Is Semantic Search Enough for Ecommerce?

A misspelled word. A vague product name. An unclear query.

That’s the reality of how people search. If that’s all a shopper gives you and your search can’t deliver, they won’t stick around.

Search has come a long way from its early days as a simple utility. Once a basic tool for retrieving documents, it has evolved into a business-critical experience layer that determines whether your customers find what they’re looking for, or bounce to a competitor.

Ecommerce search presents unique difficulties when it comes to decoding shopper intent, but is semantic search alone up to the task?

In ecommerce, the stakes couldn’t be higher. According to Coveo’s 2025 Commerce Relevance Report, 72% of shoppers will abandon an ecommerce site entirely when they can’t find what they need quickly.

The question isn’t whether your site has search. It’s whether your search is smart enough to understand shopper intent, in real time, and guide them to the right products.

To understand where modern ecommerce search is going, it helps to look at where it started.

 A Quick History of Search

• 1990s: Early internet search engines like Archie and Yahoo relied on keyword matching—ranking results based on how often and where exact words appeared in text.
  
• Late 1990s–2000s: Google disrupted the space by ranking pages not just by keywords, but by link authority—introducing the PageRank algorithm.
  
• 2010s: Google’s Knowledge Graph (2012) and Hummingbird update (2013) ushered in semantic search, enabling the engine to understand the meaning behind queries and return contextually relevant results.
  
• 2020s: The rise of AI-powered search and vector-based models opened the door to interpreting user intent, personalizing results, and even answering natural-language questions via GenAI tools.

Today, the gold standard is hybrid search — an intelligent orchestration of keyword, semantic, behavioral, and business-aware signals. But to understand hybrid, we first need to define its components.

Lexical Search (a.k.a. Keyword Search): Fast but Fragile

Lexical search, also known as keyword matching, relies on parsing individual words from a shopper’s query and matching them directly to product information using Boolean logic. It’s the foundation of most traditional ecommerce search engines, like those built on Apache Lucene (which powers Solr and Elasticsearch). 

This approach is incredibly fast and precise, making it ideal for exact-match queries like SKUs, brand names, or product models. If a shopper types “Samsung Galaxy S23 case,” lexical search will likely return highly relevant results.

Despite its speed, lexical search is brittle and often falls short. Here’s why: 

• Vocabulary Gaps: Shoppers may use terms your catalog doesn’t. A search for “rockmelon” might return no results if your products are labeled “cantaloupe.” Without an exhaustive, manually maintained synonym list, valuable queries lead to dead ends.
  
• Related Product Matching: If a customer searches for a brand you don’t carry, for example “Mizuno sneakers”, keyword search typically returns nothing. It lacks the ability to infer intent and surface relevant alternatives, like Nike or Asics running shoes.
  
• Ambiguous Queries: Keyword search treats all word orders equally. It can’t distinguish between “dress shirt” and “shirt dress,” even though they mean entirely different things. It also fails to interpret vague searches like “denim” or “jacket.”
  
• Poor Result Precision: When exact matches don’t exist, keyword search often returns loosely related items. A search for “black leather wallet” might pull in “black leather belt”— technically similar, but irrelevant.

This rigidity leads to frequent zero-results pages and missed opportunities, especially for broad or long-tail queries that make up a large portion of ecommerce search traffic. Without layers of AI or semantic understanding, lexical search alone can’t deliver the intuitive, personalized results today’s shoppers expect.

Semantic Search: More Meaning, More Mistakes?

Semantic search represents a major evolution from keyword-based approaches. Broadly speaking, semantic search aims to match the content of the query to documents that correspond to the meaning and user intent – not just its words like the full-text search approach used to do.

Rather than simply matching words, it’s designed to understand meaning. This allows search engines to interpret user intent more accurately and return conceptually relevant results — even when exact terms don’t appear in product data.

One common implementation is semantic vector search, which uses machine learning to embed both queries and products into high-dimensional “vector space.” Similar ideas — like “comfy shoes” and “supportive sneakers”—are positioned close together, so even if a shopper types “work shoes for standing all day,” the system can surface relevant items that don’t include those exact words. This helps bridge vocabulary gaps and improve discovery for long-tail or non-standard queries.

Semantic search is powerful. It solves many of the limitations of keyword search by enabling smarter, intent-driven retrieval. But it’s not perfect. Without lexical anchoring or merchandising logic, semantic systems can produce overly broad or unexpected results, and often give teams less control over what gets shown. 

That’s why leading ecommerce brands are moving to hybrid search — combining semantic understanding with the precision of keyword matching, behavioral data, and business-aware ranking to ensure that every result is not just relevant, but timely, desirable, and aligned with the bottom line.

Hybrid Search: The Best of Both Worlds

Hybrid search didn’t appear overnight. It’s the result of decades of progress in information retrieval. By combining the exactness of lexical search with the conceptual flexibility of semantic search, hybrid search represents the modern standard for ecommerce discovery.

It brings together the best of both approaches: the ability to return highly relevant, keyword-anchored results when queries are precise (like “Dyson V15 vacuum”), and the intelligence to interpret broader, long-tail, or conversational queries (like “quiet vacuum for small apartment”) through vector-based matching. 

When paired with AI, hybrid search systems can also incorporate behavioral patterns, user context, and business signals to ensure results are not only relevant, but desirable and profitable.

In practice, this means shoppers find what they’re looking for faster, even if they don’t know exactly how to describe it, and merchandisers retain the ability to influence rankings, promote key products, and meet business objectives.

Keyword search was the starting point. Semantic search expanded what was possible. But only hybrid search brings them together — unifying speed, flexibility, and intelligence into one cohesive experience.

The Future of Search

Hybrid search is a powerful foundation, but delivering truly relevant ecommerce experiences requires more than smart query interpretation. Relevance isn’t just about returning results; it’s about surfacing the right results at the right time, in a way that balances shopper intent with business outcomes.

To get there, leading retailers layer in additional signals and models that go beyond lexical and semantic matching:

• Popularity & Wisdom of the Crowd
  What products are other shoppers engaging with? Click-throughs, conversions, and add-to-cart behavior help surface items that aren’t just relevant but proven to be attractive. These collective signals help guide discovery even when queries are vague.
  
• 1:1 Personalization
  Relevance becomes far more powerful when it’s individualized. Whether a shopper prefers budget-friendly basics or high-end brands, personalization based on in-session behavior, purchase history, and affinities, ensures that results are tailored in real time.
  
• Business-Aware Ranking
  Relevance shouldn’t work against profitability. Merchandisers need the ability to boost high-margin products, prioritize seasonal inventory, and meet campaign goals without compromising the customer experience

When combined, these layers turn hybrid search into an intelligent engine that supports both shopper satisfaction and business performance.

Don’t Just Search — Solve for Relevance 

Ecommerce search is no longer about a single algorithm or even a single approach. It’s about the orchestration of multiple AI models and signals, working together to interpret intent, personalize results, and prioritize the products most likely to convert.

The best search experiences today are dynamic, contextual, and self-improving. They understand that relevance isn’t static, it evolves with every click, every query, every shopper.

That’s why modern ecommerce search isn’t just about finding products. It’s about enabling discovery, building loyalty, and aligning digital experiences with what matters most: your customers and your business.

Is semantic search enough for ecommerce? Not anymore. If you want your customers to find, buy, and return, hybrid search is how you get there.