A useful fit when shoppers need help choosing
Zipchat is worth trying when customers know what they need a product to do but do not know which item to buy. Think of someone looking for a lightweight work bag that can carry a laptop, rather than someone searching for a specific model number. Your trial should show whether the assistant can turn those requirements into a sensible recommendation.
Zipchat describes recommendations based on product information, with follow-up questions to clarify a shopper's needs. See its product recommendation overview, checked October 3, 2026. This guide focuses on preparing the catalog and evaluating shopping conversations; our Shopify support guide covers the broader use case.
The goal is a customer who understands the choice. A long list of products is less useful than a short explanation of which option fits their needs and why. Keep ordinary collection pages available too: shoppers should be able to browse and ask questions in the same visit.
Prepare comparable product information
Start with the facts a store associate would use to explain the difference between two products. For bags, that might include dimensions, weight, materials, care instructions, and what fits inside. For clothing, it could be measurements, fabric, fit notes, and intended use.
Use consistent fields across similar products. If one bag lists its weight and another does not, a question about the lightest option exposes a gap in the catalog. Fix the source information instead of asking the assistant to guess. Product variants, bundles, and discontinued items deserve attention too: the recommendation needs to lead to the correct item.
Zipchat's AI Training documentation explains the area used to manage information for the assistant. After reviewing the catalog there, check any supporting buying guides for outdated descriptions. Conflicting advice in two documents can be harder to spot than a missing specification.
Test shopping questions, not just product names
Use the kind of language customers use before they have chosen a product. These are suggested test cases, not promised responses:
| Question | What to look for |
|---|---|
| I need a bag for a short commute | A useful question or a relevant starting option |
| I want something light, but it needs to fit my laptop | A comparison based on both requirements |
| This is a gift and I do not know their style | Clarification instead of an unsupported assumption |
| What is the difference between these two? | The same comparison criteria for both products |
Add requirements gradually. Start with the purpose, then introduce a budget or size constraint. This makes it easier to see where the conversation loses track of what the shopper asked for.
Include a request your catalog cannot satisfy. Decide how your store would handle it: explain the closest alternative, ask which requirement is flexible, or direct the shopper to a person. That decision belongs in your service approach rather than in an invented product claim.
Review the catalog, instructions, and destination links
Check the underlying product information before adjusting conversational instructions. Zipchat's setup guidance describes reviewing catalog sync and refining additional instructions. Use that sequence to keep troubleshooting manageable.
First, confirm that the products you want to test are available to the assistant. Next, compare the source descriptions with the advice your staff would give. Then outline when to ask a follow-up question. Finally, run your shopping scenarios and open each recommended product link.
Separate instructions about the conversation from facts about the products. “Ask what the customer will carry” is a useful conversational preference. A product's capacity belongs in its product information. Keeping those separate makes future catalog updates easier.
Change one thing at a time when an answer misses the mark. If a product description is incomplete, update it and repeat the same question before rewriting several instructions. Save a few good conversations as examples to revisit when new products arrive.
Budget for the length of the conversation
A shopping conversation may involve several AI replies, so budget around the interaction rather than counting each shopper as one reply. Review the current reply allowance, billing period, and options for additional usage on the official pricing page, checked October 3, 2026.
During a trial, watch for unnecessary repetition. Asking the same sizing question twice adds friction; asking a relevant follow-up may be exactly what makes the recommendation useful. The aim is an efficient, helpful conversation, not the shortest possible answer.
Distinguish an annual plan's monthly equivalent from a monthly payment. Confirm the terms shown for your account before choosing a subscription. The Shopify App Store listing is another official source for the app's current offer and product capabilities.
Questions to answer during your trial
Judge both the recommendation and the explanation behind it. A correct link without a clear reason may still leave the shopper unsure.
Is this useful for a small catalog?
Consider how much help customers need choosing, rather than catalog size alone. A handful of similar products can generate detailed questions. A single-product store may need guidance about sizing or use instead of comparisons.
What should I check after a product update?
Repeat a relevant shopping question after changing the source information. Check the description, recommendation, and destination page together. Also review any buying guide that might still contain the old detail.
How much explanation is enough?
Enough to connect the customer's priorities with the product's attributes. “This one is lighter; that one has more room” can be more helpful than a paragraph of promotional language. Stick to claims your store can support.
Start with your best-known products
Bring a small set of representative products and realistic customer questions to a Zipchat trial. Check the catalog, recommendation, explanation, and product link in that order. That gives you a practical basis for deciding whether conversational shopping fits your store.
