How to Improve Product Discoverability: Product Search vs Discovery

Picture three shoppers walking into your store… Your online store. 

The first knows exactly what they want. Brand, model number, the lot. The second knows what they need it for. They just don’t know what it’s called. The third is browsing. Maybe they’ll buy something. Maybe they’ll buy three somethings.

Good search looks after the first two. Good discovery looks after the third. A great store looks after all of them.

This post covers how to do both. (Plus where AI fits in, because we know you were going to ask).

Product Search vs Product Discovery

Quick definitions first. Then we’ll get practical.

Product search helps shoppers find something specific. They type, they filter, they find. Hopefully…

Product discovery shows shoppers things they weren’t looking for. Related products, “you might also like” blocks, that sort of thing.

In real life, the line is blurry. Someone searches for a tent and leaves with a sleeping bag too. So instead of thinking in features, think in mindsets:

1. “I know what I want.” Search needs to get them there fast.
2. “I know what I need it for.” They need help turning a problem into a product.
3. “Show me something.” This is where discovery earns its keep.

We didn’t pull this out of thin air. In Baymard Institute’s research, roughly half of test participants preferred search for finding products, while the other half used the main navigation. 

Baymard also found that some shoppers don’t even know what type of product they need. All they can describe is the problem they want to solve.

Make Search Understand Your Shoppers

First, the bad news. Baymard looked at more than 170 ecommerce sites and apps, and 56% of them didn’t adequately support what users were searching for.

The good news? There’s plenty of room to stand out. Here’s where to start.

Match different ways of saying the same thing. “TV” and “television”. 13 inch and 13″. If those give different results on your store, that’s a problem. And yes, it happens at big retailers too. Your customers won’t adapt to your wording. Your search has to adapt to theirs.

Forgive typos. Some shoppers have only ever heard a product’s name out loud. Don’t punish them for spelling it the way it sounds. 

Understand descriptive searches. People search by feature (“leather jacket”), by use case (“gaming chair”), and even by problem (“stained carpet”). Lots of stores struggle here. 39%, 43%, and 37% of sites have issues with feature, use case, and problem-based searches, respectively. 

Get autocomplete right. Suggest products and categories as people type. Keep the list short. And make sure it handles common typos too.

Don’t let search become a dead end. If a search for something you sell comes up empty, shoppers usually assume you don’t stock it. Then they leave. Give your no-results page a next step instead: related categories, popular products, or a way to get in touch. 

Let people search for more than products. In one study, 34% of shoppers used the search bar to find things like the return policy or how to cancel an order. One big retailer’s search answered “return policy” with a LEGO set. Fun for the kids. Less fun for the customer. 

Read your search logs. They tell you exactly which words your customers use. Got a physical shop? Ask your staff too. Customers tend to describe their problems the same way in-store as they do online.

Guide Shoppers Who Know the Problem, Not the Product

Mindset two. Their knee hurts. They don’t know whether they need a sleeve, a brace, or new insoles.

So a search for “knee pain” shouldn’t just show everything with “knee” in the name. It should show the range of things that might actually help.

A few ways to get there:

• Turn search words into filters. Someone searches “blue shirt”? Show shirts with the blue filter already on. They can see how you understood them and change it in one click.
• Add product finders. A few quick questions can narrow a big range fast. They’re great for gifts, spare parts, and anything where compatibility matters.
• Link your buying guides. Put them right on the search results page, where people are actually comparing options.
• Keep categories logical. If your menu needs explaining, it’s too complicated.

Product Data Is the Foundation

Not glamorous. But very important.

Search can only find what’s described. Filters can only filter what’s tagged. Recommendations can only connect what they understand.

In practice, your product data matters as much as your search engine. Take a search like “spring jacket”. Unless your catalogue says which jackets suit spring, even the smartest search is just guessing.

So check the basics. Clear titles. Consistent attributes. Good images. Useful descriptions. Products in the right categories.

Show Shoppers What They Didn’t Know They Needed

Mindset three. The browser. Or the shopper who’s one click from checkout and might like a gentle nudge.

This is upsell and cross-sell territory. Done well, it feels like a helpful shop assistant. Done badly, it feels like a pushy one.

Good news: you probably have the tools already.

• Magento lets you set related products, up-sells (similar items that might be higher quality or more popular), and cross-sells, which show up in the cart as last-minute additions. 
• WooCommerce lets you set up-sells and cross-sells in each product’s Linked Products tab, and it creates related products automatically based on shared categories and tags. 
• Shopware 6 has built-in cross-selling that you configure per product, using either manual selections or dynamic product groups.

