Semantic Search: What It Means for Online Stores

Semantic search matches the meaning and intent of a shopper’s query to relevant products, even when the product description uses different words. It helps customers describe what they need without knowing your catalogue’s terminology. Exact product codes still need exact matching, so a store can combine semantic and keyword search.

Semantic Search in Plain English

A semantic search engine on a store ranks products by how well they answer the shopper’s request. A query such as ‘warm jacket for hiking’ describes a use and a requirement for warmth. The matching product might be called an ‘insulated trail shell’.

Consider a hypothetical outdoor store with suitable products and detailed attributes. Its first three results for that query could be:

1. Insulated trail shell, with synthetic fill and a wind-resistant outer fabric.
2. Alpine down parka, with insulation intended for cold conditions.
3. Thermal trekking coat, described as suitable for walking in low temperatures.

The first product title shares no words with the query. Its attributes give the search engine the connection. This example illustrates meaning-based matching, rather than results from a particular retailer.

Meaning alone doesn’t settle suitability. A heavy parka might be too bulky for the shopper’s walk. Product details and ranking rules still decide which match belongs first.

Semantic Search vs Keyword Search

Keyword search, also called lexical search, matches the query’s words against indexed product text. Semantic search matches the concept behind those words. Elastic’s semantic search explanation describes this distinction and the synonym rules that keyword search can also use.

For a hypothetical homewares catalogue, ‘sofa’ can find ‘couch’ through either a configured synonym or meaning-based matching. That result alone doesn’t prove the store uses semantic search.

A longer query, such as ‘a narrow seat for the end of my bed’, gives semantic search more context. Keyword search might match separate words or require a rule connecting the phrase to bedroom benches. Misspellings need typo handling in either approach.

Hybrid search combines keyword and meaning-based results. It preserves a direct match for a stock keeping unit (SKU), brand or model name while helping shoppers who describe a need. A customer typing a known model code expects that item before broadly similar products.

For practical changes to the search experience, see how to improve ecommerce site search.

Fuzzy Search vs Semantic Search

Fuzzy search accepts words close in spelling to the query, often a letter or two away. Semantic search connects different words through meaning. Elastic’s fuzzy query documentation explains matching through letter edits, including substitutions and transpositions.

In a hypothetical footwear store, ‘sneekers’ can match ‘sneakers’ through fuzzy search. The shopper has misspelt the product type. A query for ‘shoes for flat feet’ asks about a need instead. Semantic search can match supportive runners whose descriptions address that need.

Typo tolerance and semantic ranking can work together. A store search tool can correct or tolerate the spelling, then rank meaning-based matches. Fuzzy matching by itself doesn’t establish that a shoe has the support the customer needs.

Semantic Search vs Vector Search

Vector search is a common technique used to deliver semantic search. Semantic search describes the meaning-based result. Vector search describes how a system finds similar stored items.

An embedding is a list of numbers a model creates from text or an image. Similar meanings or visual features can produce nearby lists, so the system can compare items without requiring the same words.

Vector search finds stored items whose embeddings are nearest to the query’s embedding. With suitable image embeddings, it can also find visually similar products. Elastic’s vector search guide explains these numerical comparisons and the use of filters alongside them.

A vector search engine performs that similarity lookup. A vector database stores embeddings with the information needed to retrieve their products or documents. Vector similarity search is the comparison that finds nearby embeddings. These labels describe parts of the search system, so seeing them in a tool listing doesn’t mean choosing between three different shopper experiences.

Similarity also differs from eligibility. A visually similar jacket can still be the wrong size or unavailable. Explicit filters must enforce requirements such as stock or colour.

Semantic Search vs RAG

Retrieval-augmented generation (RAG) combines a search step with a language model that writes an answer using the retrieved information. The search can find relevant products or passages from store policies. Microsoft’s RAG overview explains the handoff from retrieved results to a generated reply.

With semantic search, the shopper sees a ranked product list. In a RAG-based assistant, the shopper can receive sentences grounded in those products and the store’s own pages. For example, the reply could explain which listed coats have insulation, using their product descriptions.

RAG can use semantic search during retrieval, so the two can work in the same system. A generated explanation still needs accurate source information. Retrieval doesn’t guarantee that every sentence is correct.

Natural Language and Conversational Search

Natural language search accepts full questions or descriptions, such as ‘Which coats suit a windy coastal walk?’ Conversational search carries relevant context across follow-up messages. A search box can accept natural language without remembering an earlier search.

In a hypothetical conversation with a store-owned shopping agent, a customer asks for an insulated coat. They then say ‘Show me those in navy’, followed by ‘Only the lighter ones’. The agent keeps the coat request and navy colour when applying the weight preference.

The chat is where the shopper refines the request. Semantic search can help retrieve products behind it, while conversation handling keeps the earlier constraints. Planning and running searches on a shopper’s behalf is covered separately in agentic search.

What Semantic Search Needs From Your Product Data

Semantic search needs clear product titles, useful descriptions and accurate attributes to match a request to what you sell. A title such as ‘Style 47’ gives less context than ‘Insulated trail shell’. A missing insulation field leaves the search system with less evidence for a warmth query.

Three data fixes help whichever search tool your store uses:

1. Replace vague titles with a recognisable product type and its distinguishing detail. Keep the model name and SKU for exact lookups.
2. Describe supported uses in ordinary language. Explain a coat’s insulation and intended conditions instead of adding unsupported claims about warmth.
3. Complete structured attributes consistently. Put colour, size and availability in their correct fields, and keep them current for each variant.

The AI-ready product data guide explains how to organise that information. Before your next search demo, take a real customer phrase and its intended products. Ask the vendor to show why those items rank and how exact model lookups behave beside them.