Product Recommendation Chatbot
An AI shopping assistant that learns what customers want and recommends the perfect products from your catalog.
How it works
Train the chatbot on your product catalog including descriptions, specifications, pricing, and customer reviews. When shoppers visit your store, the AI asks about their needs, preferences, and budget — then recommends the best-matching products from your inventory. It handles side-by-side comparisons, explains the differences between similar items, and suggests complementary accessories or upgrades. For stores with large catalogs where browsing is overwhelming, the recommendation bot acts as a personal shopping assistant that narrows choices quickly. It also learns from purchase patterns to improve suggestions over time, and can surface trending or seasonal products when relevant to the conversation.
Sample conversations
Which laptop is best for video editing?
What is the difference between the Pro and Standard plan?
I need a gift for someone who likes cooking
What pairs well with this jacket?
Which option is the best value for money?
Do you have anything under $50?
What this template includes
Conversational preference discovery
AI-powered product matching from catalog data
Side-by-side product comparison
Budget-aware recommendations
Cross-sell and accessory suggestions
Seasonal and trending product highlights
Key benefits
Example system prompt
Customize this prompt with your company name, tone, and specific instructions. Chatsy lets you edit the system prompt for any chatbot.
Implementation guide
Preparing Your Product Catalog
Export your full product catalog with detailed descriptions, specifications, pricing, availability, and customer ratings. Structure data so each product has clear attributes (category, price range, key features, use case) that the AI can use for matching. The richer your product descriptions, the more accurate recommendations become. Include information about who each product is best suited for — this helps the AI match products to customer-stated preferences. Add comparison notes between similar products so the AI can articulate meaningful differences.
Designing the Discovery Conversation
Configure the AI to ask 2-3 clarifying questions before making recommendations. For electronics: budget, primary use case, and must-have features. For fashion: occasion, style preference, and size. For gifts: recipient relationship, interests, and budget. Avoid asking too many questions — shoppers lose patience after 3-4 exchanges. The AI should make its first recommendation by the third message and refine from there based on feedback. Test the discovery flow with 20-30 common shopping scenarios to ensure the AI asks relevant questions.
Optimizing Recommendation Quality
Monitor which recommendations lead to purchases versus which get ignored. Review conversation logs weekly to identify patterns — are customers consistently rejecting the first suggestion? The AI might be weighting the wrong attributes. Adjust product descriptions and add explicit "best for" tags to improve matching accuracy. Track cross-sell acceptance rates and refine which accessories the AI suggests with which products. Most stores see recommendation accuracy stabilize at 70-80% match rate after 2-3 weeks of tuning.
Expected results
Best for
Frequently asked questions
How does the AI learn about my products?
Upload your product catalog with descriptions, specs, pricing, and categories. The AI indexes this content and uses semantic search to match customer preferences to the most relevant products in real time.
Can the bot handle product comparisons?
Yes. When a customer is deciding between two or more products, the AI provides side-by-side comparisons highlighting key differences in features, price, and suitability for their stated needs.
Does it work for stores with frequently changing inventory?
Yes. Update your product catalog in Chatsy whenever inventory changes. The AI always references the latest catalog data, so it will not recommend out-of-stock or discontinued items if your catalog is current.