Retail analytics insights
Guides, frameworks, and strategies for retail teams that want to move from dashboards to decisions.
Fill Rate Math: Why 95% Costs You More Than You Think
A 95% fill rate sounds healthy until you do the basket math. Lost units, walked customers, and substitution drag turn that 5% gap into a 1.5-3% revenue hit. Here's the decomposition.
GMROI by Category: The $3-Return SKUs Hiding in Your Mix
Chain-level GMROI of $2.40 hides categories returning $0.80 and categories returning $5.10. How to decompose the number and where the rebalancing math actually lives.
Sell-Through Curves: The Weekly Signal That Tells You When to Markdown
Most retailers markdown on the calendar. The sell-through curve says when. How weekly ST% trajectory predicts terminal sell-through 6 weeks before the season ends.
Units Per Transaction: A Cleaner Demand Signal Than Total Sales
Total sales is a lagging composite. UPT is a leading indicator of merchandising health, staffing impact, and assortment fit. Why it moves 2-3 weeks before revenue does.
Same-Store Sales Decomposition: Traffic, Conversion, ATV
A flat comp can hide three problems and a tailwind. Decomposing same-store sales into traffic, conversion, and ATV is the only way to know what to fix.
Inventory Turns by Store: The Variance Math Nobody Runs
Chain-level turns of 6.2 includes stores at 4.1 and stores at 9.8. The variance is the opportunity. Why the average is a useless number and what to measure instead.
Store Conversion Rate: Why Your Footfall Counter Is Lying
Door counters inflate traffic by 18-35%. Staff in, deliveries, employee breaks, browse-throughs. Why conversion rate is unreliable until you fix the denominator.
Basket Penetration: The Cross-Sell KPI Most Retailers Ignore
How often does a category show up in baskets that contain its adjacent category? That ratio predicts attach revenue better than any promo plan. Here's how to measure it.
Forecast WMAPE by SKU Tier: One Number Hides Three Problems
Chain-level WMAPE of 22% looks fine. By tier it's 14% on A-items and 41% on C-items. The aggregate hides the categories where forecast error costs you the most.
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