Deconstructing E-Commerce Search UX: The 8 Most Common Search Query Types (42% of Sites Have Issues)

Key Takeaways

  • Our current e-commerce search UX benchmark reveals 42% of sites fail to fully support 8 key search query types performed by users
  • Our large-scale search testing indicates that without support for key search query types users will have difficulty finding products they’re looking for — if they find them at all
  • Supporting just a few of the 8 key search query types can set your site apart from competitors and help ensure users are able to find what they need

Our latest e-commerce UX search benchmark — based on more than 3,500 manually set UX performance ratings — reveals that there’s only mediocre support at e-commerce sites for 8 key search query types. These 8 query types are those our large-scale search usability testing has consistently revealed to be the most important query types used by users when searching for products on e-commerce sites.

Indeed, 42% of all sites perform below an acceptable e-commerce search UX performance across the 8 e-commerce search query types discussed in this article. To make matters worse, 8% of sites have a downright “broken” e-commerce search UX when it comes to the performance of the 8 key query types.

While there has been a steady improvement in some of the e-commerce search query types over the past 8 years (as evident in our e-commerce search UX benchmarks from 2014, 2017, and 2019), there are still some fundamental search issues, for example:

  • 71% of sites require their users to search by the exact same product type jargon the site uses (e.g., failing to return all relevant products for a search such as “blow dryer” if “hair dryer” is used on the site, or “multifunction printer” vs “all-in-one printer”, etc.)
  • 36% of sites don’t support thematic search queries (e.g., “spring jacket” or “office chair”)
  • 49% of sites don’t support symbols and abbreviations for even the most basic units, resulting in users missing out on perfectly relevant products (e.g., if searching for “inch” when the site has used ' or in in its product data)
  • 39% of sites don’t support non-product search queries (e.g., “returns” or “order tracking”)

As a result, these implementations of e-commerce search will often directly misalign with users’ actual search behavior and expectations — leading to frustrating search experiences, time wasted refining queries, and abandonments.

In this article, we’ll go over our large-scale UX Premium research findings for each of the 8 search query types most common for e-commerce search UX.

In particular, we’ll discuss the overall performance of the 8 search query types across the e-commerce landscape, the observed user behaviors and UX issues, and the ways to best support each search query type.

8 Key Search Query Types

Below we’ll dig deeper into the search query issues users often encounter during product finding by discussing 8 specific e-commerce search query types:

  1. “Exact” search queries (42% of sites have issues)
  2. “Product Type” search queries (71% of sites have issues)
  3. “Symptom” search queries (52% of sites have issues)
  4. “Non-Product” search queries (39% of sites have issues)
  5. “Feature” search queries (22% of sites have issues)
  6. “Thematic” search queries (36% of sites have issues)
  7. “Compatibility” search queries (30% of sites have issues)
  8. “Slang, Abbreviation, and Symbol” search queries (49% of sites have issues)

1) “Exact” Search Queries (42% of Sites Have Issues)

When users know the exact product they are looking for, they will typically rely on Exact Search, entering the product’s title or model number, such as “Keurig 45” (a coffee maker).

“Exact Searches” are typically the easiest to support technically, and most of the tested sites fared reasonably well.

While this may at first seem like an easy case of keyword matching against those two product attributes, the search engine must be a little smarter than that and there are a few conditions to take into account — refinements that will take the “Exact Search” query implementation from acceptable to great.

For instance, good handling of phonetic misspellings is crucial since the user may only have heard the product title spoken and not know how to spell it (e.g., “Keurick 45”).

Furthermore, some products have alternative titles, such as Nestlé Quik vs. Nesquik or AT&T Wireless vs. Cingular Wireless vs. AT&T Mobility — all of which should invoke their respective product. This is particularly important for global e-commerce sites where product naming may be localized.

Additionally, users often don’t type out the product title themselves. During testing, participants were often observed to copy-paste product titles from external sites into the search field.

