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

Doki Nguyen

June 17, 2024

January 21, 2025

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3 min read

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Search is a revenue lever, not just a navigation tool. An analysis found that searchers, while only 24% of shoppers, generated 44% of total site revenue and converted at 2.5 times the rate of browsers.

At the same time, shoppers are getting used to conversational shopping helpers. Adobe reports that traffic from generative AI sources to U.S. retail sites doubled every two months since September 2024.

That expectation carries over to your onsite search: people search in full phrases, not just single keywords.

To effectively meet customer needs, it is essential to utilize a smarter search engine with semantic algorithms. This is crucial for understanding and processing the complex, natural language that online shoppers employ when searching for desired products.

What you’ll learn

What semantic search is, in plain language, and why it matters for Shopify stores in 2025.
How semantic search differs from keyword (lexical) search and the real-world impact on conversions.
The 8 most common eCommerce search query types and where traditional search often fails.
How Shopify’s native semantic search works, its requirements, and where a third-party app can go further.
Practical next steps to improve product discovery, even if you are not ready for a full search overhaul.

What is semantic search in eCommerce?

Semantic search is a search approach that focuses on meaning. Instead of matching exact words a shopper types, it understands intent and context, then returns products that best satisfy that intent.

For example, a shopper searches “comfortable running shoes.” Keyword searches may only return products that contain those exact words. Semantic search understands the intent (running shoes that feel comfortable) and can surface products that match, even if the product page never uses the word “comfortable.”

Is semantic search the same as AI-powered search?

Semantic search is usually a core capability inside AI-powered search. It uses techniques such as natural language processing (NLP) to interpret phrases, synonyms, and word relationships.

In eCommerce, semantic search becomes especially valuable when shoppers use:

Natural language queries (“something warm for winter”, “gift for a new mom”)
Synonyms and brand nicknames (“sofa” vs “couch”, “AirPods” vs “wireless earbuds”)
Attribute-based searches (“black waterproof hiking jacket”, “2m USB-C cable”)
Compatibility searches (“case for iPhone 15”, “filter for Dyson V11”)

Semantic search vs keyword search

Here’s the practical difference for merchants and shoppers:

Capability	Keyword search	Semantic search	Merchant impact
Understands intent	Matches exact words	Interprets meaning and context	Fewer “no results” pages and fewer dead-end searches
Handles synonyms	Needs manual synonyms or exact wording	Maps related terms automatically (and can still use manual synonyms)	Shoppers find products even when they use different wording
Supports long queries	Often struggles with full sentences	Works well with natural language and multi-attribute queries	Better discovery for high-consideration purchases
Ranks by likelihood to buy	Often ranks by text match and basic rules	Can incorporate behavior and context signals	Higher click-through and conversion from search sessions
Learns from interactions	Static unless manually tuned	Improves as it learns which results perform better	Less manual firefighting, more scalable improvements

Why semantic search matters for Shopify merchants in 2026

Most stores are still not meeting real shopper search behavior. Baymard found that 41% of sites fail to fully support 8 key search query types. When shoppers cannot search the way they naturally think, they change the query a few times, lose confidence, and bounce.

Semantic search helps because it is built to handle the way people actually search, including these common query types:

Query type	Example query	Why keyword search breaks	How semantic search helps
Exact (33% of sites have issues)	“OMEN 27i monitor” or a SKU	Misspellings, punctuation, and model numbers cause misses	Understands variants, typos, and product identifiers
Product type (29% of sites have issues)	“women’s jeans”	Pulls irrelevant results that contain a word match	Understands category intent and ranks true matches first
Feature (34% of sites have issues)	“black waterproof jacket”	Attribute terms may live in tags/metafields not indexed well	Connects attributes to the right products even with varied wording
Use case (36% of sites have issues)	“wedding guest dress”	No exact keyword match, so results skew random	Interprets intent and surfaces products that fit the scenario
Compatibility (31% of sites have issues)	“case for iPhone 15”	Requires perfect mapping between devices and accessories	Uses relationships and context to match compatible items
Abbreviation and symbol (50% of sites have issues)	“USB-C”, “12" pan”, “4K”	Symbols and punctuation can break parsing	Normalizes symbols and understands shorthand
Symptom (38% of sites have issues)	“dry scalp shampoo”	Symptoms are not always listed as product terms	Matches symptom intent to relevant product benefits
Non-product (50% of sites have issues)	“return policy”, “size chart”	Returns random products instead of helpful pages	Routes to the best page or help content, not just products

Tip: If your store has many “search exits” or “no results” queries, start by auditing these 8 query types. Semantic search closes many of these gaps by design.

How semantic search works (no jargon)

Most semantic search systems follow the same basic flow:

Interpret the query: detect intent, entities, attributes, and relationships.
Translate meaning into product understanding: connect the query to product titles, descriptions, tags, collections, and structured attributes.
Rank results: prioritize products that best match intent, plus business rules like inventory, margin, or promotions.
Improve over time: learn from clicks, add-to-carts, and purchases to refine ranking and suggestions.