Our one rule: relevance first. Would a good shop assistant suggest it? If the honest answer is “only if they were on commission”, skip it.

How AI Can Help (and Where It Can’t)

AI can make search and discovery a lot smarter. But it isn’t magic, and it’s only as good as what you feed it. Here’s where it’s genuinely useful right now.

Understanding What Shoppers Mean

Old-school search matches words. AI search tries to work out what the shopper actually means.

So “something warm for a winter hike” can return jackets, base layers, and gloves. Even if none of them have “winter hike” in the title.

You don’t need a custom build for this. The platforms are adding it themselves. Shopware’s AI search lets shoppers use everyday language to search for products. And in 2026, Adobe made Semantic Search generally available, included at no additional cost as part of Live Search. It uses AI to understand shopper intent rather than just matching keywords.

On Magento Open Source? Live Search is an Adobe Commerce feature, so you’ll usually be looking at a search extension or third-party provider instead.

Searching With Pictures

Sometimes shoppers know what something looks like, but not what it’s called. Visual search lets them upload a photo and find similar products.

Handy for fashion, furniture, and anything where “you know, the swirly one” is the best description going.

Shopware has image search built in. Shoppers upload an image and get suggestions for similar items. 

Chatting With Shoppers

Shopping assistants let customers ask questions the way they would in a shop.

Zalando’s assistant is a good example. Customers can ask what to wear to a family birthday in another city next month, and the assistant takes context like location, weather and occasion into account. Customers can now also snap a photo of something they like in real life, and the assistant finds that product or similar alternatives in the catalogue. 

And people are using it. By early May 2026, close to 10 million customers had already asked the assistant for advice that year, up from 6 million in the whole of 2025. 

Zalando has a team for this. You probably don’t. For most merchants, the realistic route is an assistant offered by your platform or search provider.

Smarter Recommendations

Instead of linking every product by hand, AI recommendations learn from how shoppers actually behave.

Think “customers who viewed this also viewed”, but kept up to date automatically. On Adobe Commerce, that’s Product Recommendations. For other platforms, there are plenty of extensions that do something similar.

Fixing Product Data Behind the Scenes

Honestly? This might be the most useful one for most merchants.

AI can draft product descriptions, suggest missing attributes, tag your images, and summarise reviews. Shopware’s AI features include an image keyword assistant, AI-generated product properties, review summaries, and a description assistant.

Better data makes everything else in this post work better. So it’s a smart place to start.

Where AI Falls Short

It’s only as good as your data. See the previous section. We’ll wait.

Check before you publish. Review anything AI writes about your products before it goes live. Your customers will trust it, so you need to trust it first.

Start with a problem, not a tool. Lots of zero-result searches? That’s a problem worth solving. “Everyone has a chatbot now” isn’t.

Every new feature is one more thing your store has to run. Which brings us neatly to…

Speed Matters

We’re a hosting company. You knew this section was coming.

Search and discovery are some of the hardest-working parts of your store. Search results and personalised recommendations change from shopper to shopper, so they’re much harder to cache than a regular page. And autocomplete can fire off a request with every single keystroke.

Everything in this post runs on your server. Your search engine, your recommendation blocks, that shiny new AI extension. So here’s how Hypernode helps.

OpenSearch, ready when you are. Magento has supported OpenSearch 2.x since version 2.4.6, and on Hypernode, switching to it just means enabling one setting.

Test first with Brancher. Hypernode Brancher is a temporary copy of your Hypernode, so you can test changes in a real environment and clean it up when you’re done. Ideal for trying a new search extension or AI tool without touching your live shop. 

Handle the peaks with Autoscaling. Vertical Autoscaling adds resources to a single server as traffic changes. Horizontal Autoscaling adds or removes entire server instances, with zero downtime. Horizontal Autoscaling was built for seasonal traffic spikes from events like Black Friday. A few requirements apply, so check them before you count on it for your next big sale.

Shameless plug over. Let’s finish with a TL;DR recap. 

TL;DR Checklist

• Walk through your store as all three shoppers
• Make search handle synonyms, abbreviations, and typos
• Support feature, use case, and problem-based searches
• Get autocomplete right, and turn no-results pages into next steps
• Let search find non-product content like your return policy
• Read your search logs regularly
• Use filters, product finders, and buying guides for shoppers who know the problem
• Set up relevant related products, upsells, and cross-sells
• Clean up your product data
• Use AI to solve a specific problem, starting with your product data
• Test changes before they go live, and make sure your hosting can handle the peaks
• Measure, tweak, repeat