A recommendation earns trust when the shopper can see why the product fits their request.
Ask for the constraint that changes the answer
A shopper looking for a jacket may care most about rain, warmth, fit, or price. Asking one useful follow-up can make the next recommendation much more relevant. Avoid turning discovery into a long questionnaire.
Use the shopper’s own words. If they say they need something for a weekend trip, clarify the conditions rather than assuming a destination or style.
Let product information do the work
Keep product descriptions, materials, variants, and sizing guidance specific. Distinguish an explicitly documented attribute from a marketing adjective. If compatibility or suitability is not confirmed, the assistant should say so.
Explain the choice
Show a small set of options with a short reason for each. Explain relevant tradeoffs such as price, dimensions, or material. A shopper should be able to make a decision without opening a dozen product tabs.
Keep unavailable variants and missing information visible. Do not promise that an item is in stock without an appropriate current source.
Measure helpfulness without overstating attribution
Review whether suggested products fit the stated needs and whether shoppers ask the same question again. Where your analytics connect a conversation to an order, describe that as assisted attribution. It does not establish that the recommendation caused the purchase.
Try the idea in your own store.
Explore the assistant and see how a useful conversation feels.
Experience Convi