Semantic search in Shopify

Shopify introduced semantic search as part of its Shopify Editions Winter ’24 updates and expanded availability in 2025. In practice, Shopify semantic search improves storefront search by using the meaning behind a query, not just the exact words.

Shopify also notes that semantic search uses product text and image data to determine relevance. This is why clean product data still matters: semantic search can only work with what your catalog gives it.

Shopify semantic search: quick facts

What to know	Details (as documented by Shopify)
Where it applies	Storefront search results (not predictive search)
Plans	Available to certain plans, including Grow, Advanced, and Plus (check your admin for eligibility)
Catalog size	Designed for stores with fewer than 200,000 products
Locale limits	Not supported for the Japanese locale
Dependency	Works with Shopify’s native search results (may not apply if another app fully replaces search results)

If you meet Shopify’s requirements, enabling semantic search is a strong baseline upgrade. It can reduce irrelevant results and help shoppers find products with fewer query rewrites.

When Shopify's semantic search is enough, and when you may need more

Shopify semantic search is a great starting point, especially if you:

Have a mid-sized catalog and straightforward product attributes
Primarily sell in supported locales
Do not need deep merchandising controls or search-specific analytics

You may want a dedicated search and discovery app if you need:

More control over search ranking and merchandising (for example, promoting high-margin products or seasonal collections)
Stronger support for complex catalogs and structured attributes
Advanced tools for synonyms, typo handling, scoped search, and query analytics
A tighter loop between search behavior and conversion insights

Semantic search in Boost Commerce

Boost Commerce includes a semantic search designed for eCommerce. It helps your store interpret natural language queries, handle synonyms, tolerate misspellings, and use context to return more relevant results.

It also comes with practical controls merchants actually use day-to-day:

AI synonyms and manual synonyms
Searchable fields and scoped search
Partial search and typo tolerance
Numeric search and special character handling
Search merchandising to promote or pin products

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Common merchant goals and the semantic search lever that helps

Merchant goal	What semantic search changes	What you can measure
Reduce “no results” searches	Understands intent and synonyms, so queries match more products	Lower “no results” rate; fewer search exits
Increase search-driven revenue	Ranks products that shoppers are more likely to buy	Higher search click-through rate and search conversion rate
Help shoppers narrow faster	Supports multi-attribute and use-case queries	Fewer refinements per session; higher add-to-cart from search
Improve long-term merchandising	Learns from what shoppers click and buy, then improves ranking	Sustained uplift in search KPIs over time

Real-world impact: what improved semantic search can look like

In internal benchmarking shared by Boost, semantic search rollouts have been associated with stronger engagement and conversion signals, including an uplift in search results click-through rate and conversion rate when comparing pre and post rollout periods.

For example, Maui Divers Jewelry reported that over 700 orders were directly linked to improved search and filter capabilities within 90 days of implementing Boost Commerce.

“The introduction of an AI-powered search system transformed how customers interacted with the site (Maui Divers Jewelry online store). This advanced tool was adept at understanding and predicting customer intent, delivering fast and relevant search results that aligned perfectly with the users' expectations.”

How a better search leads to more sales

Read Maui Divers Jevelry case study

What now? Next steps for your eCommerce business

If you want semantic search to work well, focus on three things: your catalog, your controls, and your feedback loop.

1) Make your catalog easier to understand

Use consistent naming for product types, materials, and key attributes.
Fill in structured fields (options, metafields) for the attributes shoppers filter by.
Write product descriptions that include the problems the product solves, not just specs.

2) Give your search engine guardrails

Add synonyms for the terms your customers actually use (brand nicknames, local terms, abbreviations).
Set merchandising rules for campaigns and inventory priorities.
Decide how you want to handle non-product searches (policies, size charts, guides).

3) Track what shoppers do after they search

At a minimum, monitor these KPIs monthly:

Search usage rate (what percent of sessions use search)
No-results rate and top no-results queries
Search click-through rate and add-to-cart from search
Search conversion rate and search-driven revenue contribution

Better product discovery will not fix every checkout issue, but it can reduce early frustration and improve the quality of traffic that reaches the checkout page.

Having covered the fundamentals of semantic search in eCommerce, particularly for Shopify stores, let’s delve further into AI search by comparing it with conventional search methods and see how the latest search technology can help you grow at a minimum cost.

How AI search outperforms

See the difference from conventional search

Frequently Bought Together

What is the main benefit of semantic search for eCommerce?

It helps shoppers find the right products faster by understanding what they mean, not just what they type. That typically reduces dead-end searches and increases engagement from high-intent search sessions.

Does Shopify have built-in semantic search?

Shopify offers semantic search for eligible stores and plans, improving storefront search relevance by leveraging meaning and context. Availability and limitations depend on plan, product count, and locale.

Is semantic search only for large catalogs?

No. Even smaller stores benefit because shoppers still use synonyms, misspellings, and use-case phrases. Larger catalogs usually see bigger gains because there is more room for irrelevant results.

How do I know if my search needs an upgrade?

Look for high no-results searches, frequent query rewrites, high search exits, or low search click-through rate. If searchers are bouncing, you are losing your highest-intent traffic.

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Doki Nguyen

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Content Marketing Specialist

January 21, 2025

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3 min read

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

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