JD Sports boosts sales with AI-powered search upgrades

JD Sports has added Algolia as its core intelligence layer to support an agentic commerce approach, the companies announced on September 22.

AI-driven discovery gains

The retailer began integrating the search provider into its technology stack in 2024, aiming to improve product discovery through AI-powered platforms.

Since the rollout, revenue linked to discovery functions has risen 22%, overall click-through rates have climbed 7.65%, and product-listing page engagement is up 73%.

According to a European retail ranking, the firm sits at number 28 among the region’s largest online sellers. It operates more than 4,800 stores worldwide and serves over 9 million active loyalty members.

Algolia primes JD Sports’ catalog so AI agents can find the answers they need for “conversational, intent-heavy queries.” Still, it uses the same catalog and ranking logic that powers JD Sports’ website.

As its ecommerce operations and product catalogs expanded, merchandising teams were spending “significant time manually tuning results, boosting products and maintaining static rules.” That model could not keep up with shifting trends, seasonality and shopper behavior, according to the companies.

MACH architecture adoption

Working with Algolia Professional Services, the company migrated its site to a MACH ecommerce architecture. MACH is an acronym referring to four technology principles: Microservices. This refers to individual, small features that run as independent services rather than part of one large codebase. API-first. Features communicate through application programming interfaces (APIs). That enables merchants to plug in third-party tools. Cloud-native. The site fully runs on the cloud. Headless. The front end is separate from the back end.

“Replacing a highly manual search and merchandising process with an AI-native foundation changed how we operate,” said Kristin Matter, vice president of digital operations at JD Sports. “We respond faster to changing shopper behavior and emerging trends, our merchandisers spend their time on strategy instead of tuning, and the revenue gains follow.”

Dynamic re-ranking now uses real-time click and conversion signals to surface trending items, automatically adjusting placement within pre-set merchandising rules that staff can monitor and reverse.

From this capability, click-through rates improved by 2.2%, add-to-cart actions rose 4%, and overall conversion increased the same 4%.

JD Sports and Algolia said they’ve begun using AI “to proactively surface” emerging search trends, inventory shifts and merchandising opportunities. They also have been using it “to tailor discovery to individual shopper intent.”

Because the same ranking engine feeds both human users and AI agents, the retailer maintains control over relevance regardless of the shopping interface.

Impact on performance metrics

Stephen Lynch, CEO of Algolia, noted that discovery functions act as a growth engine, with the 22% lift in related revenue reflecting a direct profit-and-loss impact.