Product Discovery for Ecommerce: Context, Personalisation and AI

Reasons to listen:

1. Learn how product context and knowledge graphs can improve search relevance.
2. Understand which shopper behaviours signal genuine intent and which may be noise.
3. Get practical ideas for testing merchandising choices and deciding where AI needs human oversight.

How can ecommerce websites help shoppers find the right products when they don’t know exactly what to search for?

In this episode, James Gurd talks with Max Etheridge, Voyado’s Director for the UK and Ireland, about improving product discovery through richer product data, customer context and thoughtful use of AI.

They discuss why conversational search alone can’t fix weak product data, and how ontology and knowledge graphs connect products to categories, occasions and shopper needs.

That context can help a site understand a search like “something to wear to a summer wedding” and surface relevant products, even without an exact product description match.

The conversation also explores how ecommerce teams can distinguish meaningful purchase intent from passing interest. Repeated behaviours, such as refining searches, revisiting products or adding items to a wishlist, can be more informative than a single page view.

Combining those signals with product knowledge helps make personalisation more relevant to what a shopper wants now, rather than simply repeating what converted before.

James and Max examine practical ways to test product discovery, from mobile-versus-desktop strategies and filter layouts to content and product placement on category pages.

They also discuss who should control those decisions: algorithms can handle high-volume, lower-risk tasks, while people should retain oversight of brand-sensitive choices. Transparent reasoning and A/B testing help teams learn what works instead of relying on opinion.

Finally, they consider agentic search, the role of human merchandisers, and whether AI will change how teams use ecommerce platforms. One point runs through the discussion: new AI features are only useful when they solve real customer problems and have strong data and context underneath.

Chapters:

• [00:30] Introduction: product discovery, customer context and AI
• [03:45] Why context matters in ecommerce product discovery
• [07:00] Ontology and knowledge graphs explained
• [12:30] Reading shopper intent from behaviour
• [14:10] Personalisation beyond repeating past purchases
• [17:00] Who controls product discovery?
• [22:40] A/B testing merchandising strategies
• [25:20] Testing mobile experiences, filters and PLPs
• [30:30] Where AI should and shouldn’t make decisions
• [33:20] Agentic search and conversational discovery
• [37:05] Bringing content into product discovery
• [39:05] AI, user interfaces and the future of merchandising
• [46:40] Product roadmap and AI-first workflows
• [50:35] AI costs, guardrails and practical value