In this article, you’ll find 8 types of search queries identified during Baymard Insititute’s large-scale usability study. Their usability testing reveals that these 8 types of queries represent the main principles behind how users think of and construct their search queries when searching in an e-commerce context.
During the usability studies, beyond the common keyword search for a specific product, users were observed to rely heavily on search queries that included a product type, a theme, or a feature . Yet surprisingly, 3 rounds of search UX benchmarking of 60 of the world’s largest sites in 2014, 2017 and late 2019, reveal a still surprisingly poor support at e-commerce sites for these query types – as is evident in the graph below (late-2019 results).
An analysis of the more than 8,400 manually set UX performance ratings reveal a surprisingly weak support for essential e-commerce search query types, with 61% of all sites performing below an acceptable search performance that will directly misalign with user’s actual search behavior and expectations. To make matters worse, 15% of sites were found to have a downright “broken” search query type performance.
Most of the site improvements observed since the first benchmark in 2014 have largely been offset by users’ increasing expectations for what type of search queries they can perform online (expectations likely carried over from their general web and social media search, that generally tends to be a lot more capable).
While there has been a steady improvement in some of the search query types over the past 5 years, there are still fundamental search UX issues. For example, among 60 top grossing US and European e-commerce sites in late-2019:
- 61% 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..
- 46% of sites don’t support thematic search queries such as “spring jacket” or “office chair”.
- 32% of sites don’t support symbols and abbreviations for even the most basic units, resulting in users missing out on perfectly relevant products if searching for inch when the site has used “ or in in their product data.
- 27% of sites won’t yield useful results if users misspell just a single character in a product title.
- 25% of sites don’t support non-product search queries, like “returns” or “order tracking”.
This article will cover the UX research findings for each of the 8 query types most common for e-commerce search — showing you the observed user behavior, where and how it causes UX issues for e-commerce search, query samples, and the principled needed for how to best support each query type.
For each query type, there’s one example to illustrate it. For many more examples, see the full article (link at the bottom).
Deconstructing the Search Query: Spectrum, Qualifiers, and Structure
When users search in an e-commerce context, they are mostly looking for products. This prompts users to search differently than they do when performing generic web searches. In particular, users will often include one or more criteria in their search which the product must meet.
Users will commonly combine the 8 query types when devising their search queries, for example, searching for Sleeves 11” where sleeve is a type of product and 11” is the feature of the product (size).
Keep these different functional aspects of the 8 query types in mind as you read through each of them below, as it will help you better understand how users approach search on e-commerce sites. By deconstructing the functional aspect of each query type, its role in the user’s query as a whole becomes clearer, which can be immensely helpful when looking to design search logic and interfaces that align with user behavior and expectations.
#1. Exact Searches
At Costco, an “Exact Search” for the model number of a mixer is unsuccessful, even though the product is sold at the site and findable via browsing. The model number data is even shown on the product details page, so it’s not because Costco doesn’t have the data – it’s because it’s not utilized by the search engine. This is highly problematic for all those users who copy-paste product titles and model numbers from external sites (e.g. review or manufacturer sites).
When users know the exact product they are looking for, they will typically rely on #1 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.
Great support for #1 Exact Searches proved crucial during testing, as the users were observed to quickly conclude that a site didn’t carry the product if it didn’t show up for a request as specific as a model name or number (as opposed to more open-ended queries, such as #6 Thematic Searches).
Looking at the benchmarked top 60 grossing US and European e-commerce sites, 29% of sites are incapable of producing decent results for #1 Exact Searches as of late-2019. More specifically, 27% of sites are incapable of handling misspelling of just a single character in the product title, and 2% don’t allow searching on model names (as seen at Costco).
#2. Product Type Searches
At B&H Photo, a search for “multifunction printers” displays 65 results, while a query for “all-in-one printer” shows 347 results. When users search for product types that aren’t an exact match for a site’s category labeling, only a fraction of the results display, which presents a missed opportunity, since users aren’t presented with as many relevant (nor full-breadth) results.
When users aren’t looking for a specific product but rather a type of product, they will rely on #2 Product Type Searches, querying for a whole category of products, such as “Sandals”.
When used on their own, product type searches 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. During a recent mobile testing, 60% of mobile users turned to search as their first product-exploration strategy when landing on the homepage of a new site.
When the site can be sure of a 1:1 match with a product type search 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 #2 Product Type Searches 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 labelling of products, but also proper handling of synonyms and alternate spellings of those groupings.
Users will not necessarily know what specific product type the site uses to identify a product. At H&M synonyms like “maternity” and “pregnant” have been mapped, meaning that users who search for “dress pregnant” will also see all the “dress maternity” results.
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 users may type “multifunction printer” or even “copy machine” when looking 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 users never thought of trying another synonym when they received poor search results but instead simply assumed that the poor or limited results for a search such as “copy machine” meant that was then the site’s full selection for such products.
