Shoppers who use site search usually know what they want. They type a product name, a feature or a problem, and expect the right products on the first screen.
Whether that happens depends on two things: the search tool, and the product data it searches. A search engine can only match what's in the product record, so a missing attribute or an unfamiliar product name can hide the right product from the shopper looking for it.
This guide covers both, with 14 best practices and examples from online stores.
What is ecommerce site search and how does it work?
Ecommerce site search, also called internal search or on-site search, is the search bar that lets shoppers find products on an online store by typing what they want.
When a shopper searches, the search engine looks through the product data behind each listing, including titles, descriptions, categories, attributes and tags. It then ranks the matches by relevance, often adjusted for popularity and stock. Search relevance is how closely those results match what the shopper meant, and it depends on both the ranking rules and the product data behind each listing.
Because the search engine reads the product data, the quality of that data sets the limit on what search can find. For example, a search for "waterproof hiking boots" can only return boots whose records say they're waterproof.
Why does site search matter for ecommerce?
Visitors who use site search often arrive with a clear purchase intent. Forrester found they are 2 to 3 times more likely to convert than other visitors.
- Higher conversion. The faster shoppers find the right product, the more likely they are to buy it.
- Customer insight. Search queries show what shoppers want and the words they use, including products you don't stock yet.
- Personalization. Search history and past purchases can shape results for each shopper.
What features should ecommerce site search have?
Most ecommerce search tools offer a similar core set of features:
- Autocomplete and typo tolerance
- Synonym handling
- Natural language and semantic search
- Faceted filters
- Merchandising rules for boosting or burying products
- Search analytics, including zero-result queries
- Results for non-product content, such as return policies
The best practices below cover how to get the most from each one, starting with the product data they all depend on.
14 ecommerce site search best practices (with examples)
These best practices apply whichever ecommerce search engine you use.
1. Fill in the attributes shoppers search for
Search can only match details that exist in the product record. If a pair of boots is waterproof but no field says so, a search for "waterproof boots" won't find it.
Start with the attributes shoppers use most in their searches, such as material, size, fit or compatibility, and check how many products in each category have a value. Fill the gaps in the categories with the most search traffic first.
2. Name and tag products with the words shoppers use
Product names and tags should use the terms shoppers type, not internal codes or supplier names. For example, a product named "Women's Cotton Crew Neck T-Shirt" with tags like "tee", "short sleeves" and "cotton" can be found through any of those words.
Use descriptive ecommerce product tags that cover attributes like color, size, material and style, so shoppers with specific needs can find a match.
3. Standardize attribute values
"Fragrance-free" and "Unscented" should be one value, and every measurement should use the same unit. When the same detail is written several ways, search and filters treat each version as a different value, and shoppers miss products tagged with the others. Our guide to faceted search best practices covers this in more detail.
4. Make the search bar easy to find
Show the search bar on every page, in the header, with a clear label like "Search products" and a magnifying glass icon. On mobile, keep it visible without an extra tap.
5. Add autocomplete and typo tolerance
Autocomplete suggests queries and products as shoppers type, which saves time and guides them toward terms that return results.
Typo tolerance catches misspellings. A search for "shurt" should still return shirts, so a typing mistake doesn't end in an empty page.
6. Map synonyms
Shoppers describe the same product in different ways. A search for "couch" should also return sofas and sectionals, and "sneakers" should find "trainers".
Build a synonym list from your search logs, starting with the queries that return few or no results. For key terms, add common alternatives to the product data too, so every tool that reads the record can match them.
7. Understand natural language
Shoppers increasingly type the way they'd ask a person, such as "gray sofa for a small apartment under $1,000". Natural language and semantic search interpret the intent behind the words, not just exact keyword matches.
For example, a search for "summer dresses" can return sundresses and maxi dresses, even when "summer" isn't in their titles. The results are only as good as the attributes behind them, such as fabric weight or sleeve length.
8. Use faceted search
Faceted search lets shoppers narrow results by product attributes such as price, size, color and brand, with several filters combined at once. It matters most for large catalogs, where a single search can return hundreds of products.
Facets work only when products have consistent values for each attribute. See our guide to faceted search best practices for how to choose and set them up.
