How do AI product recommendations work?
In a shopping conversation, an assistant interprets what the visitor needs, finds relevant products in the store catalog and explains why the options may fit. Follow-up questions can narrow the choice by budget, intended use, material or available variant. This is conversational product discovery: the shopper adds context rather than repeatedly changing a search filter. Different apps implement matching differently. Evaluate the visible result against your real catalog rather than assuming every assistant uses the same recommendation method.
An example of conversational product recommendations
Imagine a visitor asking for a lightweight bag for a laptop and a daily train commute. Useful catalog details include laptop compartment dimensions, bag weight, material and price. The assistant should explain which available bags match those needs, then ask about laptop size if compatibility is unclear. It should not claim that a bag is waterproof when the description only says water-resistant. This is an illustrative scenario; a recommendation is only useful when the product information supports it.
Start with the question
A shopper rarely arrives with the same vocabulary as your catalog. They may describe a use, a preference, or a problem. Start by reviewing the questions your team already receives and checking whether your product information can answer them.
Make the differences clear
Products that look similar need clear distinctions. Keep sizes, materials, compatibility, and available variants accurate. A useful comparison explains the differences that matter to a purchase, rather than repeating two descriptions side by side.
Make the next step visible
After an answer, the customer should know what to do next. That might mean opening a product page, choosing a variant, or asking a follow-up question. Keep that action close to the information that supports it.
Review the conversations
Look for questions that repeat and answers that need more context. Update the underlying product information first. A better catalog helps shoppers whether they use an AI agent, site search, or a conversation with your team.
Keep policy answers next to product answers
Shoppers rarely separate “does this fit” from “can I return it”. Shipping windows, return rules, and warranty terms belong in the same place an assistant reads your product data from. When those answers are missing, a question that could have ended in a purchase turns into a support ticket instead.
Explore this workflow in Dejor.
See how shopify ai chatbot and shopping assistant fits into your store, including setup steps and common questions.