Semantic search vs traditional ecommerce search

Traditional search looks for matches between what a person types and the words attached to products. Semantic search attempts to interpret the meaning of the request. Neither method covers every request by itself. The right choice depends on the catalog, the queries, and the level of control the store needs.

How traditional search works

Keyword search works well when shoppers know the product, model, or category name. It is predictable, fast, and easy to inspect. Synonyms, spelling corrections, and merchandising rules can extend its coverage.

Its limitation appears when the shopper's language does not match the catalog. Someone may describe an occasion, recipient, or need without knowing the term used by the store.

What semantic search adds

Semantic search represents the meaning of queries and products so it can find a relationship without identical words. It can help with requests that combine:

• a use case;
• a preference;
• a constraint;
• a recipient;
• a desired characteristic.

Understanding meaning does not make every result correct. When product attributes are incomplete, a system may find linguistic similarity without having enough evidence for a recommendation.

Precision, recall, and explainability

Traditional search tends to be precise for explicit queries but may miss products described with different language. Semantic search broadens recall but needs thresholds and rules to avoid drifting away from the request.

A balanced experience can combine both methods. Exact matching can handle brands, codes, and names. Meaning can support open-ended requests. Filters and sorting then let shoppers inspect the selection.

When conversation is useful

Semantic search is not automatically a chatbot. It can return a list without opening a dialogue. Conversation becomes useful when the request contains alternatives or lacks a decisive criterion.

A clarifying question should serve a specific purpose. When the catalog already has a coherent selection, showing it may be the better response. When several incompatible interpretations exist, asking for a preference can prevent random results.

Preparing the catalog and measurement

Before changing technology, collect internal queries and classify them. Find searches with no results, repeated rewrites, and searches followed by an immediate return to the list. Missing names may be solved with synonyms and attributes. Needs expressed in natural language may benefit more from a semantic layer.

Evaluate quality by query family rather than only in aggregate. Check whether selected products respect every explicit constraint and whether shoppers can correct the direction.

Where Valen fits

Traditional search returns results from a query, but by itself it does not clarify an ambiguous need or coordinate what happens after the result. This calls for a layer that can work with catalog data, interpret free-form language, and leave shoppers in control of browsing.

Valen imports the product catalog from a Shopify store and enriches its attributes, as demonstrated on a demo store. Valen lets shoppers search the catalog and ask questions about products within the conversation, as demonstrated on a demo store.

Intent understanding can also connect to a specific action. Valen recognizes from free-form text when a shopper intends to try a product on, as demonstrated on a demo store.

Results do not remove browsing. Valen keeps the catalog browsable while offering recommendations and provides a results page the store can enable, as demonstrated on a demo store.

This is more than an isolated chat because the conversational request connects to catalog data, a results page, and a journey the shopper can keep exploring. These capabilities are demonstrated on a demo store and do not imply a commercial outcome.

Book a demo to evaluate conversational search against your catalog and real customer queries.