Acquiring all these local and alternate spellings can prove quite the challenge and vendors may not necessarily input this data on their own. Partnering with relevant industry databases can therefore be a good way to acquire this product information — especially if they are public and users are copy-pasting from them in the first place.

“Let’s see if I can search my model number…I can, and there we are, so good.” This test participant was grateful that he could search by SKU (model number) in the Home Depot app. Allowing users to search by SKU ensures that they can quickly locate a specific model in which they have a particular interest.

Support for Exact Searches proved crucial during testing, as participants were observed to quickly conclude that a site didn’t carry the product if it didn’t show up when a model name or number was inputted (as opposed to more open-ended queries, such as Thematic Searches).

When we look at the benchmark, we find that 42% of sites are incapable of producing decent results for Exact Searches.

2) “Product Type” Search Queries (71% of Sites Have Issues)

When users aren’t looking for a specific product, but rather a type of product, they will rely on Product Type queries and query for a whole category of products (e.g., “Sandals”).

When used on their own, Product Type queries are generally an attempt by the user to quickly access a category on the site — either because it’s more convenient to search for it or because they are having difficulties finding the category via the main menu.

When the site can be sure of a 1:1 match with a Product Type query and an existing category, it’s worth autodirecting the user to the relevant matching intermediary category page, if one exists.

Still, a very important aspect of supporting Product Type queries is to return relevant results regardless of whether the searched-for product type exists as a category on the site or not.

This not only requires detailed categorization and labeling of products, but also proper handling of synonyms and alternate spellings of those groupings.

A participant at Target searched for ”ladies hoodies” and was presented with multiple ”women’s hoodies”. In this case, the use of ”ladies” has been well mapped to women’s products, making for a smooth experience where the user is unaware that these mappings are even occurring. Using synonyms for common Product Type queries speeds product exploration by presenting relevant results without requiring any user effort.

For example, a search for “t-shirt” should yield the exact same results as one for “tee shirt”, regardless of how it happens to be written in each product’s title or description. Other examples include “hair dryer” where the user might search for “blow dryer”, or “multifunction printer” or “copy machine” for an “all-in-one printer”.

From a user’s point of view these everyday descriptions are just as correct as the industry jargon, and most of the participants during large-scale testing never thought of trying another synonym when they received poor search results.

Instead, participants simply assumed that the poor or limited results were site’s full selection for such products.

Despite the severe impact on the user’s search experience 71% of sites do not return all the relevant results, if any at all, when users search by a product type or synonym.

The Product Type query is largely a missed opportunity within the industry and should always be one of the first things considered in any e-commerce search UX improvement project.

A few key types of synonyms to consider when auditing or trying to improve a site’s Product Type search capabilities include the following:

  • Near-identical word meanings (i.e., “multifunction printers” vs. “all-in-one printer”)
  • Regional dialect synonyms (i.e., “spanner” vs. “wrench”)
  • Regional spelling variations (i.e., “fibre” vs. “fiber”)

Since Product Type Searches are performed by users who are looking to browse a whole category of products, it’s also crucial that such queries enable relevant filtering and sorting options so the user can easily narrow down the list and compare products.

Ideally such filters and sorting options are available directly from the search results (via “faceted” search filters; Premium subscribers see #376 for more on this), although an acceptable alternative is to guide users towards a relevant category scope.

3) “Symptom” Search Queries (52% of Sites Have Issues)

As we’ve seen thus far, when users know the specific product they are looking for, they will use Exact query, and if they don’t know the exact product or aren’t sure about which one they want, they will often rely on Product Type queries.

However, sometimes users don’t even know the type of product they are looking for — all they know is the problem that they are experiencing and that they want a solution to it.

In these cases, they will rely on Symptom Queries: entering the problem they are experiencing, such as “stained rug” or “dry cough”, in hopes of being presented with viable solutions to this problem.

Symptom queries are important because they will often be the user’s last recourse: if users don’t know what solution to look for and can’t search for products by entering their problem or symptom, it’s going to be almost impossible to find the relevant products on the site.

