Margin pressure in retail has reached a point where minor adjustments are no longer enough. Promotions that once drove volume are less effective. Shopper loyalty is harder to secure. In this environment, category teams are expected to do more with less. And increasingly, they are turning to a lever that has long been underestimated: the assortment itself.
Are we offering the right mix of products at the right depth and price?
The answer rarely comes from spreadsheets. Recent pilots show that even mature retailers with well-managed assortments can unlock significant margin potential. In one example, a European multi-category retailer used AI-based assortment optimization to identify three key issues:
- Internal price cannibalization within product families
- Redundant long-tail listings that blocked better-performing alternatives
- Margin dilution from overlooked competitor pricing
Within three months, the retailer saw a margin improvement of 5% on the targeted assortment segment. This translated into a seven-figure EBIT uplift with zero additional marketing spend.
What changed? The team moved away from generic delisting thresholds and toward targeted, data-backed actions. For instance, they used AI to detect product clusters where multiple variants confused shoppers and caused price-based downgrading. Instead of removing all slow movers, they consolidated overlapping products and repositioned the remaining ones with clearer pricing logic. This alone recovered margin without reducing assortment breadth.
In other cases, the Zenline flagged items that appeared healthy in terms of revenue but were cannibalizing higher-margin alternatives. These internal conflicts often remain invisible in traditional KPI reports. But when product relationships are analyzed holistically, the margin cost of keeping such items becomes evident.
One of the most powerful aspects of this approach is its ability to simulate the financial and behavioral impact of decisions before they are made. What happens if a product is removed? Will shoppers switch to another item, leave the category or go to a competitor? AI-based tools use historical data and market signals to predict these shifts, reducing the risk of unintentional revenue loss.
Pricing adjustments also contributed to the margin uplift. By comparing internal contribution margins with competitor benchmarks, the team identified items that were underpriced relative to their perceived value. Small corrections in these zones led to immediate gains without affecting conversion.
Across categories and markets, retailers who embed AI into their assortment and pricing workflows are seeing similar patterns. They achieve more precise execution, faster decision cycles and fewer blind spots in margin management. And perhaps most importantly, they reduce the burden on their teams. Instead of spending weeks preparing quarterly reviews or cleaning Excel sheets, category managers can focus on evaluating recommendations, aligning with commercial goals and steering the business.
Zenline enables this shift by combining sales, margin, inventory and competitor data into one decision system. It identifies margin leaks, product overlaps and pricing inconsistencies and translates them into clear recommendations. Instead of static dashboards or complex exports, category teams receive actionable decisions they can validate and implement. In pilot cases, this has led to EBIT gains of up to 5% within months and a return on investment of up to ten times in the first year.