AI-powered eCommerce site search
People who use site search usually already intend to buy.
If they search for "sneakers", "running shoes", "iphone cover", "dog muzzle" or misspell a brand, the search engine has to understand the intent, not stop at the exact word.
If they search for "sneakers", "running shoes", "iphone cover", "dog muzzle" or misspell a brand, the search engine has to understand the intent, not stop at the exact word.
What we improve
- Synonyms and equivalent terms.
- Typos and spelling variants.
- Suggestions as the user types.
- Product ranking by availability, margin or relevance.
- Zero-result searches and internal redirects.
Data to use
- The queries users actually type.
- Products clicked after a search.
- Add-to-carts and purchases.
- Synonyms from the catalogue and from reviews.
- Tags, categories, brands and attributes.
Where AI comes in
- Proposing synonyms and tags.
- Normalising product attributes.
- Semantic understanding of queries.
- Flagging the zero-result searches that matter.
- Supporting the chatbot connected to the catalogue.
Site search, SEO and customer care overlap
Internal searches tell you what customers can't find, what they call your products and what they're unsure about. They are useful for improving menus, categories, facets, product pages, FAQs, chatbots and campaigns.
See also the AI chatbot for eCommerce and AI product cataloguing.
See also the AI chatbot for eCommerce and AI product cataloguing.
FAQ
Do I need site search even with a small catalogue?
It depends. It becomes essential when the catalogue has variants, brands, synonyms, technical terms, or users who search in very different ways.
Does AI replace manual rules?
No. Commercial rules still matter. AI helps discover patterns, synonyms and relationships that are hard to maintain by hand.
Can zero-result searches be measured?
Yes. They are one of the most useful signals you have: they show missing products, unhandled terms and navigation problems.