Can AI determine where products should be placed on shelves?

From Intuition to Data-Backed Shelf Decisions

Shelf placement has always mattered to retail sales, and merchandisers have long understood general principles, like the value of eye-level positioning or the benefit of placing complementary products near each other. What AI adds to this established knowledge is the ability to analyze detailed sales data tied to specific shelf positions across many stores, identifying patterns that are more precise and store-specific than broad, generalized merchandising rules. Rather than relying solely on industry conventions, retailers can use AI to ground shelf placement decisions in their own actual sales performance data.

This doesn’t mean established merchandising principles are discarded — many AI-driven findings actually reinforce well-known patterns — but it does allow for more granular, evidence-based refinement of placement decisions.

How the Analysis Actually Works

AI models used for shelf-placement analysis typically combine sales data with the specific physical location where a product was displayed, allowing the system to compare performance across different positions, such as top shelf versus eye level, or end-cap displays versus standard aisle placement. This analysis can confirm and quantify long-understood patterns, such as the value of eye-level and easy-reach positioning, while also surfacing more specific, less obvious insights particular to a retailer’s own product mix and customer base. AI can also identify beneficial product adjacencies by analyzing which items are frequently purchased together, informing decisions about which products should be placed near each other to encourage complementary purchases.

Because this analysis relies on historical sales data tied to placement, it tends to work best for established products with a meaningful sales history, and is less reliable for brand-new items that haven’t yet accumulated enough placement-specific data.

Where Commercial Factors Still Shape the Final Decision

Shelf placement in many retail categories isn’t determined purely by sales-optimization data. Supplier agreements, sometimes called slotting arrangements, can involve suppliers negotiating for premium shelf positions as part of broader commercial relationships with a retailer, independent of what a pure sales-data analysis might recommend. This means AI-generated placement recommendations function as one significant input among several factors retailers weigh, rather than an automatic, final determination of shelf layout. Category managers typically review AI recommendations alongside these commercial considerations before finalizing actual shelf arrangements.

This blend of data-driven insight and commercial negotiation reflects the reality that retail merchandising decisions often serve multiple objectives beyond pure sales optimization for any single product category.

Bottom Line

AI can generate data-driven recommendations for shelf placement by analyzing sales performance tied to specific positions and identifying beneficial product adjacencies, informing decisions merchandising teams ultimately review and finalize. Commercial factors like supplier slotting agreements also continue to shape final placement decisions alongside these AI-driven insights.

Go deeper

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