In a recent discussion, James Gurd and Voyado’s Max Etheridge unpack a critical challenge for modern e-comm…

In a recent discussion, James Gurd and Voyado’s Max Etheridge unpack a critical challenge for modern e-commerce: helping shoppers find the right items when they lack exact search terms. The core consensus aligns perfectly with the reality of modern catalog distribution—flashy AI features like conversational and agentic search cannot fix weak, unstructured product data.

Listen to the episode here: Inside Commerce Podcast

Key Takeaways for E-Commerce Teams:

Ontology Over Keywords: Connecting products to occasions and needs via knowledge graphs is what allows a site to accurately parse ambiguous queries like "something to wear to a summer wedding" without relying on exact-match descriptions.

Qualifying True Purchase Intent: A single page view is just passing interest. Real-time personalization should be driven by deeper behavioral signals—search refinements, return visits, and wishlist additions—to serve what the shopper wants now, rather than blindly repeating what converted in the past.

Strategic AI Delegation: Algorithms are built to handle high-volume, lower-risk merchandising tasks at scale. Human teams must retain oversight over brand-sensitive placements, using transparent A/B testing rather than guesswork to optimize filter layouts and category pages across devices.

The Foundation of Agentic Search: As AI reshapes how e-commerce platforms operate, new features will only drive revenue if they solve genuine customer friction points using a bedrock of clean, rich, and highly contextual product data.