EP367: Ecommerce Site Search Is Only As Good As Your Data: Add Context And Win In The Age of AI Discovery

<p><strong>Product Discovery for Ecommerce: Context, Personalisation and AI</strong></p><p><strong>Reasons to listen:</strong></p><ol><li>Learn how product context and knowledge graphs can improve search relevance.</li><li>Understand which shopper behaviours signal genuine intent and which may be noise.</li><li>Get practical ideas for testing merchandising choices and deciding where AI needs human oversight.</li></ol><p>How can ecommerce websites help shoppers find the right products when they don’t know exactly what to search for?</p><p>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.</p><p>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. </p><p>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.</p><p>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. </p><p>Combining those signals with product knowledge helps make personalisation more relevant to what a shopper wants now, rather than simply repeating what converted before.</p><p>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. </p><p>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.</p><p>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.</p><p><strong>Chapters:</strong></p><ul><li>[00:30] Introduction: product discovery, customer context and AI</li><li>[03:45] Why context matters in ecommerce product discovery</li><li>[07:00] Ontol