Or, as one participant during our testing reasoned, “You must be able to search on anything. I’m used to that from Google”. This is especially true if categories on the site aren’t problem- or symptom-based (which they often aren’t).

Furthermore Symptom queries often have multiple different product types as relevant solutions, making it both difficult and inconvenient for users to find the best solution for them if the Symptom Search query type isn’t supported. For example, a symptom query for “knee pain” at a sports equipment site should provide users with an array of different product types such as knee pads, knee sleeves, supporting socks, pain-relieving products, sports tape, shock-absorbing shoe insoles, etc.

If search teams need inspiration for the complete range of possible solutions to a problem relevant for your industry, consider going to a physical store in your same product vertical and actually talk to the in-store experts, as users in physical retail will often have the same product-exploration approach.

At Chemist Direct, placeholder text in the search feature suggests users ”Search by product, brand or condition” — encouraging users to search by condition (or symptom) and reassuring them that such queries will be supported.

For sites where Symptom searches are particularly important, it’s often a good idea to suggest its usage in the placeholder text of the search field to let users know that they can search by symptom (as it isn’t all users who think to query by symptom).

Users can also be guided during search query formulation with the help of scope suggestions (which are a key component of autocomplete UX).

Scope suggestions are a good complement to “Symptom” queries because they begin to reveal hints at possible solutions (i.e., categories) that can help guide users to a related query path.

Integrating or interlinking any help or buying guides available on the site directly on the search results page is essential, too, so that users can learn more about the differences between the different solutions available.

Although Symptom queries won’t be applicable to every e-commerce industry, they are vital to some, such as drugstores, health and beauty, tools and hardware supplies, cleaning and housekeeping, specialty electronics, and B2B.

Yet the UX benchmark shows that 52% of sites within industries where Symptom queries are relevant don’t support them fully.

Indeed, on many sites, searching for a symptom like “knee pain” will primarily return any products related to the keyword “knee”, and predominantly show irrelevant knee-related products first. The actual solutions to the “knee pain” symptom are then scattered throughout the following hundreds of partial matches — in practice rendering it extraordinarily difficult for users to get an overview of their relevant options.

4) “Non-Product” Search Queries (39% of Sites Have Issues)

Searching for “return policy” on Amazon yields returns center links, as well as a short description of the return policy along with a set of links to relevant help sections.

At Wayfair, a search for “return policy” directs users to the “Returns Center” where numerous return-related FAQs are available.

“Non-Product” queries are when users search for something that isn’t a product, such as the return policy or shipping information.

While the primary function of search in an e-commerce context is obviously to find relevant products, the search engine shouldn’t be limited to just searching the product catalog, as we consistently observe that users expect the search field to search the entire website (not just the product catalog).

After all, that is typically what a search field does on any other non–e-commerce website. Indeed, during our Accounts & Self-Service usability testing 34% of participants tried to search for non-product content (e.g., “return policy”, “unsubscribe”, “cancel my order”, etc.).

During testing users would often search for this type of auxiliary content when they had difficulties finding navigational links to it. This is a logical consequence of this auxiliary content being secondary, and links to it therefore tend to be relegated to the page footer or nested deep within help sections.

On mobile, finding “Non-Product” content via on-page links or navigation can be even more challenging as these options are often even harder to find compared to desktop sites.

Yet despite its importance to users’ overall purchasing experience, 39% of sites don’t fully support Non-Product queries.

5) “Feature” Search Queries (22% of Sites Have Issues)

Feature queries are queries that include one or more product attributes in the search.

For example, a user may not want just any “jacket” but will often be looking for something slightly more specific, such as a “leather jacket”.

“Features” can be a wide range of product attributes, such as the following:

  • Color (e.g., “red dresses”)
  • Material (e.g., “fabric sofas”)
  • Performance specs (e.g., “100000 IOPS hard drive”)
  • Format (e.g., “Hobbit DVD”)
  • Price (e.g., “$100–$200 backpacks”)
  • Brand (e.g., “Revlon lipstick”)
  • Size (e.g., “size 8 sneakers”)

The list goes on, and all significant product attributes should be searchable, even if they don’t exist for all products sold on the site. For example, even if all products on the site don’t have a “format” attribute, movies on the site should still be searchable by it.

