Can AI recommendation engines increase average order value?

Why Retailers Invest in Recommendation Engines

Retailers build and refine recommendation engines partly because relevant product suggestions can encourage shoppers to add more to their basket than they originally planned. This can take the form of cross-selling — suggesting a complementary item, like a phone case alongside a new phone — or upselling, nudging a shopper toward a higher-value version of what they’re already considering. Because these engines can be applied consistently across an entire catalog and every shopper session, they’ve become a standard tool for retailers trying to grow order value without relying purely on discounting.

The premise is straightforward: a relevant, well-timed suggestion removes friction from a purchase a shopper might have made anyway, while an irrelevant one simply gets ignored.

Where and How the Effect Shows Up

The impact of recommendations tends to depend heavily on placement and timing. Suggestions on a product page, such as “frequently bought together,” often target shoppers who are already committed to a purchase and are weighing whether to add related items. Cart and checkout-stage recommendations work similarly, catching shoppers at a moment when they’re finalizing an order and may be receptive to a small addition. Homepage or category-page recommendations, by contrast, tend to influence broader browsing and discovery rather than driving an immediate basket increase.

Retailers generally test these placements against each other using controlled experiments, adjusting which recommendations appear where based on measured results rather than assuming any one approach works universally.

Why Relevance Is the Real Deciding Factor

The actual lift from a recommendation engine hinges on how relevant its suggestions are, not simply on how many recommendations are shown. Highly relevant suggestions can feel like helpful guidance, while irrelevant or repetitive ones tend to get ignored and, in some cases, can frustrate shoppers enough to reduce trust in future suggestions. This is why retailers invest heavily in refining the underlying models rather than simply maximizing the number of recommended items on a page.

Because the impact varies so much by category, retailer, and even time period, any specific percentage improvement claimed by a vendor or case study should be treated as context-specific rather than a guaranteed outcome for every retailer.

Bottom Line

AI recommendation engines can meaningfully increase average order value when suggestions are relevant and well-placed, which is why cross-selling and upselling remain a core use case. The actual effect varies widely based on category, placement, and execution, so it’s a real but not universally guaranteed benefit.