Semantic Search vs Keyword Search: What's Actually on Your Store?

A quick experiment. Go to your store right now and type this into the search bar:

"laptop for college"

What comes back?

If your search returns a solid list of lightweight laptops, backpack-friendly ultrabooks, and budget-friendly student options, great. Your search understands what that query means. But if it returns a mess of unrelated results, or worse, nothing at all, your store is almost certainly running keyword search. It reads the words "laptop for college" and looks for products that literally contain all three of those words. "College" isn't in any product title. So it fails.

The gap between a search that reads words and a search that understands meaning is the entire story of keyword vs. semantic search. It matters enormously to your revenue, and the rest of this post explains exactly what's happening under the hood.

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How Keyword Search Works

Keyword search has been the backbone of ecommerce since the early days of online retail. The technical name for what it uses is an inverted index, though the concept is simpler than it sounds.

Think of it like the index at the back of a textbook. If you want to find every page that mentions "velocity," you look up "velocity" and get a list of page numbers. The index doesn't know what velocity means in context. It just knows which pages contain that exact word.

Keyword search works the same way. Every product in your catalog gets broken down into a list of words, and those words are mapped to the products that contain them. When a shopper searches, the engine looks up their exact words in that map and returns whatever matches.

It's fast. It's reliable. For simple queries like "blue denim jacket size 10" it works perfectly. The problems start the moment a shopper uses language that doesn't match the words in your product descriptions:

• They type "gift for dad" but your products say "men's accessories"
• They type "something warm to sleep in" but your products say "fleece pajamas"
• They type "waterproof boots for mud" but your product says "all-weather terrain footwear"

In each case, the words don't overlap, so keyword search returns nothing. If you have a synonyms configuration layer, it might partially recover, but only for synonyms someone explicitly programmed in advance. Nobody can predict every way a customer might phrase a request.

There's another limitation. Keyword search can't rank by how well something matches the shopper's intent. It can rank by how many times a keyword appears, or by what you've manually boosted, but it has no model of what a person is actually trying to accomplish.

How Semantic Search Works

Semantic search takes a different approach. Instead of matching words, it matches meaning.

Imagine you walk into a physical store and ask an experienced sales associate, "I need something for back-to-school tech for my kid." That person doesn't run to a filing cabinet to look up every product with the word "school" in the description. They think: student, probably tight budget, needs portability, maybe durability. Then they walk you to the right section.

Semantic search works like that knowledgeable associate, but at machine speed.

The technical mechanism behind this is called embeddings (sometimes called vector search). In plain English, every piece of text (a search query, a product title, a product description) can be converted into a list of numbers that represents its meaning in a mathematical space. Similar meanings produce similar numbers. Very different meanings produce very different numbers.

When a shopper searches "laptop for college," a semantic search engine converts that query into its numerical meaning-fingerprint, then finds all the products whose meaning-fingerprints are closest. Whether those products use the word "college" doesn't matter. A product titled "13-inch ultrabook, 10-hour battery, under $700" scores as a close match because it means what a college student needs.

The models that generate these embeddings are trained on massive amounts of text, so they've learned that "college" relates to "student," that "student" relates to "budget" and "portable," and that "laptop" is more specific than "computer." All of that contextual knowledge gets packed into those numbers.

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Side-by-Side: The Same Query Through Both Engines

Here's how the two approaches handle real-world search queries:

| Shopper's Query | Keyword Search Returns | Semantic Search Returns | |---|---|---| | "laptop for college" | Nothing (no products contain "college") | Lightweight laptops, student bundles, affordable ultrabooks | | "gift for mom" | Nothing or irrelevant results | Curated gift sets, popular items in categories associated with women | | "something cozy for movie night" | Nothing (abstract phrase) | Blankets, loungewear, candles, slippers, snacks | | "non-toxic cleaning supplies" | Products only if "non-toxic" is in descriptions | Natural cleaners, plant-based products, fragrance-free options | | "shoes I can wear all day at work" | Partial results if "work shoes" exists | Cushioned flats, supportive sneakers, ergonomic footwear | | "waterproof but not too sporty" | Fails on "too sporty" (not a keyword) | Rain jackets, casual waterproof sneakers, lifestyle-oriented options |

Notice that the semantic column is essentially what a great human salesperson would surface. The keyword column is what a database does when it can only look for exact text matches.

