What is semantic search? How AI captures more sales through your search bar

Ellie Ho

July 22, 2026

•

6 minutes

Get started

Most ecommerce stores lose sales through their search bar without ever knowing it.

The products are in stock. The prices are competitive. Shoppers are actively searching for them - but if a they describe a product differently from the way it’s labelled in your catalogue, a traditional search may never connect the two.

Semantic search solves the problem.

It’s one of the technologies behind AI-powered product discovery, turning more searches into product views, clicks, and sales.

Summary

Traditional keyword search misses sales because it only matches the words shoppers type - not what they actually mean.
Semantic search understands shopper intent, interpreting different language, product attributes, compatibility and outcome-based searches to return more relevant results.
This reduces zero-result searches and helps shoppers find the right products faster - improving engagement and conversion.
Boost Commerce combines semantic search with powerful merchandising controls, giving you complete visibility over how search performs and the flexibility to optimize results for your store.

Meet shopper intent
every time.

Book your Boost demo

Why do ecommerce search bars fail?

Most ecommerce search bars rely on keyword matching. They compare the words a shopper types with the words stored in your product catalogue. If those words don’t align, relevant products can be missed.

This isn’t a rare problem. Research from Baymard Institute found that 56% of stores fail to support the most common query types.

Those failure rates carry a real cost. Shoppers who use your search bar aren't just looking around -they're actively trying to find something to buy.

Search behaviour analysis shows that shoppers who use on-site search convert at 2.5× the rate of non-search users. They represent just one quarter of visitors, yet generate almost half of ecommerce revenue.

KEY TAKEAWAY:

Site search isn’t just another navigation tool. It’s one of the highest-value moments in the customer journey. When high-intent shoppers find the right products faster, better search translates directly into better commercial outcomes.

What is semantic search?

Semantic search is an AI-powered approach that matches a shopper’s intent rather than the exact words they type. Instead of looking for a literal keyword match, it identifies products that best satisfy the meaning behind a search.

It does this by combining natural language processing (NLP), query understanding and AI models that analyze context - identifying product types, attributes and related terms before matching them against your catalogue.

Techniques such as synonym matching in search help connect different ways shoppers describe the same product, while query understanding keeps the overall meaning of the search intact.

For example, someone searching for a waterproof coat should still find products labelled rain jacket. Likewise, someone searching for a coffee table shouldn't have to filter through dining tables simply because they share the word "table".

That’s the key difference.

Keyword search asks:

“Does this product contain these words?”

Semantic search asks:

“Is this the product the shopper is trying to find?”

KEY DEFINITION:

Semantic search matches shopper intent to product meaning, rather than requiring an exact word match. It returns relevant products even when a shopper’s language doesn’t match what’s labelled in your catalogue.

What can semantic search understand?

Semantic search can interpret a wide range of search behaviours that traditional keyword search often struggles with. Here are some of the most common examples in ecommerce.

When shoppers use different language

There isn't one "correct" way to describe a product. Different shoppers naturally use different words for the same item. For example:

Trainers → Running shoes
Sofa → Couch
Cabin suitcase → Carry-on luggage
TV stand → Media unit
Duvet cover → Quilt cover

Semantic search recognises these relationships, helping shoppers find relevant products without needing to use the exact language in your catalogue.

Why it matters: Different wording shouldn’t create dead ends. By recognising equivalent terms, semantic search reduces zero-result searches and helps recover revenue that might otherwise be lost.

When shoppers combine product attributes

Many shoppers search using combinations of attributes rather than a single product name. For example:

Waterproof jacket
Leather office chair
King size oak bed frame
Stainless steel water bottle
White linen duvet cover

Semantic search understands how these attributes combine to identify the products that best match the shopper's requirements.

Why it matters: Shoppers with detailed searches are often closer to making a purchase. Returning relevant products quickly shortens the path from search to checkout.

When products need to work together

Some searches rely on understanding the relationship between products, not just the words they contain. For example, a shopper might search for:

iPhone 16 case
HP 305 ink cartridge
Dyson V11 replacement filter
Nespresso Vertuo pods
Canon EOS R50 battery

In each case, semantic search understands that the shopper isn't simply looking for those keywords - they're looking for products that are compatible with a specific device or model.

