Product Recommendations in Chat: A Practical Guide for Shopify Stores

Short answer: good product recommendations in chat start with one or two smart questions (what the shopper needs it for, what matters most, their budget), followed by one to three suggestions with a clear reason for each. Ground every suggestion in real product and stock data, add a related item only when it genuinely helps, and hand off to a person for complex or high-value requests.

"Which one should I get?" is one of the most valuable messages a Shopify store can receive. The shopper is already interested; they just need help choosing. Answer it well and you turn a browser into a buyer. Answer it poorly, or not at all, and they go back to scrolling. This guide shows how to recommend products in chat the way a great in-store associate would.

Why recommendations belong in chat

Recommendation widgets ("You may also like…") are useful, but they can't ask questions. Chat can. That matters because:

• Shoppers often don't know the right search terms. They know their problem ("something warm for hiking in spring"), not your product names.
• Choice overload is real. A catalog with dozens of similar items can stall a decision.
• Context changes the answer. A gift, a replacement and a first purchase each need different suggestions.

A short conversation narrows the field quickly and makes the shopper feel looked after.

Step 1: Ask before you recommend

The best associates ask before they suggest. In chat, keep it to one question per message, and stop as soon as you know enough.

Four questions that do most of the work

1. Purpose: "Is this for you or a gift?"
2. Priority: "What matters most: comfort, durability or style?"
3. Constraints: "Any size, color or budget in mind?"
4. Timing: "When do you need it by?"

You rarely need all four. If the shopper already said "gift for my dad, under $80", skip straight to suggestions.

Read the signals already available

Before asking anything, use what you know: the product page they're on, items in their cart, and (for returning customers) past purchases. Asking for information you already have feels careless.

Step 2: Recommend one to three options, with reasons

Once you understand the need, suggest a small number of products, each with a one-line reason.

Based on what you said, I'd look at these two:

1. Trailhead Fleece: lightweight, packs small, good for cool mornings.
2. Ridge Softshell: a bit warmer and wind-resistant if you'll be on exposed trails. Both are in stock in your size. Want a quick comparison?

What makes this work:

• Few options make deciding easier.
• Reasons tied to the shopper's words prove you listened.
• Stock confirmation removes a common doubt.
• An open question keeps the conversation moving.

Step 3: Make comparisons easy

Many recommendation chats end with "What's the difference?" Prepare for it. For your best-selling product families, write short, honest comparisons:

Question	Option A	Option B
Best for	Everyday use	Heavier use
Fit	Relaxed	Slim
Care	Machine wash	Hand wash

Your chat assistant can use these to answer consistently, instead of improvising.

Step 4: Cross-sell only when it helps

A relevant add-on can be a genuine service: the right charger, a care kit, matching accessories. The test is simple: would a knowledgeable friend suggest this?

Good cross-sells:

• Complete the product ("This lens needs a filter size of 58 mm. Here's one that fits.")
• Prevent a problem ("This leather bag pairs well with a protector spray.")
• Match an occasion ("Want gift wrapping or a card with that?")

Avoid pushing unrelated or more expensive items just because you can. Shoppers notice, and trust drops.

Step 5: Keep recommendations accurate

Nothing hurts more than recommending something that's out of stock or doesn't fit the shopper's needs.

• Connect recommendations to live product data: variants, availability and key specs.
• Keep product information clean: consistent sizing charts, materials and compatibility notes.
• Review conversations regularly: if shoppers keep asking a question your data can't answer, add that information.

Step 6: Know when a human should step in

Some requests need a person: custom orders, large or bulk purchases, technical fit questions, or a shopper who's unhappy. Set clear handoff rules and pass along a summary of the conversation so your team can pick up instantly.

Mistakes to avoid

• Interrogating shoppers. Ask only what you need, one question at a time.
• Listing ten products. Suggest fewer, with reasons.
• Recommending without checking stock.
• Upselling by default. Recommend the best fit, even if it's the cheaper option. That honesty earns repeat customers.
• Answering in a different voice than your brand. Keep tone consistent with your store.

How TaggoAI helps

TaggoAI for Shopify gives your store an AI assistant that recommends products the way a helpful associate would:

• It answers from your own data: website, catalog, policies and files.
• It can recommend products, look up orders and track deliveries.
• It works on your store chat, Messenger, Instagram, WhatsApp and email, around the clock.
• When a request needs a person, it hands off with a conversation summary.
• You decide how independent it is, from suggesting replies for your team to approve, to answering routine questions on its own.

FAQ

How many products should a chat assistant recommend at once? Usually one to three. More than that tends to slow the decision. Offer a comparison if the shopper wants more detail.

Do product recommendations in chat feel pushy? Not when they follow a question and come with a clear reason. Recommendations feel pushy when they ignore what the shopper said or always point to the most expensive item.

What data does an AI assistant need for good recommendations? Accurate product details (sizes, materials, compatibility), current stock, and your policies. Short comparison notes for similar products help a lot.

Can recommendations work for returning customers? Yes. Knowing past purchases lets you suggest refills, complementary items or upgrades, instead of starting from scratch.

Conclusion

Product recommendations in chat work when they feel like good service: a quick question, a short list of well-matched options, and honest reasons for each. Ground suggestions in live product data, cross-sell only when it truly helps, and keep a human ready for the complex cases.

Want every "Which one should I get?" answered in seconds, day or night? Try TaggoAI for Shopify and give your shoppers a knowledgeable assistant on every channel.