How to Tell Which Your Store Is Running

You don't need to look at any code to figure this out. Run these three diagnostic queries on your own store's search and watch what happens.

If your store fails two or three of those queries, you're almost certainly running keyword search, possibly with a thin layer of manually configured synonyms on top. That configuration helps at the margins, but it can't keep up with the infinite variety of natural language.

One more signal: if your store has a "did you mean?" feature that frequently triggers, or a "no results found" page that shows up often, that's a strong indicator your search engine is struggling with the gap between how shoppers phrase things and how your products are described.

The Revenue Gap Between the Two

The difference between keyword and semantic search isn't just a technical curiosity. It shows up directly in conversion rates and revenue.

When shoppers can't find what they're looking for, they don't rephrase the query and try again. They leave. Research consistently shows that shoppers who use site search convert at 2–3x the rate of those who don't, but that multiplier only holds when search actually surfaces the right products. A search engine that fails on natural-language queries is silently funneling motivated, high-intent shoppers straight to a competitor.

Industry data from merchants upgrading to semantic search suggests the lift can be significant:

• Double-digit conversion rate increases from search-driven sessions are commonly reported across the industry
• Meaningful revenue gains from site search when shopper intent is properly decoded
• Some merchants report multi-fold increases in search-to-purchase conversions after making the switch

The reason the lift is this large comes down to where in the funnel the failure happens. A shopper typing "laptop for college" isn't browsing. They know what they need and they're ready to buy. When keyword search returns nothing, it's not losing a casual browser; it's losing a buyer.

There's also a compounding effect on zero-results rates. Every time a shopper hits a dead end in search, it signals to your analytics that the query "didn't match anything." What it really means is that your catalog has the product and the shopper couldn't find it. You're leaving money on the table in a way that your standard analytics won't flag explicitly.

We've seen merchants upgrading from keyword to semantic search find that their best early signal is a drop in the zero-results rate. Queries that used to return nothing start returning relevant products because the engine is now working from meaning, not just exact text.

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"AI Search" Is Mostly This

When search vendors use the phrase "AI search" or "NLP search," semantic search (specifically the vector embedding approach described above) is the core of what they're talking about. The AI in AI search is the same type of language model technology that powers tools like ChatGPT, adapted for matching a shopper's query to a product catalog.

Some implementations layer additional AI on top: re-ranking results by predicted conversion probability, generating natural-language explanations of why a result was returned, or augmenting a sparse query ("boots") with inferred context before searching. But the foundation is always semantic understanding, encoding the meaning of both query and product into a shared mathematical space and finding the closest neighbors.

The practical implication for merchants: if a vendor claims "AI search" but can't clearly explain that their engine works from meaning rather than word matching, push back. Ask them what happens when a shopper types "gift for mom." The answer tells you everything. If you're currently evaluating platforms, the comparison of Klevu, Algolia, and XTAL breaks down how different vendors approach this.

Where Search Is Heading

The keyword-vs-semantic framing is useful today, but it's already starting to blur. The strongest search implementations in 2026 don't pick one. They run both in parallel and merge the results. A keyword index catches exact SKU lookups and brand-name queries with perfect precision. A semantic layer catches everything else: the natural-language questions, the vague descriptions, the intent-heavy queries that keyword search was never designed to handle.

This hybrid approach (sometimes called reciprocal rank fusion) is where the industry is converging. For most ecommerce teams, the real question isn't "should I switch from keyword to semantic?" It's "does my current platform have a real semantic layer, or is it just keyword search with better synonyms?" The diagnostic queries above give you a fast answer.

How exactly the two ranking signals should be weighted against each other is still an open question. We haven't seen an industry consensus, and optimal blending likely varies by catalog size, product type, and query mix. That's an area where ecommerce search will keep evolving through 2026 and beyond.

If you want to understand where your store sits on the broader spectrum of search quality, what a good ecommerce search score looks like breaks down the eight dimensions that separate a search bar from a search experience. And for the practical playbook on improving search without a full replatform, ecommerce site search best practices covers the full checklist.

The stores that figure this out first capture the shoppers that everyone else is losing at the search bar.