Customers rarely search the way product data is written.
They use fragments, needs, use cases, typos, synonyms, local language habits, and phrases that make sense to them, but not always to your catalog.
A shopper might search “jacket for rainy bike rides” when your product data says “water-resistant commuter shell.” A B2B buyer might search by size, function, nickname, or half a product number.
According to IMPACT’s Omnichannel Index 2026, only 11% of brands offer semantic search.
AI is also the lowest-scoring discipline at 19%, despite being one of the most discussed topics in commerce.
Most brands are interested in AI. Far fewer have deployed it where customers actually feel it.
Search is one of the clearest intent signals in ecommerce. According to Salesforce, shoppers who use ecommerce site search are 6.4 times more likely to convert because they have high purchase intent.
So when shoppers use the search bar, they are telling you what they want in their own words.
The problem is that many search experiences still expect perfect product language. Customers do not search that way. They describe problems, use synonyms, search across languages, mistype, and combine attributes the catalog may not expect.
Keyword search works when the customer and catalog use the same language.
Synonyms, redirects, manual rules, and merchandising logic still matter, but they cannot solve the full problem alone. They help with known terms, but struggle with broader intent, natural phrasing, and relationships between concepts that do not share the same words.
Semantic search matches on meaning instead of exact wording. Shoppers get relevant results without needing to know how the catalog is named or structured.
For the business, that can mean fewer failed searches, more relevant clicks, and more high-intent sessions turning into revenue.
Semantic search helps the engine understand what the shopper means. Personalization helps it understand what this shopper is most likely to need.
Two shoppers can search for the same thing with different intent. One searching “washer” may need a 20mm steel washer; another needs stainless steel. One searching “running shoes” may usually buy trail gear; another wants entry-level road shoes.
Relewise combines semantic search with real-time shopper behavior, product context, and language-aware logic. Results are shaped by the query plus signals like browsing behavior, cart contents, filters, product views, purchases, and live demand.
AI does not create strong discovery out of thin air. It needs clean product data, structured content, behavioral signals, and a system that can activate them in the moment.
That is why semantic search should not sit alone. It works best when search, recommendations, merchandising, and content discovery inform each other.
A shopper searches “waterproof jacket for biking.”
A keyword-only engine looks for those exact words. If your titles say “rain shell,” “commuter jacket,” or “cycling outerwear,” results may be incomplete or poorly ranked.
With semantic search, the engine understands the relationship between waterproof, rain protection, biking, commuting, and relevant jacket types. With personalization, it can also weigh brand preferences, size, cycling interest, and cart context.
The market is still early. When only 11% of brands offer semantic search as mentioned earlier, the opportunity is sitting in one of the most used parts of the ecommerce experience.
Search is where intent becomes visible. Semantic search makes that intent easier to understand. Personalization makes it more useful.
Together, they help customers find the right products faster while giving ecommerce teams a stronger way to guide discovery, conversion, and long-term relevance.