Helpful information ...

How to improve an online store's search function

A customer who can't find a product on your online store within a few seconds often won't keep browsing through categories. They'll leave for somewhere else. That's why the question of how to improve your online store's search isn't a technical detail — it's a direct sales question. A good search takes a visitor quickly from a vague need to the right product. A poor search shows them an empty page, unrelated results, or too much choice with no clear order.

This is even more pronounced for specialized stores. Customers often search by model number, dimensions, materials, brand, compatibility, or technical terms. A generic solution that only recognizes an exact match to the product name quickly falls short here.

Search should understand customer intent

A basic search engine compares the typed word against the product title. That's a starting point, not a solution. Customers rarely use the same term the store's admin panel does. Someone might type "black sneakers size 42," while the product is listed in the system as "Men's Urban Black Sports Shoes." They might type a code without a space, use a colloquial term, or make a typo.

A good search engine therefore searches across multiple data points at once: product name, description, category, brand, SKU, tags, attributes, and other data relevant to that particular store. Hierarchy matters too. An exact match on the name or SKU should take priority over a loose match somewhere in a longer description.

This isn't a universal setting that makes sense to apply identically to every store. A spare-parts store needs very precise search by SKU and compatibility. A fashion store, on the other hand, benefits more from a good understanding of colors, cuts, sizes, and synonyms. Search should follow the logic of your own product range, not the limitations of a pre-built platform.

How to improve your online store's search with better data

Even the best technology can't properly rank products if the underlying data is incomplete or inconsistent. Search and the catalog are connected systems. If product attributes are entered inconsistently, filters become useless and results unconvincing.

For every product, it's worth defining which data points actually help a customer decide. That could be size, color, material, intended use, power, dimensions, vehicle type, brand, price range, or stock status. Consistency in how these are recorded matters just as much. If one entry says "stainless steel," another says "inox," and a third says "rust-proof steel," the system won't treat these the same way without deliberate normalization of the data.

Synonyms and alternative spellings help here. Customers might search for "vacuum cleaner," "vac," or the name of a specific product line. They might swap a letter, drop a diacritic, or type part of a code. For frequently searched terms, it's worth setting up rules that link them to the right products or categories.

Don't overdo the tagging just to make a product show up in as many searches as possible, though. If a user searching for a specific product gets ten irrelevant results, trust in the search function drops quickly. Relevance matters more than the number of hits.

Autocomplete suggestions shorten the path to a product

The search box is often the fastest route through a large catalog. Autocomplete suggestions while typing can shorten that path even further. As a customer starts typing, they should see useful suggestions for products, categories, or brands. For a larger catalog, it helps to also show an image, a price, and a basic detail that distinguishes the product from similar options.

This display needs to be fast and restrained. If it shows dozens of random results after just two letters, it makes choosing harder rather than easier. Good practice is to activate suggestions only once the input is specific enough, and to keep the most relevant results at the top.

The mobile experience deserves particular attention. On a smaller screen, the search box needs to be clearly visible, typing needs to be easy, and suggestions need to be clear without covering important page elements. A lot of purchases start on a phone, so a desktop-only solution isn't enough on its own.

Filters should help people decide, not create extra work

When a search returns multiple results, filters take on an important role. Their purpose isn't to display every piece of data the store has — it's to help the customer quickly narrow down their choice. For a store selling technical equipment, that might be power, dimensions, connectors, and compatibility. For cosmetics, skin type, ingredients, intended use, and certifications make more sense.

Set up filters based on how customers actually shop. If customers typically pick a size first and then a color, the filter order should reflect that. If almost no one uses a particular filter, it doesn't necessarily deserve a prominent spot. Base these decisions on visitor behavior data, not just intuition.

It's also important that filters clearly show the consequence of a selection. Users need to understand what's currently active, how many products remain, and how to easily remove a filter. For categories with very diverse products, filters can be dynamic — showing only the attributes that are relevant to the current set of results.

A no-results page shouldn't be a dead end

An empty page that just says "No results" is a missed opportunity. There are many reasons a search can fail: a typo, a different way of writing something, a product that's currently out of stock, or the system simply not recognizing the user's term.

A good no-results page offers a next step. It can suggest a corrected search term, show related categories, display popular products, or offer a way to get in touch. For larger stores, it's worth logging failed searches. That's often where you'll find the terms visitors actually use that the store hasn't yet linked to its catalog.

If people frequently search for a product you don't sell, that's not necessarily a flaw in the search engine. It can be a useful signal for purchasing decisions, content, or presenting an alternative product more clearly.

Speed and ranking determine trust

Users don't evaluate the system's architecture — they evaluate how it feels to use. If results take too long to load, filters freeze, or the page reloads on every click, the search feels unreliable. Speed is therefore part of the user experience, not just a technical metric.

The same goes for result ranking. Sorting by default order — date or alphabetical — is rarely the most sales-effective approach. Products that closely match the search term, are in stock, and fit the store's business logic should be front and center. In some cases it makes sense to also factor in popularity, margin, or seasonality, but carefully. Paid or business-prioritized placement should never override clear relevance, since customers quickly notice when a result has nothing to do with their search.

For stores connected to a warehouse, an ERP, or other external systems, up-to-date stock status is also essential. Showing a product that can't actually be ordered creates unnecessary questions and adds pressure on your support team.

Measure search performance — don't guess

Overhauling your search isn't a project you finish once and forget about. Customer habits, your product range, and the season all change. That's why you need to track what visitors are searching for, which queries return no results, which filters get used, and whether people add a product to their cart after searching.

Particularly useful data points include the exit rate after a search, the share of searches that return no results, and differences between mobile and desktop users. If a particular search generates a lot of views but few purchases, the problem could lie in ranking, pricing, product presentation, or the offer itself. That data only becomes useful once you connect it to a concrete action.

With a custom-built solution, the advantage is that you can tailor search to your company's processes, your catalog structure, and data from connected systems. When building online stores, Moxy Web doesn't treat decisions like these as an afterthought tacked onto the end of a project — they're part of the store's sales logic from the start.

Start with the ten most common search terms in your store. Check the results through the eyes of a customer, not an administrator. If the path to the right product isn't fast, clear, and convincing, that's the best reason to improve it.