JD Sports has deployed Algolia as the retailer’s governing intelligence layer on which to build its agentic commerce strategy.

Announced Sept. 22, the companies seek to enable product discovery and shopping through agentic artificial intelligence (AI) platforms. JD Sports initially implemented Algolia into its tech stack in 2024.

Since then, JD Sports said:

• Revenue from search has increased 22%.
• Search click-through rates have increased 7.65%.
• Product listing page (PLP) click-through rates have increased 73%.
• PLP revenue has increased 16%.

JD Sports is No. 28 in the Europe Database. The market research tool tracks the region’s largest online retailers, ranking them by annual ecommerce sales and more.

The retailer said it operates more than 4,800 stores globally. It also has more than 9 million active loyalty program members.

Algolia said it 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.

JD Sports said 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.

JD Sports, working with Algolia Professional Services, moved its website to a MACH ecommerce architecture. MACH is an acronym referring to four technology principles:

1. Microservices. This refers to individual, small features that run as independent services rather than part of one large codebase.
2. API-first. Features communicate through application programming interfaces (APIs). That enables merchants to plug in third-party tools.
3. Cloud-native. The site fully runs on the cloud.
4. 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.”

How JD Sports is optimizing for agentic commerce

JD Sports now uses dynamic re-ranking (DRR). That means it uses real-time click and conversion signals to identify trending products and automatically adjust their placement, the companies explained. It does so within merchandising rules that JD Sports’ team defines. The retailer can see and reverse each adjustment.

The retailer saw a 2.2% increase in click-through rate from DRR. Additionally, DRR led to a 4% increase in JD Sports’ add-to-cart rate as well as 4% growth in its conversion rate.

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.”

The same process that displays search results now works for AI agents, the companies explained. That gives JD Sports a way to “control relevance” across shopping channels.

“JD Sports understood before most that discovery is a growth engine, not a website feature, and a 22% lift in search revenue is a P&L result, not an IT metric,” said Stephen Lynch, CEO at Algolia.

Lynch said in a category where demand shifts weekly, “winning takes intelligence merchandisers can inspect and control, not a black box they’re asked to trust.”

He added that he sees the next wave of retail growth going to the companies whose catalogs agents can shop. To enable that, retailers have to make their catalogs structured, current and accurate, he said.