Product Recommendations That Actually Understand What Shoppers Want
Shopify's built-in recommendations show "you might also like." Mika has a conversation. Visitors describe what they need in plain language and see matching products with photos and pricing, right in the chat.
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This is a real Shopify catalog. Ask for products by color, size, price, occasion, or anything else. See how Mika responds with product cards.
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How It Works
No rules to configure. No decision trees to build. Mika reads your Shopify catalog and handles product discovery automatically.
Visitor asks a question
"Do you have any summer dresses under $60?" or "I need a birthday gift for a 10-year-old boy" or "What running shoes do you carry in wide?" Natural language, not menu options.
Mika searches your catalog
Mika understands the intent and searches across product names, descriptions, categories, tags, colors, sizes, and price ranges. Every filter the visitor mentioned is applied simultaneously.
Products appear in the chat
Matching products show up as scrollable cards with photos, prices, and direct links to the product page. The visitor can tap through photos, ask follow-up questions, or refine their search.
How a Real Product Discovery Conversation Flows
A visitor lands on your Shopify store from a Google search. They are looking for a birthday gift for their sister, but they do not know exactly what they want. Your store has 200 products across a dozen categories. The visitor scrolls the homepage, clicks into a collection, scrolls some more. Nothing jumps out. On a typical store, this visitor leaves within 90 seconds.
With Mika, they type: "I need a birthday gift for my sister, she likes cooking, budget is around $50." Mika breaks this down: category keywords related to cooking, price ceiling of $50, gift context. She searches your entire catalog and returns 3-4 matching products as visual cards in the chat, each with a photo, the price, and a direct link to the product page.
The visitor sees the results and refines: "She already has a nice knife set, more like accessories or gadgets." Mika adjusts the search, filtering out knives and surfacing items tagged as accessories, gadgets, or kitchen tools. A new set of cards appears. The visitor taps through photos on one of them and asks: "Does this come in a gift box?" Mika checks the product description and answers.
This entire interaction takes under two minutes. The visitor went from "I have no idea what to buy" to "This is perfect, adding to cart" through a natural conversation. No filtering dropdowns. No scrolling through collection pages. No navigating away from the chat. The product discovery happened inside the conversation, the same way it would happen if they walked into a physical store and asked a salesperson for help.
Conversational Recommendations vs. Everything Else
| Approach | How it works | Limitation |
|---|---|---|
| Shopify "You May Also Like" | Algorithm suggests related products on product pages | Cannot handle open-ended requests. Only works if visitor is already on a product page. |
| Product Quiz Apps | Fixed questionnaire maps answers to product sets | Rigid. Breaks when visitor has a question the quiz does not cover. Requires manual setup. |
| Search Bar | Keyword matching against product titles | Fails on natural language. "Something warm for winter" returns nothing. |
| Rule-Based Chatbots | Decision trees with pre-written paths | Every path must be manually built. Cannot handle unexpected questions. |
| Mika | Conversational AI searches your full catalog based on natural language | Requires product data in Shopify (auto-imported). |
Why Conversational Recommendations Convert Better Than Widgets
Traditional product recommendation widgets on Shopify ("You might also like," "Customers also bought") are passive. They sit on the product page and show algorithmically selected items. The visitor has to already be on a product page for the widget to appear. If the visitor is browsing your homepage or a collection page, the widget does nothing. If the visitor does not know what product category they want, the widget is irrelevant.
Conversational recommendations are active. The visitor initiates the conversation from any page on your store. They describe what they need in their own words, and the system responds with matching products. The critical difference is that the conversation handles ambiguity. "Something warm for winter" is a query that your search bar cannot process, that your recommendation widget cannot trigger, and that your filter sidebar cannot express. But in a conversation, it works.
Conversational recommendations also handle refinement naturally. A visitor who sees 4 product cards and says "Something cheaper" or "In blue instead of red" gets an updated set of results instantly. Compare that to the standard Shopify flow: adjust the price filter, reselect the color swatch, wait for the collection page to reload, scroll through results again. Each extra step is friction, and friction kills conversion.
The visual component matters too. When Mika recommends a product, it appears as a card with the product photo, name, price, and a direct link. The visitor can tap through multiple photos right in the chat. This is not a text response that says "Check out our Classic Wool Scarf for $45." It is a visual shopping experience embedded in a conversation. The gap between "I see a product I like" and "I am on the product page ready to buy" is one tap.
What Your Shoppers Can Ask
These are real queries Mika can handle. No pre-programming needed.
When Product Recommendations Work Best (and When They Don't)
Conversational product search works best for stores where visitors need help choosing. If you sell 50 different candles and a visitor wants "something woodsy that is not too strong," a conversation handles that beautifully. If you sell one product in one variant, there is not much for a product search to do.
Stores with 20-2,000 SKUs are the sweet spot. Enough products that browsing is overwhelming, but not so many that the catalog is impossible to search effectively. Fashion, home goods, beauty, gifts, food and beverage, and specialty retail all fit perfectly.
Product data quality matters. Mika searches across product names, descriptions, categories, tags, variant colors, variant sizes, and prices. If your Shopify products have detailed descriptions and proper tagging, recommendations will be precise. If your products only have a title and a price, Mika can still match on those fields, but nuanced queries like "something for a summer wedding" will have less to work with.
Gift shopping is the highest-converting use case. "Gift for mom," "housewarming present under $40," "something for a 10-year-old who likes science." These open-ended, intent-rich queries are exactly what conversational search is built for. No search bar, filter sidebar, or recommendation widget can handle them. A conversation can.
Frequently Asked Questions
How does Mika know which products to recommend?
Mika auto-imports your entire Shopify catalog including product names, descriptions, prices, variants, tags, and photos. When a visitor describes what they are looking for, Mika searches across all of those fields to find the best matches. It understands natural language, so "something warm for winter under $100" works just as well as "red wool scarf."
How is this different from Shopify's built-in product recommendations?
Shopify's native recommendations are algorithm-based widgets that show "You might also like" or "Customers also bought" on product pages. They cannot have a conversation. A visitor cannot say "I need a gift for my mom, she likes gardening, budget is $50" and get a curated result. Mika handles that kind of open-ended, conversational product discovery that static recommendation widgets cannot.
Do I need to tag or categorize my products in a specific way?
No. Mika works with whatever product data you already have in Shopify. If your products have descriptions, Mika uses those. If you have tags, she uses those too. Better product data leads to better recommendations, but there is no required format or tagging system.
Can Mika filter by size, color, and price?
Yes. When a visitor asks for "black dresses in size medium under $80," Mika filters by color (from variant data), size (from variant data), and price range simultaneously. It shows only products that match all criteria.
What does the visitor see when Mika recommends a product?
The visitor sees a scrollable strip of product cards right inside the chat. Each card shows the product photo (tap to cycle through multiple photos), the product name, price, and a direct link to the product page on your store. No redirects, no popups.
Does Mika replace product quizzes?
For most stores, yes. Product quizzes are rigid. They ask predetermined questions in a fixed order and map answers to products. Mika is conversational. A visitor can start with a vague idea and refine naturally: "I need running shoes" becomes "something for trail running" becomes "trail shoes with good ankle support under $150." No quiz builder can handle that.
Give Every Visitor a Personal Shopper
Your Shopify catalog, searchable through natural conversation. Product cards with photos and pricing. Auto-synced daily. Set up in five minutes.