Category teams replacing the Monday spreadsheet.
Retailloom's customers are regional and mid-market chains — 9 to 50 stores — where the category manager is the analytics department. No dedicated data team, no data warehouse, no consultant on retainer. Just a POS export on Friday and decisions that need to be made by Tuesday.
The problem
Northgate's category team was running a single assortment plan across all 18 stores — set at the beginning of the season and reviewed once mid-year. The category manager managed Home Goods and Seasonal simultaneously, which meant the review was always a five-hour spreadsheet exercise the week before the planogram reset.
The chain had 8 stores in suburban locations and 10 in urban cores. The assortment was tuned for the suburban profile. Urban stores were routinely running out of several SKUs by mid-week while suburban stores had excess stock sitting on the shelf. Nobody had visibility into this pattern.
How they use Retailloom
Northgate connected their NCR Counterpoint POS export on a Monday. By Wednesday of that week, the first decision sheet identified 14 SKUs occupying more shelf space than their sell-through justified. It also flagged three SKUs running out consistently in urban locations that weren't being reordered because the chain average looked fine.
The category manager now spends Monday morning reviewing the ranked list, Tuesday in buyer conversations, and the rest of the week on category development instead of report-building.
The problem
Prairie Home Hardware has a nine-store network across two states. The buyer manages both Hand Tools and Power Tools, roughly 1,800 active SKUs across both categories. The spring season drives the highest sell-through, but it also creates the most pressure: new SKUs from the annual line review need space, old SKUs need to be cleared.
The buyer was relying on a season-end sell-through report to inform these decisions. By the time that report was ready, the spring floor set had already been finalized. The data was always one season behind the decisions it was meant to inform.
How they use Retailloom
Prairie Home connected via CSV upload — they export a weekly transaction file from their POS every Friday. The Retailloom decision sheet arrives Monday morning. During the spring planning window, the buyer ran a side-by-side comparison of Retailloom's SKU rankings against the traditional end-of-season report. In 78% of cases where the two disagreed, Retailloom's weekly view proved more accurate in hindsight.
The buyer now uses Retailloom's cut list as the first input to the annual SKU rationalization meeting, rather than building that list from scratch from last year's sell-through averages.
The problem
Lakeview Outdoor has stores ranging from busy lake-resort destinations to urban-edge formats. These two types of stores buy different customers at different points in the trip-planning cycle — but they had been running the same assortment plan for three years because there was no analytical infrastructure to support differentiated planning.
The category manager suspected the stores were different. But proving it — and getting buy-in from the VP Merchandising to run separate assortments — required data that wasn't available from the existing reporting stack.
How they use Retailloom
The store-cluster analysis was the first output Lakeview asked for during onboarding. Within the first week, Retailloom's clustering had automatically separated the 22 stores into four performance groups — not the two regional clusters that had been used historically. The recreational-hub stores formed a distinct high-velocity cluster across the camping category, while urban-edge stores showed lower velocity but higher margin on technical items.
The category manager used this output to build the business case for a differentiated assortment plan. The VP Merchandising approved a two-cluster strategy for the fall season based on Retailloom's store groupings.
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Join category teams replacing spreadsheet reviews with weekly ranked lists.