When a customer lands on your Shopify store and opens chat with "Which lotion should I get for dry skin?" or "What's the difference between your silver and gold plan?", they're telling you exactly where they're stuck in the decision, and they're giving you one chance to unstick them before they leave.
Most stores treat product recommendation questions like support tickets. They answer them eventually, or they ignore them because chat is already overloaded. But recommendation questions aren't support. They're pre-sales conversations with high intent, and the store that answers them well and fast wins the sale.
This guide breaks down how to handle product recommendations in chat for Shopify stores: what customers actually ask, why speed and context matter, and how to structure answers that close the sale instead of extending the conversation.
Why product recommendation questions are different from support
A shopper asking "Which size should I order?" is in a fundamentally different state than someone asking "Where's my order?" The support question is reactive. The recommendation question is active buying intent.
When someone asks for a recommendation, they've already done some browsing. They're interested. They're just not confident enough to add to cart yet. If you don't help them decide quickly, they'll open a new tab or close the browser.
That's why product recommendation questions can convert at a higher rate than many other chat interactions when handled well. The customer wants to buy. They just need a push in the right direction, grounded in their specific situation.
What shoppers actually ask when they want a recommendation
Product recommendation questions come in a few recognizable patterns:
- Direct ask: "Which one should I get?" or "What do you recommend?"
- Use case or constraint: "What's best for sensitive skin?" or "I need something under $50"
- Comparison: "What's the difference between the Pro and the Max?" or "Is the large or XL better for a 6'2" guy?"
- Fit or compatibility: "Will this work with my iPhone 14?" or "I usually wear a medium, should I size up?"
- Confidence check: "Is this one good for beginners?" or "Will this actually help with back pain?"
Each of these is a slightly different flavor of the same question: help me feel confident I'm choosing the right thing. The answer format changes, but the job is the same.
Most stores answer these in one of two bad ways. They either give a generic "It depends on your needs!" response that doesn't help, or they dump a feature list and let the customer figure it out. Neither moves the sale forward.
The right answer references what the customer just told you, narrows the choice, and explains why in one or two sentences. Then it makes it easy to buy.
How to structure a recommendation answer that converts
A good product recommendation in chat has three parts:
- Acknowledge the constraint or use case: Repeat back what they said so they know you heard them.
- Make a specific recommendation with a reason: Name the product and explain why it fits their situation in plain language.
- Offer a path to purchase or one follow-up question: Link to the product, offer to add it to cart, or ask one clarifying question if you need it.
Here's a hypothetical example structure:
Customer: "Which serum should I get for dry skin?"
Good reply: "For dry skin, the Hydration Boost Serum is your best bet. It's got hyaluronic acid and squalane, which both pull moisture in and lock it down. Want me to add it to your cart, or do you have questions about how to use it?"
That answer gave a clear recommendation with a reason and offered a next step. It didn't list every serum in the catalog. It didn't ask three more questions. It helped the customer decide.
Compare that to a bad version:
Bad reply: "We have several serums that are great! You can check them all out on our Serums page. Let me know if you need help!"
That answer does nothing. It sends the customer back to browsing, which is where they were already stuck.
When to recommend one product vs. offering options
If the customer gave you enough information, recommend one thing. Don't hedge. "Based on what you said, I'd go with X" is more helpful than "You might like X or Y."
Offer two options only when there's a meaningful trade-off the customer needs to decide:
- Budget (a premium and a budget pick)
- Intensity or strength (mild vs. strong, beginner vs. advanced)
- Size or quantity (starter vs. full size)
Even then, explain the trade-off clearly and tell them which one most people in their situation pick. People don't want endless choice. They want confidence.
If you don't have enough information to recommend, ask one clarifying question. Not five. One.
Connecting recommendations to live Shopify catalog and stock
The worst version of a recommendation is suggesting a product that's out of stock or discontinued. It breaks trust instantly.
When you're answering recommendation questions, whether manually or with an AI agent, the reply needs to be grounded in your live Shopify catalog and current stock levels. That means checking what's actually available, what variants are in stock, and what the real price is right now.
Tools like Shopify Inbox and Kolton connect directly to your Shopify store, so recommendations are based on real inventory, not a static script written three months ago. If the product you'd normally recommend is sold out, you can recommend the next best option that's actually available and explain why.
This is especially important if you run promotions, seasonal collections, or have stock that moves fast. A recommendation is only useful if the customer can act on it immediately.
Upselling and cross-selling inside recommendation answers
Product recommendation questions are one of the best moments to increase average order value, but only if you do it without sounding like you're just trying to upsell.
The key is relevance. If someone asks "Which moisturizer should I get?", you can say (hypothetically):
"For dry skin, the Hydration Boost Serum works great. A lot of people also grab the Barrier Repair Cream to layer on top at night. The two together cover day and night hydration. Want both, or just the serum for now?"
That's an upsell, but it's tied to the original question and framed as a benefit, not a pitch. You're still helping them solve the problem. You're just helping them solve it better.
Avoid bundling random products or suggesting add-ons that don't connect to the question. "You should also check out our candles!" when someone's asking about serums is jarring and kills trust.
For more on how to increase order value in chat without sounding pushy, see How to Increase Average Order Value Through Conversation Without Sounding Pushy.
Handling "What do you recommend?" with zero context
Sometimes a customer opens chat and just says "What do you recommend?" with no other information. It's tempting to say "What are you looking for?" and start a long discovery process, but that often leads nowhere.
Instead, ask one specific question that splits your catalog in a useful way:
- "Are you shopping for yourself or as a gift?"
- "What's your biggest skin concern right now?"
- "Do you prefer loose leaf or tea bags?"
Pick the question that matters most for your catalog. The goal is to get enough signal to make a recommendation in the next reply, not to qualify them like a sales lead.
