Filters and search work when shoppers know the words for what they want. Many don't. "A gift under $100 for a runner" or "a sofa that works with two cats" doesn't map neatly to a filter. An AI agent can turn that kind of request into two or three good suggestions, if it's set up well.
Recommend from live data only
The agent must recommend from your current catalog, with live price and stock. Recommending something that's sold out, or quoting last month's price, is worse than not recommending at all. Connect your store or product feed and keep it in sync.
Ask one good question, not a quiz
The best human shop assistants ask one or two clarifying questions, then make a suggestion. Agents should do the same:
- "Is this for road running or trails?"
- "Do you need it to be machine washable?"
Avoid five-question flows. Shoppers came for an answer, not a survey.
Suggest a few, and say why
Three options is usually right. For each, give a reason that connects to what the shopper said:
"The trail belt is the most popular with runners who carry a phone. The merino socks suit cold mornings. Both are under $40, so you could get them together."
Reasons build trust. A bare list of links feels like an ad.
Compare honestly
When a shopper asks "which is better?", the agent should explain real differences from your product data (battery life, materials, dimensions) and say which suits which need. If two products are nearly the same, say so. Overselling in chat leads to returns later.
Know when to hand over
For high-value or complex purchases, offer a person:
- "Would you like to see these on a quick video call with one of our team?"
A live video call or a scheduled consultation often closes what chat alone can't.
Put recommendations where questions happen
- Product pages: "What would go with this?"
- Category pages: "Help me choose"
- Instagram DMs: replies to product stories
- Empty search results: "Couldn't find it? Ask us."
Measure
- Recommendation clicks
- Add-to-cart from recommended products
- Conversations that end in an order
- Recommendations that led to returns (a sign the comparison wasn't accurate)
Good recommendations feel like a knowledgeable friend, not a pop-up. That comes from accurate data, one good question and an honest reason for each suggestion.