“Feature” queries are nearly always used as a qualifier for another search type — a way to filter the results of a certain search.

For example, the user may perform a “Product Type” query and then combine it with a “Feature” query to only get a subset of those products (e.g., “volumizing paraben-free shampoo” or “blue breathable north face jacket”). During our testing, participants also combined “Feature” queries with “Thematic”, “Symptom”, and “Compatibility” queries.

Users are becoming more and more accustomed to the robust features of major web and social media search engines and their almost uncanny ability to intelligently interpret and yield relevant results to complex search queries — an expectation that users increasingly carry over to e-commerce search.

A search for “red sofa” at Wayfair returns sofas with the “red” color filter preapplied, allowing users to quickly begin browsing products that are most relevant to their query.

In terms of implementation, the ideal solution is if to dynamically apply any features searched for as filters on the results page (if a filtering value for the feature exists).

This increases transparency and user control — with the user being able to see what is and isn’t included and being able to quickly toggle related filters on/off.

For instance, in the above Wayfair example, the “red” aspect of the “red sofa” query is applied as a color filter, with the user being able to see and toggle the other available colors of sofas.

Among the top e-commerce sites, only 22% don’t fully support Feature queries — a strong indication that the importance of feature queries is well understood by e-commerce sites.

Yet sites with poor support for Feature queries are therefore even more likely to be perceived negatively by users, given the relatively wide support for Feature queries in e-commerce.

6) “Thematic” Search Queries (36% of Sites Have Issues)

What exactly constitutes a “living room rug”, an “extreme weather sleeping bag”, or a “retro dress” ? While these are certainly concepts we can recognize, defining their exact boundaries can be a challenge.

Thematic queries are often a little difficult to define because they are inherently vague in nature and can include the following fuzzy boundaries:

  • Usage locations (e.g., “living room furniture”)
  • Seasonal or environmental conditions (e.g., “spring jacket”, “cold weather sleeping bag”)
  • Occasions and events (e.g., “wedding gift”)
  • Promotional attributes (“sale lipsticks”)

Nevertheless, they are very real notions to users who — in certain industries, such as apparel e-commerce and furniture e-commerce — include thematic qualifiers liberally in their searches.

At Kohl’s there’s a clear support for thematic queries. Here a query for “winter jacket” returns the “Coats & Jackets” category, with the “Weather: Midweight” and “Weather: Heavyweight” filters preapplied.

Clearly, a great deal of interpretation is required to support “Thematic” searches, both in terms of the meaning of the actual query itself and also in the internal tagging of products.

Indeed, it’s vital that a query for, for example, “spring jacket” presents all the relevant products, not just the handful of products that happen to have those keywords in their title or description.

This will often require some sort of thematic tagging of the product catalog to determine, for example, which jackets would be suitable for spring (and which wouldn’t).

Similar to Feature queries, the ideal support for most Thematic queries is often achieved by having these thematic attributes as actual filtering values that are then preapplied when users search by them.

This gives users direct insights into how the site has interpreted their thematic query and provides an easy way for users to narrow or broaden the thematic qualifier. (This also improves the general navigation-based filtering experience greatly.)

Similar mappings may be necessary for abstract queries such as “macbook power” — an actual search query one of the participants in our testing submitted when looking for an adapter for his MacBook Pro laptop.

Because users will sometimes think in these “Thematic” terms when typing into the search field, their product exploration is disrupted when results don’t meet their expectations — forcing users to refactor their queries in order to start a new search.

If “Thematic” search queries aren’t supported, users trying Thematic queries are left with absent, few, or irrelevant results, which can give the impression that the products sought simply aren’t available at the site.

