Most ecommerce teams treat search and merchandising as two separate problems. Search is a
technical feature. Merchandising is a creative and strategic one. But the best-performing online
stores know these two things work better together — and when you connect them
intentionally, the results show up directly in your average order value.
The way customers find products tells you a lot about what they want. And what they want —
when surfaced at the right moment — turns a single-item order into a multi-item cart.
How Search Data Informs Merchandising
Your site search is one of the most honest data sources you have. When a customer types
something into your search bar, they’re telling you exactly what they’re looking for in their own
words. That’s valuable information — and most stores barely use it.
Looking at what people search for reveals patterns that aren’t always obvious from category
browsing alone. You might notice that customers frequently search for a specific use case, like
“outdoor safe” or “bulk order,” even though you don’t have a category organized that way.
That’s a signal to create one.
Search data also shows you which product attributes matter most to your customers. If people
are consistently searching by material, color, size, or a specific feature, your filters and facets
should reflect that. When the browsing experience matches the mental model customers
already have, friction drops and conversion goes up.
On the merchandising side, this means letting search behavior guide decisions about which
products to feature, how to organize collections, and where to focus your promotional energy.
Instead of guessing what shoppers want, you’re following the data they’re already giving you.
Using AI to Recommend Context-Aware Add-Ons
Traditional product recommendations work on a pretty simple model: show people what other
customers bought. That’s better than nothing, but it misses a lot of context. A shopper
searching for a specific item, at a specific price point, for a specific purpose, doesn’t necessarily
want what the average customer bought alongside a different product in a different category.
AI-powered recommendations can close that gap. By factoring in search terms, browsing
history, cart contents, and broader behavioral signals, AI can surface add-ons that actually make
sense for what the customer is trying to accomplish right now. That’s a meaningful difference.
Think about a customer who searches for “camping stove” and lands on a product page. A
generic recommendation engine might show them other stoves. A context-aware system
understands that they’re likely outfitting for a trip and surfaces fuel canisters, cooksets, or fire
starters — things they probably need and might not have thought to search for separately.
This kind of recommendation isn’t just good for AOV. It’s genuinely helpful to the customer,
which means it builds trust and reduces the likelihood they’ll go elsewhere to complete their
purchase.
Reducing “No Results” Searches
Few things kill a sale faster than a “no results found” page. When a customer searches for
something and gets nothing back, they don’t usually dig deeper into your site — they leave.
And if it happens often enough, they stop trusting your search entirely and go somewhere else.
The first step to fixing this is knowing where it’s happening. Regularly reviewing your zero-result
searches tells you which queries are falling through the cracks. Sometimes the fix is simple: a
product you carry is being searched under a different name, or customers are using shorthand
you haven’t accounted for in your product data.
From there, there are a few ways to address the problem. You can improve your synonym
mapping so that “couch” and “sectional” both return the right results. You can update product
descriptions and tags to match the language customers actually use. Or you can set up redirects
for common searches that point to the best available category or product.
When search works well, it doesn’t just prevent drop-off — it actively drives customers toward
products they’re ready to buy. That’s the intersection where discovery turns into revenue.
Putting It Together
The stores that see the biggest gains from search and merchandising working together are the
ones that treat it as an ongoing practice rather than a one-time setup. They review search data
regularly, update their merchandising rules based on what they find, test new recommendation
placements, and iterate on what’s working.
It’s not complicated, but it does require intentionality. The data is already there. The question is
whether you’re using it to guide customers to more of what they want — or leaving that value
on the table.
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Lucinda Miller