9. Set merchandising rules
Merchandising rules let you adjust results beyond the search query itself. For example:
- Boost popular or high-margin products
- Highlight new arrivals or promotions
- Push out-of-stock items down the list
- Show related accessories alongside the main product
See our guide to ecommerce merchandising for more on these tactics.
10. Fix zero-result searches
A search with no results tells the shopper you don't have what they want, even when you do. Review your zero-result queries every month, starting with the most frequent.
Each one usually points to a specific fix: a missing synonym, a misspelling the search doesn't catch, a product that's missing the attribute being searched for, or a product you don't stock. When there's no match, show close alternatives or popular products in place of an empty page.
11. Include non-product results
Shoppers also search for information, such as return policies, shipping times, store hours or care guides. Index those pages too, so a search for "returns" leads to the returns policy.
12. Design search for mobile
Mobile devices account for over 75% of ecommerce traffic. On a small screen:
- Keep the search bar visible and easy to tap
- Show fewer suggestions, with product images
- Let shoppers pick several filters, then apply them together
- Keep results fast on slower connections
13. Show trending searches
Showing popular or trending searches when a shopper taps the search bar helps them discover products and gives them a starting point.
Ensure product pages load instantly from search results
Optimizing product search improves engagement, but to take things further and encourage conversions, it's essential to ensure that products in the search results load instantly upon click.
New technologies now allow you to preload these pages in advance, making the navigation experience smoother and more engaging. Tools like Navigation AI simplify this process by predicting user behavior and pre-rendering the next page, helping ecommerce sites boost speed and reduce drop-offs.
14. A/B test search changes
Test one change at a time, such as a new autocomplete layout or ranking rule, and measure its effect on search click-through and conversion before rolling it out.
How do you measure ecommerce site search performance?
Track these metrics every month, on mobile and desktop separately:
- Search usage rate. The share of sessions that include a search.
- Search click-through rate. The share of searches followed by a click on a result.
- Search conversion rate. The share of searches followed by an add to cart or purchase.
- Zero-result rate. The share of searches that return nothing.
- Search exit rate. The share of searches after which the shopper leaves the site.
- Top queries. The most common searches, and which of them lead to few clicks.
Low click-through on a common query often means results are there but not relevant. That usually traces back to product names, attributes or ranking rules.
How do you choose an ecommerce site search tool?
Built-in platform search is often limited to matching titles and descriptions. When comparing dedicated tools, check:
- Typo and synonym handling, including how easy it is to add your own synonyms.
- Faceting from your own attributes, with facets that adapt to each category or query.
- Merchandising controls that your team can use without developers.
- Search analytics, including zero-result reports and query-level click-through.
- Integration with your ecommerce platform and your product data source, such as a PIM.
- Speed, with suggestions appearing as shoppers type.
Whichever tool you choose, it can only search the product data you give it.
How does Hypotenuse AI improve site search?
Hypotenuse AI is an AI-native PIM that improves the product data site search reads, so the right products show up for the words shoppers use.
| Best practice | What Hypotenuse AI does |
|---|---|
| Fill in the attributes shoppers search for | Fills missing attributes from the web, PDFs and product images. It shows how sure it is of each value and where it found it, so your team knows which values to check before they go live |
| Name and tag products with the words shoppers use | Tags products from their images and writes titles and descriptions using the terms shoppers search for, so products match more of the queries they should |
| Standardize attribute values | Maps every tag to your own attributes and allowed values, so "0.25 in." and "1/4 inch" become one value, and a search or filter for 1/4 inch finds every matching product |
| Bring in supplier data | Maps supplier fields to your attributes and normalizes values, such as Small and s to S, so products from every supplier are searchable the same way |
Ecommerce site search FAQs
How important is search in ecommerce?
Very. Site search users often have a clear purchase intent, and Forrester found they are 2 to 3 times more likely to convert than other visitors. A search that returns the wrong products, or nothing, sends those shoppers elsewhere.
How does ecommerce search work?
The search engine matches the shopper's query against product data such as titles, descriptions, attributes and tags, then ranks the matches by relevance. More advanced tools add typo tolerance, synonyms, natural language understanding and personalization.
What are the different types of search in ecommerce?
Shoppers search in four main ways:
- Exact search: the shopper knows the product name or model
- Product type search: the shopper searches for a general product or category, such as "laptop"
- Problem-based search: the shopper describes a need, such as "back pain chair"
- Non-product search: the shopper looks for information, such as return policies
.avif)