In fact, 36% of our benchmarked sites have problems handling “Thematic” search queries, if the thematic identifier doesn’t happen to be part of the product title.

7) “Compatibility” Search Queries (30% of Sites Have Issues)

A search at Dell for “Vostro 2420 adapter” displays irrelevant products at the top of the results (a handset adapter, a camera adapter, and an incompatible battery). Moreover, users have no clear way to identify appropriate products in the list since compatibility details are excluded, meaning that users must click into each product page to determine compatibility and relevance.

Users often don’t know the name of the accessory or spare part they need — instead they know the details of the product they already own.

It’s therefore not uncommon to see users perform Compatibility queries, where they input the name or brand of a product they own along with the type of accessory or spare part they are looking for, such as “sony RX100 camera case”.

Compatibility queries require strict compliance — it’s two or more products that must work together. As such, they’re typically generated as follows:

  • By combining the brand name and specific model or series of the primary product (e.g., “Dell XPS 13 Touch Laptop”) along with an accessory type (e.g., “adapters”)
  • By combining the brand name and “Product Type” of the primary product (e.g., “dell laptop”) along with an accessory type (e.g., “screen protector”)
  • By using only the primary product name (e.g., “macbook pro”) and expecting to find supported accessories among the results
  • By using only the accessory or part name (e.g., “laptop adapter” or “vacuum cleaner belt”)

Users who know the exact model of their product will often submit this to search for compatible accessories, and Compatibility queries should therefore obviously work with product models. In fact, sometimes compatibility can only be ensured if the exact product model is provided.

However, users don’t always know or recall their specific product model, and support for more generic product types and brand names are therefore important as well, so that users can enter generic queries like “13in laptop sleeve” rather than specifying the exact laptop they own.

It’s useful to note that some users deliberately exclude the accessory type in their search and only search for the product they own, as they expect accessory products will be available next to the product. Other users simply forget to include the accessory product type.

In both cases, displaying an option to see accessory products for such searches will prove incredibly useful for these users, as it gives them a way to access products compatible with the product they already own.

At Crutchfield, when users search for “Subaru WRX speakers”, the site detects the car brand and model and presents users with all the car stereos that are compatible with this specific car model. This allows users to quickly begin exploring products instead of having to refine their search, or trying to find relevant products through category navigation.

Compatibility relationships can be very complex and have numerous dependencies that can be difficult to capture in a free text search.

It may therefore be a good idea to integrate Compatibility searches with any product finders or wizards available on the site.

For example, if a Compatibility search is detected for “Dell laptop adapter”, it could send the user to a “Laptop Adapter” wizard, ideally with “Dell laptop” preselected. Or the wizard could be displayed as an option among any autocomplete suggestions or on the search results page.

However, 30% of sites don’t fully support Compatibility queries, making it overly difficult for users to find relevant products.

8) “Slang, Abbreviation, and Symbol” Search Queries (49% of Sites Have Issues)

Users rely on a wide range of linguistic shortcuts when they search.

This quickly became evident during our testing as several of the users frequently relied on Slang, Abbreviation, and Symbol Searches, writing slang-based queries such as “RayBan shades”, or including abbreviations like “13in laptop sleeve”, or relying on symbols such as “sleeping bag -5 degrees”.

Slang and abbreviations are by far the easiest to support technically as it essentially just requires mapping between different terms; for example, pairing slang words like “kicks” with “shoes” and “fixie” with “fixed-gear bike”.

Similarly abbreviations must be mapped so, for example, “ml” pairs up with “millilitre” and “HP” with “Hewlett-Packard”.

Symbols can prove a little tricker since they may act as more than mere synonyms and may change meaning depending on the word arrangement of the query.

For example, the “-” symbol could be used to denote both a minus (e.g., “sleeping bag for -10 deg.”) and a range (e.g., “sweaters $50-$100”).

In these instances, not only does the meaning of the symbol change, but the symbol also acts as a filtering instruction.

