Customer stories
Category teams who moved from gut feel to data-driven ranging
Three examples of how European retail teams are using Zenline to find white-space revenue, cut range review preparation time, and close competitive assortment gaps faster than was previously possible.
Mid-size grocery chain identifies hidden revenue in ambient and chilled categories
A Central European grocery retailer with 800 stores across three markets was managing category assortment decisions through quarterly syndicated data and manual competitor store walks. The category team had a strong instinct that they were missing growth in several sub-segments, but could not quantify the gap or prioritise which segments to address first. With multiple categories under management and a buyer review cycle that moved faster than their data could, decisions were being made conservatively — choosing not to range a new SKU rather than risking a miss.
After connecting their POS exports and EDI 852 feeds to Zenline, the category team ran a white-space analysis across their ambient grocery and chilled convenience categories. The platform identified 23 SKU slots where three or more tracked competitors were carrying products with measurable sell-out velocity that the retailer did not stock. When scored by opportunity value — combining sell-out velocity data with the number of competitors distributing the item — six SKUs rose to the top as clear, quantifiable opportunities.
Before Zenline, we knew we were probably missing something in plant-based chilled. We just couldn't show anyone the number. Now we walked into the buyer review with a ranked list, a competitive context, and a revenue estimate. That is a different kind of conversation.
— Head of Category, Ambient & Chilled
Health and beauty pharmacy chain halves range review preparation time
A pharmacy-anchored health and beauty chain operating 220 stores had a weekly category review process that was consuming two full working days of preparation time from the category management team. The primary bottleneck was data reconciliation: POS exports from their store management system used a different product code structure than their distributor EDI feeds, which in turn did not match their ERP's SKU master. Each weekly review required a manual mapping exercise that the team had largely automated in Excel — but which still required oversight, error-checking, and periodic re-calibration when source system formats changed.
The category team connected all three data sources to Zenline's normalization layer and configured a weekly briefing cadence. The platform handled EAN/GTIN reconciliation across sources, applied timing-gap interpolation to weeks where distributor feeds arrived late, and pre-assembled the competitive view before the team opened their Monday briefing. Data provenance was annotated throughout — interpolated data spans were clearly marked so the team knew exactly where the underlying data was complete and where estimates had been applied.
The two days we were spending on prep was not analysis. It was formatting. Every senior person on the team was doing formatting work before they could do their actual job. That time is now back, and we are using it on supplier strategy and ranging decisions instead.
— Commercial Director, Health & Beauty
Convenience store group scales assortment gap detection across a large, fragmented estate
A convenience store group operating 1,400 locations across two markets had a structural assortment challenge: their estate included both company-operated stores and franchise locations, with meaningfully different ranging compliance between the two. The category team knew that significant portions of the franchise network were not carrying their full recommended range — but identifying where, at what scale, and for which SKUs was a manual audit process that the team could only run quarterly, and only for a sample of locations. The gap between recommended assortment and actual in-store ranging was invisible between audits.
Connecting Zenline to their POS estate — which covered both company-operated and franchise locations — gave the category team a continuous distribution gap view at the individual store level. Rather than quarterly sampling, they could see on any given week which store groups were underlisted on which SKUs, how that compared to the recommended ranging guide, and which gaps represented the largest revenue opportunity by volume. The automated gap detection replaced a manual process that had required two analysts working full-time during audit weeks.
We went from a quarterly picture to a weekly one. And the weekly picture is actually more accurate because it is based on every store, not a sample. We found distribution gaps in our franchise network we had simply never known about — and we could prioritise fixing the highest-value ones first.
— Head of Category & Ranging
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