By industry
Customer stories
See how our clients are using Zipchat and the awesome benefits it's bringing to their businesses!
Standard recommendation engines match purchase patterns. Zipchat AI understands intent: it surfaces the right product for a shopper who describes what they need in natural language. Conversion from product discovery interactions is 15% to 35% higher than standard site search.
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CAPABILITIES
Every capability in this family, ready to activate in minutes.
Personalized product suggestions based on intent, history, and catalog affinity.
Learn more →Answer ingredient and formulation questions for beauty, food, and supplement brands.
Learn more →Explain materials, sourcing, and sustainability attributes to shoppers.
Learn more →Suggest alternatives when products are unavailable rather than losing the sale.
Learn more →Confirm compatibility between products before purchase to prevent returns.
Learn more →Amazon-style "customers also bought" engines surface popular products. They don't understand "find me a moisturizer that won't break me out and is fragrance-free." AI discovery does. It reads product attributes, reasons about the shopper's stated need, and surfaces the 3 to 5 most relevant items with explanations. This is why conversion rates are 30% to 50% higher than rule-based recommendations.
| Feature | Zipchat | Algolia | Doofinder |
|---|---|---|---|
| Conversational search | Yes | Partial | No |
| Intent understanding | Yes | No | No |
| Sizing AI | Yes | No | No |
| Customer service included | Yes | No | No |
AI product recommendations use machine learning to suggest the most relevant products for each shopper based on their current intent, browsing behavior, purchase history, and catalog attributes. They outperform static recommendation rules by 30% to 50% in conversion rate.
Standard engines use purchase co-occurrence ("customers also bought"). AI discovery understands intent: "find something for a marathon runner who needs a gift under $80." It reasons across attributes, not just purchase patterns.
Fashion (sizing and style), beauty (ingredients and skin type), electronics (compatibility), and supplements (health goals and restrictions) see the highest lift from AI discovery.
Twitter Bike USA achieved 90% recommendation accuracy with Zipchat AI vs. 40% with rule-based recommendations. Accuracy improves over time as the AI learns from click and conversion data.
Yes. Zipchat handles catalogs of 10,000+ SKUs with full attribute awareness. Larger catalogs often see higher lift because there are more relevant products to surface from deep in the catalog.
AI sizing collects measurements or body type inputs and recommends the correct size based on the brand's sizing data and return patterns. Brands using Zipchat AI sizing report 15% to 25% return rate reduction.
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