Users often copy-paste search queries from various sources during activities like research and comparison shopping. Considering that many copy-pasted product titles include symbols (e.g., “Men’s Levi’s® 511™ Slim-Fit Stretch Jeans”), it’s important that search properly interpret these symbols when generating results — or risk giving users the perception that a product simply isn’t available if it doesn’t appear at or near the top of search results.

Yet 49% of sites do not support even the most basic symbol or abbreviation searches for units common to the site (e.g. “13 cubic feet fridge” vs. “13 cu ft fridge”, “3 ounce” vs. “3 oz”, “200 GB” vs. “200 gigabyte”).

Improving Support for the 8 Search Query Types

Ever since our first round of e-commerce search UX benchmarking back in 2014, we’ve observed surprisingly poor support at e-commerce sites for these most common types of search queries.

(Note: Premium subscribers can view our historical benchmark data to see how e-commerce search UX has performed over time for the top e-commerce sites.)


When e-commerce sites with billions of dollars in revenue show this mediocre level of support for essential query types such as Product Type, Thematic, and Symbol searches, you know the current state of e-commerce search UX is still problematic.

However, it’s important to note that the current overall mediocre state of e-commerce search UX shouldn’t be misunderstood as “users cannot use search at all on these sites”.

Yet, it is a clear indication that e-commerce search isn’t nearly as easy to use as it should be and that users’ e-commerce search success and search conversion rates can be improved dramatically on most sites.

The good news is that this also means plenty of opportunity to rise above the competition.

Creating a (comparatively speaking) superior e-commerce search UX only requires proper support of 6–7 query types. In fact, supporting the right handful of the most essential query types is enough to create a decent e-commerce search experience.

Therefore, it’s a good idea to start out by making sure that your search engine supports these 4 essential query types: Exact, Product Type, Feature, and Thematic. Users can get by with basic e-commerce searches when these 4 query types are supported. (Conversely, failing to support any of these core query types will result in a defective e-commerce search UX that can easily lead to abandonments.)

However, to create a truly great e-commerce search UX, all 8 of the search query types must be supported:

  1. “Exact” search queries (42% of sites have issues)
  2. “Product Type” search queries (71% of sites have issues)
  3. “Symptom” search queries (52% of sites have issues)
  4. “Non-Product” search queries (39% of sites have issues)
  5. “Feature” search queries (22% of sites have issues)
  6. “Thematic” search queries (36% of sites have issues)
  7. “Compatibility” search queries (30% of sites have issues)
  8. “Slang, Abbreviation, and Symbol” search queries (49% of sites have issues)

This is not done overnight and often requires more than “simply” investing in good e-commerce search logic — detailed and structured product data is often just as important.

Also, since the same search engine tends to be accessed by all platforms, any investments will typically pay off universally across those platforms — improving the search e-commerce UX of your desktop website, as well as the mobile UX of mobile websites and native mobile apps alike.

Finally, during our testing we observed how participants were greatly influenced by prior experience with the site.

If they had previously had success searching for something on the site, they were much more likely to use search on that site, even if they generally preferred category navigation.

And vice-versa: prior poor search experiences steered otherwise search-happy users towards category navigation.

Thus, not investing in good e-commerce search UX can not only cost sales in the short- and mid-term — it can also set flawed user expectations for future use of the site. And these expectations can be difficult to shake off even if the search experience is eventually improved down the road.

It’s time to improve the state of e-commerce search UX — and that starts by improving the search engine logic with support for the 8 query types.

This article presents the research findings from just a few of the 650+ UX guidelines in Baymard Premium – get full access to learn how to create a “State of the Art” e-commerce user experience.

Note: some eagle-eyed readers may notice that a previous version of this article covered 12 query types. While the other 4 query types that we found during our testing (Relational, Subjective, Implicit, and Natural Language) are still valid, they are much less frequently used by users and are often industry specific. For these reasons, we have omitted them from the article. To find out more about these query types, as well as more about the 8 covered here, you will need to access our Premium research findings.

Authored by Edward Scott on July 14, 2022

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