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When a shopper asks an AI tool for a product recommendation, they may describe what they need the product to do rather than name a brand or model.
Product pages can help the tool evaluate that request by explaining relevant uses alongside product features.
Consider a hypothetical product page for a pair of boots. It lists “leather upper, black,” while a shopper asks for “boots that survive a muddy festival weekend.” The page describes materials and color, but the shopper wants to know whether the boots will suit those conditions.
In this example, the description leaves that use case unanswered.
seoClarity will host “How AI Shopping Agents Decide Which Products to Recommend” on Wednesday, October 28, at 2 p.m. ET. Chris Sachs, VP of Value Enablement, and Aris Vrakas, Product Lead, will discuss how product details affect discovery in AI-assisted shopping.
This webinar will show you how to connect product details with shopper language, giving AI shopping tools the context to match a relevant product to a shopper’s request.
What Agentic Commerce Means For Product Discovery
Product-page language, recommendation frequency against competitors, and the information AI can read from rendered pages and structured data are distinct areas for ecommerce SEO teams to inspect when a relevant product does not appear in AI shopping recommendations.
This upcoming AI search webinar for ecommerce will focus on product discovery rather than checkout inside an AI service.
Sachs and Vrakas will cover:
- How complete product details and shopper language can help AI match products to the requests people make.
- Steps to measure how often Google and ChatGPT recommend individual products compared with competing products.
- Ways to check rendered product pages and structured data for details that AI may not read.
If you cannot attend live, register to receive the recording!
