Search Relevance

Search relevance is one of those terms that sounds technical but actually describes something very simple: when a customer searches for something, do they find what they’re looking for?

That’s it. That’s search relevance in a nutshell. But getting it right makes an enormous difference to your sales, and getting it wrong costs you customers every single day.

What good search relevance looks like

Imagine a customer walks into a physical store and asks “Do you have comfortable running shoes?” A good sales assistant doesn’t just point to any shoes, they show running shoes specifically, prioritize ones known for comfort, maybe ask about the customer’s needs, and show the best matches first.

Good search relevance works exactly the same way. When someone searches for “comfortable running shoes”, they should see running shoes (not just any shoes), products that are actually comfortable (based on descriptions, reviews, or features), and the best matches should appear at the top of the list, not buried on page three.

Sounds obvious, right? Yet many webshops get this wrong. Let me show you why.

When search relevance goes wrong

Let’s look at some real examples of poor search relevance and what causes them:

A customer searches for “blue dress”. Your search shows all dresses, but the blue ones are mixed randomly throughout the results instead of appearing first. This happens when the search treats “blue” and “dress” as equally important, rather than understanding that color is a filter that should strongly influence which products appear.

Another customer searches for “winter coat”. Your search shows coats, but also jackets, sweaters, cardigans, and even scarves, all equally ranked. The customer has to scroll through dozens of items to find actual winter coats. This happens when the search doesn’t understand that “winter coat” is a specific product type, not just any winter clothing.

A third customer types “gift for dad”. Your traditional keyword search looks for products literally containing those words and probably returns nothing, or random items that happen to have “gift” in their description. This happens because the search doesn’t understand intent, it can’t interpret that the customer wants gift-appropriate items suitable for men.

Each of these scenarios represents poor search relevance. And each one means a frustrated customer who might leave without buying anything.

What determines relevance?

Search relevance depends on several factors working together. Understanding these helps you see why modern search is so much better than old-fashioned keyword matching.

First, there’s text matching, does the product description actually relate to what was searched? This is the baseline. If someone searches for “lamp”, products about lamps should obviously appear.

But text matching alone isn’t enough. Relevance also considers context. A search for “laptop bag” in your electronics category should prioritize tech-focused bags over fashion bags, even if both contain the words “laptop bag”. The category context matters.

Then there’s behavioral data. If hundreds of customers search for “waterproof jacket” and consistently click on and buy products with Gore-Tex in the description, the search learns that Gore-Tex jackets are highly relevant for that query, even though the words don’t match exactly.

Product attributes matter too. In-stock products are more relevant than out-of-stock ones. Products with good reviews are more relevant than ones with poor reviews. Recent products might be more relevant than old ones, depending on your business.

Finally, there’s personalization. For some customers, a size 42 shoe is highly relevant; for others, it’s completely wrong. Search relevance can consider individual customer characteristics to show more personally relevant results.

The cost of poor relevance

Let’s talk numbers, because poor search relevance has a real, measurable impact on your business.

Studies consistently show that visitors who use search are 2-3 times more likely to make a purchase than those who just browse. These are high-intent customers who know what they want. But here’s the crucial part: they only convert if the search actually works well.

When search relevance is poor, you see several problems: customers refine their searches multiple times, trying different words to find what they want. They click on products but immediately return to search because the results weren’t relevant. They eventually leave without purchasing. They develop negative associations with your store, if they can’t find products even when you stock them, they assume you don’t have what they need.

The data on this is stark. Improving search relevance typically increases search-driven conversions by 20-30%. That’s not a small improvement, that’s a major impact on revenue.

How modern search achieves high relevance

This is where technology makes a huge difference. Traditional search relied on keyword matching and manual configuration. You had to teach the system that “sneakers” and “trainers” mean the same thing. You had to set up rules for every common query. You had to constantly maintain synonym lists and search configurations.

Modern AI-powered search learns relevance automatically. It analyzes thousands or millions of search-click-purchase patterns to understand what makes results relevant. It discovers that when people search for “warm jacket”, they buy products described as “insulated”, “thermal”, or “winter”, so those products become more relevant for that search.

It understands intent without you teaching it. A search for “gift for mom” triggers different relevance signals than “shoes for running”. The system recognizes these are different types of queries requiring different approaches to relevance.

It learns from mistakes. If customers consistently skip certain products in search results, those products become less relevant over time. If they consistently click on products you might not expect, those products become more relevant.

Real-world example: before and after

Let me show you a concrete example of how search relevance improvements play out.

Before: A customer searches “comfortable office chair”. Your basic keyword search returns 50 products containing those words, in no particular order. The first result is a cheap basic chair that happens to mention “office” and “comfortable” in the description, even though it has terrible reviews. The actually good ergonomic office chairs are mixed in randomly throughout the results. The customer has to carefully examine each product to find good options. Maybe they find something, maybe they give up.

After: With good search relevance, the same search returns products ranked by multiple relevance signals. Ergonomic office chairs specifically designed for comfort appear first. Products with good reviews rank higher than those with poor reviews. Best-sellers that other customers bought for similar searches appear prominently. The customer sees the best options immediately, without scrolling or filtering. They click on the first few results, find one they like, and purchase.

The difference? In the first scenario, the customer might spend 10 minutes searching and still not find what they want. In the second scenario, they find a great product in under a minute. That’s the impact of good search relevance.

How to improve relevance in your store

If you run a webshop, you might be wondering what you can actually do about search relevance. The good news is that modern search solutions handle most of this automatically, but there are some things you can influence:

Product data quality is foundational. The better your product titles, descriptions, and attributes, the better the search can understand what each product is and match it to relevant queries. A product titled “Shirt” is harder to make relevant than one titled “Men’s Blue Oxford Button-Down Dress Shirt”. More information means better relevance.

Keeping your catalog current matters. Remove discontinued products, mark out-of-stock items, update seasonal categorizations. Current data leads to relevant results.

Let the system learn. The more customers use your search, the better it gets at determining relevance. AI-powered search improves over time, learning from actual shopping behavior in your specific store with your specific products and customers.

Monitor your search analytics. Look for queries with high search volume but low conversion, these indicate relevance problems. Look for queries that lead to multiple refinements, another sign that initial results weren’t relevant enough.

What this means for you

Search relevance isn’t just a technical metric, it’s the difference between customers finding what they want and customers leaving frustrated. It directly impacts your conversion rate, revenue, and customer satisfaction.

The good news is that with modern search technology, you don’t need to manually tune relevance. Systems like TextAtlas use AI to automatically learn what makes results relevant for your products and your customers. The search continuously improves, becoming better at showing relevant results as it learns from customer behavior.

Your job is simply to provide good product data and let the system work. The result is search that actually helps customers find what they’re looking for, every single time they search.