AI Product Recommendations

AI product recommendations are product suggestions generated by software from what a shopper has said, done or bought. The idea is as old as Amazon's "customers who bought this also bought" from the late 1990s. What has changed is who makes the recommendation.

Today they operate at two levels:

On-site recommendations. Traditional recommendation engines on retailer websites that suggest products based on browsing behavior, purchase history, and collaborative filtering. These are powered by platforms like Dynamic Yield, Nosto, and built-in Shopify/Magento tools.

AI assistant recommendations. AI shopping agents such as ChatGPT, Perplexity and Google AI Mode recommend products inside a conversation. They work from the shopper's question, the product information in their index and reviews, not from the shopper's history on any one site.

The second kind is the real change. When ChatGPT recommends a product, the shopper was never on that retailer's site. The assistant is an independent recommender, and what it knows about your products decides whether they are in the answer.

On-site engines and AI assistants use different signals:

On-site: you viewed these shoes, here are similar shoes. The recommendation follows what the shopper already did on your site. It reinforces existing intent and rarely introduces a new brand.

Assistant: "I want comfortable running shoes for flat feet under 150 dollars." The assistant searches everything in its index and recommends the best matches regardless of brand or retailer. The shopper may never have heard of the brand it picks.

That cuts both ways. A smaller brand can be recommended next to Nike and Adidas if its product information clearly describes what the shopper asked for. A major brand with thin product data can be left out entirely.

The intent is also stronger. Adobe Analytics found AI-referred visitors completed purchases at a rate 38 percent higher than traditional search visitors over Black Friday 2025, because the assistant had already matched the product to the need.

To get your products recommended:

Write the way shoppers ask. Descriptions should answer the questions people put to assistants: who is this for, what problem does it solve, how does it compare. Plain, specific language beats keyword-heavy SEO copy here.

Complete the attributes. Assistants filter and compare on material, size, weight, compatibility and use case. A missing attribute means the product cannot be matched to a question that mentions it. Product data enrichment is the work of filling those gaps.

Keep price and availability current. An assistant that recommends an out-of-stock product or the wrong price loses the shopper's trust and stops recommending the source.

Reach the platforms that matter. Each surface has its own inputs: a crawlable site, a product feed, merchant enrollment. Choose the ones your buyers use and keep the product information consistent across them with product feed management.

Paz does this work for ecommerce teams. A Paz expert tracks how your products appear in AI answers for the questions that matter to you, investigates gaps and corrects supported product information and shows you the change over time. See how the AI visibility service works.