. The sales director wants to understand both the total profit each segment generates AND their average profit margin — to know which segment is most valuable per transaction.
segment → Columns well (or the Rows field if using a simple table)profit → Values → keep as Sum. Then drag profit_margin → Values → click dropdown → Average.segment → Legend. Drag profit → Values (Sum).| Segment | Sum of Profit | % of Total | Avg Profit Margin |
|---|---|---|---|
| Consumer | $134,119 | 46.8% | 11% |
| Corporate | $91,979 | 32.1% | 12% |
| Home Office | $60,299 | 21.0% | 14% ← |
| Total | $286,397 | 100% | 12% |
The board wants a clear picture of sales trajectory. They need to see monthly sales from January 2019 through December 2022, identify any recurring seasonal peaks, and understand whether the overall trend is improving. The raw date column currently loads as Text — you'll need to fix this first.
order_date column header → Column tools tab → change Data type from Text → Date.order_date still selected → Column tools → Groups icon (histogram) → Name: order_date (bins) · Group type: Bin · Bin size: 1 Month → OK.order_date (bins) → X-axis. The axis will show January 2019 … December 2022.sales → Y-axis → click dropdown → select Median (not Sum). Median is more meaningful than sum for typical order size.segment → Legend to split into three separate trend lines by segment.The finance team suspects that the aggressive discounting strategy is hurting the bottom line. They want to see every single order plotted as a point showing its discount rate against its profit — to find out whether a pattern exists and if orders with high discounts are losing money.
discount → X Axis. Then click dropdown next to it → "Don't summarize". This is critical.profit → Y Axis. Again click dropdown → "Don't summarize". Now each dot = one order.category → Legend. Dots are now coloured by Furniture / Office Supplies / Technology.sales → Size. Larger dots now represent higher-value orders — showing which big orders are losing money.The customer success team needs to identify VIP customers for a retention programme. With 793 unique customers, a table is unreadable. They want a visual where the most valuable customers stand out immediately in size — without needing to sort or scroll.
customer → Category well. Each customer gets a rectangle.profit → Values well. Keep as Sum. Rectangle size now = total profit per customer.2000 → click Apply filter.segment → Details to colour each box by whether the customer is Consumer, Corporate, or Home Office.
The geographic sales team wants to know where expansion efforts should focus and where losses are occurring. A table of 49 states is hard to scan. They want a map where each state is coloured by profitability — instantly showing geographic patterns, regional clusters, and any problem states.
state → Location well. Power BI automatically geocodes US state names. A yellow warning may appear — click to confirm it should be treated as geographic data.profit → Color saturation well. Keep as Sum. States with higher profit appear darker blue; states with losses appear lighter or different.state and profit → Tooltips well. Hover over any state to see exact profit figures.The pricing team wants a cross-tabulation showing the average transaction value for every category–subcategory combination. They need to see which specific product types command the highest average order value — to guide upsell strategies. A matrix (pivot table) is the right tool.
category → Rows well. You see: Furniture / Office Supplies / Technology as expandable row headers.subcategory → Columns well. 17 subcategory columns appear across the top.sales → Values well → click dropdown → Average. Blank cells are expected — e.g. Furniture will not have a Binders column.| Category | Bookcases | Chairs | Copiers | Furnishings | Phones | Accessories | Binders | Paper | Total Avg |
|---|---|---|---|---|---|---|---|---|---|
| Furniture | 503.86 | 532.33 | — | 91.67 | — | — | — | — | 348.55 |
| Office Supplies | — | — | — | — | — | 57.13 | 33.56 | 18.92 | 29.41 |
| Technology | — | — | 2,198.94 | — | 215.97 | — | — | — | 456.12 |
| Grand Total | 503.86 | 532.33 | 2,198.94 | 91.67 | 215.97 | 57.13 | 33.56 | 18.92 | 278.13 |