Power BI.
Retail reporting examples built around the questions leaders actually ask.
Multi-store performance dashboard
The problem
A chain of 120+ stores across eight districts had performance data but no single view of it. Total sales were flat at a 0.20% comp, which tells leadership almost nothing — the question is which stores are driving that and why. Store-level profit, traffic, conversion, and occupancy costs lived in separate reports, so identifying an underperforming location meant pulling several files and reconciling them by hand.
The approach
Built a single-page executive view with a KPI panel (sales, comp, transactions, traffic, AVT, conversion, UPT), ranked top and bottom ten stores by profit, and a store-level profit and loss bar chart across the full fleet. Cross-filtering slicers for quarter, region, district, store type, and sales volume tier let a district manager isolate their own stores without a separate report. Drill-through pages let you select any store in the top or bottom ten and see its profit alongside store type, average units on hand, and annual rent.
Sole contributor – built the data model, all measures, and the drill-through pages.
What it showed
The top ten stores generated $9.3M in profit while the bottom ten lost $3.4M — a $12.7M swing inside a fleet posting a roughly flat comp. The drill-through explains most of it: the bottom ten skew heavily toward Major Metro locations carrying $2.9M+ in annual rent, while several profitable College-format stores run on under $500K. The problem in those locations isn’t selling; it’s occupancy cost against the volume the format can support.
Profit by sales volume tier also runs counter to intuition — the lowest-volume tier contributes more total profit than the flagship tier, which is worth pairing with store counts before drawing a conclusion.
Markdown sell-through and weeks of supply
The problem
Markdown inventory is capital sitting in a warehouse, and a single national sell-through figure hides where it’s actually moving. A category clearing well overall can be badly stuck in two distribution centres, and by the time that surfaces in a monthly report, the season is over and the markdown has to be taken deeper.
The approach
Built a weekly-grain report across five distribution centres, two divisions, and eight categories, calculating markdown sell-through percentage and weeks of supply from units on hand against units sold. Reporting weekly rather than monthly keeps the view close enough to act on, and holding warehouse, division, and category on the same grain makes it possible to isolate whether a problem is regional, categorical, or specific to one combination.
Created with an artificial data source.
What it showed
Across 1.65M units, markdown sell-through ran 27.7% at 3.61 weeks of supply — but the spread underneath that is wide. Calgary cleared 33.0% at 3.03 weeks while Montreal managed 25.5% at 3.92, a difference of nearly a full week of supply on comparable inventory. By category, fleece cleared fastest at 34.5% and hanging apparel slowest at 23.7% with 4.22 weeks of supply.
The weekly view is where it gets useful. Vancouver’s hanging apparel hit 8.3% sell-through in a single week, sitting on twelve weeks of supply, while the same category in Montreal cleared 78.1% that week. That’s a distribution question, not a markdown-depth question, and a monthly average would have buried it entirely.
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