If they still won't give you information, recommend your best-seller and explain why it's the best-seller. Social proof is a recommendation when you don't have personal context.
When to hand off a recommendation question to a human
Product compatibility questions can often be answered from verified specifications. But some recommendation questions should go to a human:
- The customer has very specific technical requirements that need specialist knowledge
- They're buying in bulk or for a commercial use case
- They've already tried multiple products and are frustrated
- The question involves custom orders or personalization
- They're asking for medical, legal, or safety advice
For more on setting boundaries between what to automate and what to escalate, see Setting Guardrails for AI Support: What to Automate and What to Escalate.
The goal isn't to automate every conversation. It's to automate the straightforward ones so your team has time for the complex, high-value ones.
Using product recommendation questions to learn what's confusing
When you start tracking recommendation questions in chat, you'll notice patterns. If five people a week ask "What's the difference between the Pro and Max plan?", that's a signal that your product page isn't explaining it clearly.
If everyone asks "Will this work for curly hair?", that's a signal that you need to add that detail to the product description or create a comparison table.
Recommendation questions are free user research. They tell you exactly where your store is unclear. Fix the top five repeated questions on your product pages, and you'll reduce chat volume while increasing conversion.
Tools that connect product recommendations to your Shopify catalog
Manually answering recommendation questions works when you have one or two a day. When you have ten, or fifty, or when they come in at 11 p.m., you need a system.
Shopify's native Inbox offers chat and can surface some product context. Shopify Inbox agents can use web search for product reviews and feedback, and when customers sign in with Shop, can personalize product recommendations based on their preferences, sizes, and purchase history.
Kolton connects to your Shopify store and answers product recommendation questions in web chat, Instagram DMs, Facebook Messenger, email, and WhatsApp using your live catalog, stock levels, and order data. It can compare products, recommend based on customer input, check inventory, and explain why one option fits better than another. It also handles upsells and cross-sells in the same conversation, then hands off to your team when a question needs human judgment.
Every plan includes a review-and-approve mode, so the agent drafts recommendations and a team member sends them with one click until you're confident enough to let it reply autonomously. The Pro plan includes all channels, AI replies to public Instagram and Facebook comments, personalized follow-ups and abandoned-cart recovery, 3,000 AI replies per month, and a private customer support manager. You can see the full breakdown at kolton.ai/pricing.
What happens after the recommendation
Answering the question is step one. Step two is making it easy to act.
If you recommend a product, include a direct link or offer to add it to their cart. If they say yes, confirm it was added and ask if they're ready to check out or if they have other questions.
If they don't respond, follow up once after a few minutes: "Still interested in the Hydration Boost Serum? I can send you a link or answer any other questions."
Don't follow up five times. One is helpful. Five is annoying.
If they buy, great. If they don't, you've still built trust and made their decision easier. That's worth something even if it doesn't convert today.
For more on turning pre-purchase questions into conversions, see Pre-Purchase Questions That Actually Convert: What Shoppers Ask Before Buying.
Putting it all together
Product recommendation questions are one of the highest-leverage conversations you can have in chat. The customer wants to buy. They just need confidence. Your job is to give them a clear answer, grounded in what they told you and what's actually in stock, and make it easy to take the next step.
Do that well, and recommendation questions become one of your most reliable sources of revenue. Do it poorly, and they're just noise in an inbox that nobody wants to answer.
If you're handling more recommendation questions than your team can answer quickly, or if you're losing sales because they're coming in outside business hours, it's worth looking at how an AI agent can handle them for you. Kolton answers product recommendation questions across all your channels using your live Shopify catalog, closes sales, and hands off to your team when needed.
Learn more about how it works at kolton.ai/platform.
Key takeaways
- Product recommendation questions are high-intent pre-sales conversations, not support tickets. Speed and relevance matter.
- A good recommendation acknowledges what the customer said, names a specific product with a reason, and offers a clear next step.
- Always ground recommendations in your live Shopify catalog and stock levels. Suggesting an out-of-stock item breaks trust.
- Upsell and cross-sell only when it's relevant to the original question. Frame it as solving their problem better.
- Track repeated recommendation questions to find gaps in your product pages. If everyone asks the same thing, fix the content.
Frequently asked questions
- Should I recommend one product or give the customer options?
- If they gave you enough context, recommend one thing. Offer two choices only when there's a meaningful trade-off they need to decide, such as budget, intensity, or size, and explain which one most people in their situation pick.
- What if a customer asks for a recommendation but gives me zero details?
- Ask one specific question that splits your catalog in a useful way, like 'Are you shopping for yourself or as a gift?' or 'What's your biggest skin concern?' If they still won't engage, recommend your best-seller and explain why it's popular.
- Can an AI agent handle product recommendations, or does that need a human?
- Many straightforward recommendation questions can be handled by an AI agent that has access to your live Shopify catalog and stock. Hand off to a human when the question involves specialist technical knowledge, bulk or commercial orders, frustration with past purchases, or anything requiring medical, legal, or safety advice.
- How do I upsell during a recommendation without sounding pushy?
- Tie the upsell directly to the original question and frame it as a benefit. For example, if they ask for a moisturizer, suggest a serum to layer underneath and explain how the two work together. Avoid random add-ons that don't connect to what they're trying to solve.
- What should I do if the product they're asking about is out of stock?
- Recommend the next best option that's actually available and explain why it's a good alternative. Be honest that the original is out of stock and offer to notify them when it's back if that's an option.
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About the author
Mario
Mario Horvat builds Kolton, the AI agent that helps Shopify stores turn conversations across chat, DMs and social comments into orders. He focuses on how the agent answers shoppers from a store's live catalog, stock and order data.
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