Why it matters: Making compatible products easier to find builds confidence at the point of purchase and reduces the chance of customers abandoning their search.

When shoppers search by outcome

Sometimes shoppers don't know exactly what product they need - they simply know what they're trying to achieve:

Shoes for standing all day
Gift for a coffee lover
Luggage for a two-week holiday
Skincare for dry skin
Desk for a small home office

These are conversational search queries, and they're becoming increasingly common. Semantic search helps interpret the intent behind them to surface relevant products.

Why it matters: Outcome-based searches help shoppers discover products that solve their problem, creating more opportunities for product discovery and conversion.

Where semantic search has limits

Semantic search improves how shopper queries are interpreted, but it doesn't replace good product information. If important product details aren't available in your catalogue, AI has less information to match against. That's why semantic search performs best alongside well-structured product data.

How does semantic search improve conversion?

Semantic search doesn’t increase conversions simply because it’s AI.

It increases the chances that shoppers actually find the products they intended to buy.

When more relevant products appear, shoppers engage with search results more often instead of abandoning their search or returning to category navigation.

Over time, that typically leads to:

Higher search click-through rates.
Higher search conversion rates.
Fewer no-result searches.

Combine semantic search with AI-powered product recommendations, and you create a connected shopping journey that supports both product discovery and basket growth.

Case study highlight: Valerie Simon

Valerie Simon, a family-run fashion retailer, needed its search bar to understand shoppers regardless of the exact words they used - a French "foulard" and an "écharpe" are both scarves, but a keyword-only search bar would only ever recognise one of them.

Using Boost's semantic search and AI synonym detection, mismatches like that are resolved automatically, without a merchant building a synonym list by hand for every regional term and alternative name.

Since rolling it out, Valerie Simon has seen its search click-through rate reach 11.35% and its search conversion rate reach 2.3% - well above what a keyword-only search bar was returning.

Why it matters: the improvement didn't come from a catalogue rewrite or new photography. It came from the search bar finally reading different words as the same request. The products were already there - shoppers just needed to be matched to them.

Read the full Valerie Simon case study →

How Boost Commerce powers semantic search for Shopify stores

Semantic search isn’t unique to Boost. It’s an industry-wide approach used by modern ecommerce search platforms.

What matters is how it’s implemented.

Boost combines traditional keyword matching with semantic understanding, allowing your search bar to interpret shopper intent while still benefiting from precise keyword matches where appropriate.

Rather than forcing merchants to choose one approach or the other, Boost lets you control how much semantic matching influences your search results.

Using the semantic search slider, you can:

Increase semantic influence when you want broader intent matching.
Prioritise keyword matching when precision matters most.
Compare search results with semantic search enabled and disabled before making changes.

That level of control helps merchants improve relevance without turning search into a black box.

Once semantic search is live, monitoring search health metrics helps you understand how shopper behaviour changes over time, from click-through rates and conversions to zero-result searches.

Drive conversion with
smarter search.

Book your Boost demo

Frequently asked questions

Is semantic search the same as AI search?

Semantic search is one part of AI-powered search - specifically, matching a query to product meaning rather than exact words. AI search can also include personalization, recommendations, and ranking.

How does semantic search handle product variants?

If a customer searches for a specific size, colour, style, or material, semantic search can surface the most relevant product variant as long as that information is clearly described in your catalogue.

Does semantic search help on a small catalogue?

Yes, but the improvement is usually smaller. The biggest gains show up on large or varied catalogues, where word mismatches are frequent and harder to catch manually.

Does semantic search understand regional spelling or slang?

Yes. AI synonym detection is built to learn how shoppers phrase the same product differently, including regional terms and informal language, without you maintaining that mapping by hand.

‍

Trusted by fast-growing brands

Start your 21-day free trial

Get started

Ellie Ho

•

Content Marketing Specialist

July 22, 2026

•

6 minutes

There's More

What is semantic search? How AI captures more sales through your search bar

Shopping shouldn’t feel like a chore

Start your 21-day free trial

Related Posts

Semantic Search for Shopify: Everything You Need To Know (2026 Update)

Everything An eCommerce Store Owner Needs To Know About Dynamic Filtering

[Infographic] Dig Into Search Foundation Of Boost AI Search & Discovery