Semantic Search

Imagine you’re searching for a “warm jacket” in a webshop. Traditional search looks for products that literally contain the words “warm” and “jacket”. But what if the perfect product is called an “insulated winter coat”? Traditional search would miss it completely, even though it’s exactly what you’re looking for. This is where semantic search comes in.

Semantic search is technology that understands what you mean, not just what you type. Instead of matching words letter-by-letter, it grasps the concept behind your search and finds products that match that concept, even when completely different words are used.

How does it actually work?

Think of semantic search like a knowledgeable shop assistant who really understands products. When you ask for “warm jacket”, they don’t just look for those exact words on labels. They understand you want something insulated, suitable for cold weather, outerwear. They’ll show you winter coats, parkas, insulated jackets, anything that fits what you’re actually looking for.

Semantic search does the same thing, but digitally. It analyzes your search query to understand the intent and context. Then it looks through your product catalog to find items that match that intent, regardless of the specific words used.

Let’s look at a real example. A customer searches for “shoes for running”. Traditional search would need products to contain those exact words. But semantic search understands this customer wants athletic footwear designed for jogging. So it shows running shoes, trainers, athletic sneakers, all products that match the intent, even if they don’t use the words “shoes for running”.

Why this matters for your webshop

When customers can’t find what they’re looking for, they leave. It’s that simple. Studies show that visitors who use search are much more likely to buy something, but only if the search actually works well. Here’s what semantic search changes:

Your customers use all kinds of different words for the same thing. Some search for “sneakers”, others for “trainers”, and some just want “comfortable walking shoes”. With traditional search, each of these queries might show completely different results, or worse, no results at all. Semantic search understands they’re all looking for similar products and shows relevant results for each query.

The same goes for descriptive searches. When someone types “gift for my girlfriend”, traditional search gets confused, it looks for products literally containing those words. Semantic search understands they want gift-appropriate items and can make suggestions accordingly.

Real-world improvements

Let’s say you run a furniture webshop. A customer searches for “cozy chair for reading”. Traditional keyword search struggles with this because most product titles don’t include the word “cozy” or mention reading. The search returns few or no results, and the customer leaves frustrated.

With semantic search, the system understands this customer wants a comfortable armchair, probably with good back support, suitable for long sitting periods. It shows wingback chairs, reading chairs, comfortable recliners, products that fit what they’re looking for, even though none contain the exact search words.

This isn’t science fiction. This is how modern search works. The technology analyzes millions of product-search combinations to learn which products satisfy which types of searches. Over time, it gets better and better at understanding what customers really want.

The technical magic (explained simply)

You don’t need to understand the technical details to use semantic search, but here’s a simple explanation of what happens behind the scenes: the technology converts both your search query and your products into something called “vectors”, essentially, mathematical representations of meaning.

Think of it like this: instead of storing words, the system stores the meaning of words as coordinates in space. Words with similar meanings get stored close to each other. So “sneakers”, “trainers”, and “athletic shoes” all end up in the same neighborhood, even though they’re completely different words.

When someone searches, the system converts their query into the same type of coordinates, then looks for products in that neighborhood. It’s like having a map where all similar concepts are geographically close to each other.

Does it work in different languages?

Yes, and this is one of the most powerful aspects of semantic search. Because it understands concepts rather than specific words, it can match searches in one language to products described in another language. A customer searching in Dutch can find products with English descriptions, as long as the concepts match.

This is incredibly valuable for international webshops or regions where multiple languages are common. You don’t need to translate everything, the search understands meaning across language barriers.

Combining semantic search with traditional search

Here’s an important point: semantic search doesn’t replace traditional keyword search. The best approach uses both. Here’s why:

Traditional keyword search is still excellent for specific searches. When someone types “Nike Air Max 90”, they want exactly that product. Semantic search might think they want similar Nike shoes, but the customer knows precisely what they want. Keyword search handles this perfectly.

But when someone searches for “comfortable running shoes for beginners”, semantic search shines. This is where understanding intent matters more than matching exact words.

Modern search systems like TextAtlas combine both approaches automatically. Exact matches get priority, but semantic understanding fills in the gaps and catches searches that would otherwise fail. You get the precision of keyword matching and the intelligence of semantic understanding, working together.

What this means for you

If you run a webshop, semantic search means fewer frustrated customers, fewer empty search result pages, and more sales. Customers find what they’re looking for faster, even when they don’t know the right words to use. Your search starts working more like a helpful shop assistant and less like a rigid keyword matcher.

The beauty is that you don’t need to do anything special to make it work. You don’t need to add tags or keywords or manually connect related products. The system learns automatically from your product catalog and customer behavior. It gets smarter over time, continuously improving at understanding what your customers want.