September 15, 2026
7 min read
Semantic vs lexical search: the short answer
Lexical search matches the words in a query to words in the catalog. Semantic search tries to match the meaning behind the query, even when the product page uses different wording. Ecommerce teams usually need both: lexical matching protects exact product names and SKUs, while semantic matching helps with natural language, use cases, and unfamiliar product terms.
How the two approaches differ
| Approach | Best at | Common weakness |
|---|---|---|
| Lexical | Exact names, brands, SKUs, attributes | Misses related wording and implied intent |
| Semantic | Use cases, natural language, related concepts | Can return broadly related but wrong products |
| Combined | Exact control plus meaning-based discovery | Needs testing, field quality, and clear rules |
Lexical search protects precision
If a shopper searches for a model number, brand, or exact product name, word matching is often the safest path. It is easier to explain and tune. Merchandising teams can add synonyms, field weights, and ranking rules without retraining a model.
Lexical search can fail when the shopper’s language differs from the catalog. “Rain coat for commuting” may not match a title that says “waterproof shell jacket”.
Semantic search handles the way people describe needs
Semantic search represents words and products by their relationship in language. It can connect a use case with products that satisfy it, even if the exact phrase is absent from the title. That makes it useful for broad discovery and longer queries.
Meaning-based matching still needs boundaries. A shopper searching for a carry-on bag should not see every travel accessory. Product type, availability, brand, and category rules must remain visible in the result logic.
When should an ecommerce store use each one?
Use lexical matching as the foundation for navigational searches. Keep exact matches for SKUs, brands, named collections, and product types. Add semantic matching when shoppers use natural language, search by problem, or use vocabulary that is missing from product titles.
The strongest setup routes each query through several signals rather than treating “semantic” as a replacement for search basics. A combined system can match exact fields first, use synonyms and typo recovery next, then add meaning-based results where the query needs them.
How to test semantic search safely
Build a test set from real searches. Label each query as exact, attribute-led, use-case-led, ambiguous, or misspelled. Judge the first page for relevance, not just whether it contains a related product.
Track zero-result rate, search conversion rate, add-to-cart rate, and revenue per search. For high-value queries, compare the old and new result sets side by side. Keep a small list of protected queries where exact matches must remain first.
Clerk.io’s Intelligent Search combines search signals such as typo tolerance, synonym detection, category search, facets, and sales-based ranking. Read the Search ebook for more ways to connect search quality with store performance.
Customer evidence to use as a benchmark
Search quality should be judged against a store’s own data. Hairlust describes its search engine as “much more relevant” after improving discovery. Its reported result was €21,000 through Search and Recommendations in the last 30 days. That is a customer case result, not a general benchmark.
Start a free Clerk.io trial or book a demo to test the approach on your catalog.
TL;DR
Lexical search is strong for exact intent. Semantic search helps when shoppers describe a need in unfamiliar words. Combine them, protect exact matches, and compare result quality using real queries and revenue-linked metrics.
