Keyword Search

Keyword search is the foundation of how search has worked for decades. It’s straightforward: when you type “blue jeans” into a search box, the system looks through all your products and finds ones that contain the words “blue” and “jeans”. Simple, right?

And yes, it is simple, but that simplicity is both its strength and its limitation. Let’s explore what keyword search does well, where it struggles, and why it’s still essential even in the age of AI-powered semantic search.

How keyword search actually works

Imagine you have a massive filing cabinet with thousands of product cards. Each card contains a product’s title, description, and other details. When a customer searches for something, keyword search is like having someone flip through every card looking for matches to the exact words they typed.

But modern keyword search is actually more sophisticated than that. It doesn’t just look for perfect, letter-for-letter matches. It uses several tricks to be more helpful:

First, it treats variations of words as similar. If someone searches for “running”, the system understands that “run”, “runner”, and “runs” are related. This is called stemming, reducing words to their root form. So searching for “running shoes” will also find “run shoes” or “runners” in product descriptions.

Second, it understands that some words matter more than others. Words like “the”, “and”, “for” are called stop words, they’re so common they don’t help narrow down results. The search ignores these and focuses on the meaningful words in your query.

Third, modern keyword search usually ranks results by how well they match your query. A product with “blue jeans” in its title ranks higher than one with “blue” in the title and “jeans” buried in the description. This is called relevance scoring.

Where keyword search excels

Keyword search is incredibly effective in specific situations. Understanding these helps you appreciate why it’s still widely used:

When someone types “Nike Air Max 90”, they want exactly that product. They’re not interested in similar shoes or alternative brands. Keyword search handles this perfectly, it finds products with those exact words and shows them immediately. No interpretation needed, no AI guessing what they might want. Just fast, accurate results for specific queries.

The same goes for product codes and SKUs. If someone searches for “SKU-12345”, that’s not a concept to interpret, it’s a specific identifier. Keyword search finds it instantly.

Keyword search is also very predictable. The same search always returns the same results. This consistency is valuable for customers who know what they’re looking for and want reliable results.

Where keyword search struggles

Now let’s talk about the limitations, because understanding these shows why modern search needs more than just keyword matching.

The biggest challenge is vocabulary mismatch. Your customers might call things by different names than you use in your product catalog. They search for “sneakers”, but your products say “trainers”. They look for “sofa”, but you call it a “couch”. Traditional keyword search sees these as completely different things. No matches found, customer leaves frustrated.

This problem is more common than you might think. Studies show that customers and businesses use different terminology for the same products about 30% of the time. That’s a huge number of potentially lost sales.

Another limitation is that keyword search doesn’t understand context or intent. When someone searches for “gift for my mom”, keyword search looks for products literally containing those words. It doesn’t understand this person wants gift-appropriate items. It can’t interpret that “comfortable shoes for walking” means the customer cares about cushioning and support, not just any shoes.

Keyword search also struggles with descriptive searches. “Something warm for winter” or “clothes for a beach vacation” are too vague and natural for keyword matching to handle effectively. The system doesn’t know that “warm” relates to “insulated” or “fleece”, or that “beach vacation” suggests swimwear and light clothing.

Real-world example

Let me show you how this plays out in practice. Imagine you run a home goods webshop. A customer searches for “comfy chair for reading”. Let’s break down what happens with keyword search:

The system looks for products containing “comfy”, “chair”, and “reading”. But here’s the problem: your product titles probably say “comfortable armchair” or “reading chair”, but not necessarily all three of these words together. Some products might match “chair” but not mention comfort. Others might be perfect reading chairs but don’t use that exact word.

Meanwhile, you have a beautiful wingback chair that’s ideal for reading, deep seat, good back support, perfect for curling up with a book. But the product description says “elegant wingback armchair with lumbar support”. Keyword search misses it completely because there’s no word overlap with “comfy chair for reading”.

This is the frustration of keyword search. It’s literal and rigid. A human sales assistant would immediately show that wingback chair to someone asking for a comfortable reading chair. They’d understand the intent. Keyword search can’t do that.

Making keyword search better

Despite its limitations, there are ways to make keyword search more effective. Many webshops improve keyword search by adding synonym dictionaries. This means teaching the system that “sneakers” and “trainers” mean the same thing, or that “sofa” and “couch” are interchangeable.

You can also improve results by weighting different fields differently. A match in the product title might be worth more than a match deep in the description. A match in the brand name might be weighted heavily when the customer seems to be searching for a specific brand.

Some systems add fuzzy matching for typos, allowing “snaekers” to match “sneakers” even though it’s misspelled. This helps reduce frustrating zero-results pages when customers make small typing errors.

But here’s the thing: all these improvements require manual configuration, constant maintenance, and still have limitations. You’re essentially trying to teach a rigid system to be flexible, which is difficult and never quite perfect.

Why keyword search isn’t going away

With all these limitations, you might wonder why we still use keyword search. Here’s why: for certain types of queries, it’s still the best option.

When customers know exactly what they want and search specifically for it, keyword search delivers fast, accurate results. No AI interpretation needed, no possibility of the system “helpfully” showing similar products when you want an exact match.

Keyword search is also computationally very efficient. It’s fast and can handle enormous catalogs without slowdown. This speed and reliability make it valuable for high-traffic webshops.

Most importantly, keyword search provides a baseline of accuracy. When combined with semantic search, it ensures that exact matches always appear in results, even as the system also shows conceptually similar products.

The modern approach: combining both

The truth is, you don’t need to choose between keyword search and semantic search. The best modern search systems use both together, letting each handle what it does best.

This hybrid approach means when someone searches for “Nike Air Max 90”, keyword search ensures that exact product appears at the top. But when someone searches for “comfortable sneakers for walking”, semantic search takes over to interpret intent and find relevant products.

From your perspective as a webshop owner, this happens automatically. You don’t need to configure which type of search handles which query. The system figures it out, using keyword matching for specific queries and semantic understanding for natural, descriptive searches.

This combination gives you the best of both worlds: the precision and speed of keyword search, plus the intelligence and flexibility of semantic search. Your customers get relevant results whether they search like a product catalog (“SKU-12345”) or like a human (“something warm for winter”).

What this means for you

Keyword search is still the backbone of e-commerce search, but it works best when it’s not working alone. Think of it as the reliable foundation that handles specific, exact searches, while semantic search adds the intelligence to handle everything else.

Modern search solutions like TextAtlas combine both automatically, so you don’t need to think about which type of search to use. Your customers just get better results, whether they’re searching for exact product codes or describing what they need in their own words.