Despite the severe impact on the user’s search experience 61% of major e-commerce sites do not return all the relevant results, if any at all, when users search by a product type or synonym, e.g., “blow dryer” instead of “hair dryer”. This is virtually no change from 2014 that found 64% and 2017 that found 61% of sites not returning all relevant results when searching by common product type synonyms.
The Product Type query is largely a missed opportunity within the industry and should always be one of the first things considered in any search UX improvement project due to the severe combination of Product Type searches being frequently used by users, the likelihood of users getting stuck and ultimately abandoning if synonyms aren’t well supported, with the fact that 61% of e-commerce sites currently don’t have proper synonym support.
A few key types of synonyms to consider when auditing or trying to improve a site’s Product Type search capabilities are:
- 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”
#3. Symptom Searches
At Home Depot, a search for “drafty window” returns many results for entirely new windows, failing to address the user issue indicated in this “Symptom” search.
The same symptom query at Amazon for “drafty window” presents dozens of potential solutions (e.g., “window insulator kits”, “window shrink film”, “foam insulation tape”, “cold blocking drapes”, etc.) giving users a range of options to help resolve the problem.
When users know the specific product they are looking for, they will use Exact search, 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 searches.
However, sometimes users don’t even know the type of product they are looking for — all they know is the problem which they are experiencing and that they want a solution to it. In these cases, they will rely on #3 Symptom Searches, entering the problem they are experiencing, such as “stained rug” or “dry cough”, in hopes of being presented with viable solutions and products to this problem.
Symptom search is important because it 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.
Only 25% of sites within industries where Symptom Searches are even relevant, actually have decent support for it. On most sites, searching for a symptom like “knee pain” will return any products related to the keyword “knee”, and predominantly show irrelevant “knee” related products first, with the actual solutions to the “knee pain” symptom scattered throughout the following hundreds of partial matches – in practice rendering it impossible for users to get an overview of their relevant options.
#4. Non-Product Searches
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.
Non-Product Searches are searches where the user is searching 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. 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-ecommerce website. During a usability testing, 34% of users tried to search for non-product content (e.g., “return policy”, “unsubscribe”, “cancel my order”, etc.).
#5. Feature Searches
On Sephora, a search for ”$30 shampoo” automatically applies a $25–35 price range filter to the search results. Applying an ever-so-slightly relaxed price range is sensible as the $30 figure can arguably be interpreted as a price indicator — a user asking for shampoos that cost $30 will likely consider a shampoo that costs $29.50 or $30.5 to be a good match.
Users often have a set of criteria that they want the product to meet, and very often these criteria relate to features of the product. #5 Feature Searches is when the user includes one or more features in their search query that they want the product to have. 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”.
The term “Features” should be understood in a rather broad sense referring to any type of product aspect or attribute. Features can thus be the product’s color (“red dresses”), material (“fabric sofas”), performance specs (“100000 IOPS hard drive”), or format (“Hobbit DVD”), not to mention price (“$100-$200 backpacks”), brand (“Revlon lipstick”), or size (”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.
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 carries over to e-commerce search.
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), as 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.
During testing, #5 Feature Searches were the by far most common query qualifier, making it a definite “must have”. Among the top e-commerce sites, 86% support that users search by a wide range of features (e.g. color, material, brand), which is a great improvement from the 46% seen in 2014 and 73% in 2017.
#6. Thematic Searches
Thematic queries aren’t supported well at Marks & Spencer, where e.g. a query like “retro dress” return the same table set in 4 colors, and show none of the more than 180 dresses sold at the site.
What exactly constitutes a “living room rug”, an “extreme weather sleeping bag”, or a “retro dress” ? While these are certainly concepts we can easily recognize, defining their exact boundaries can be a challenge.
Thematic Searches are often a little difficult to define because they are inherently vague in nature — they often include fuzzy boundaries of 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”), or even promotional attributes (“sale lipsticks”). Nevertheless, they are very real notions to users who — in certain industries, such as apparel and furniture — 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, as it’s vital that a query for e.g. “spring jacket” presents all the relevant products, not just the handful of products which happen to have those keywords in their title or description.
When “Thematic” search queries aren’t supported, users are left with absent, few, or irrelevant results, which costs users extra time to rethink (and retype) query wording, and sometimes gives the impression that the products sought simply aren’t available at the site. In fact, 46% 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 Searches
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 Searches 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 cybershot camera case”.
Compatibility search requires strict compliance — it’s two or more products that must work together, with the user trying to find products that are specifically compatible with a product they already own (or are about to buy).
At B&H, a compatibility search for “hp ink” offers faceted compatability filters to narrow by “Hewlett-Packard Model” , helping users restrict results only to accessories compatible with their specific printer model.
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.
Generic compatibility queries are among the most supported query types, supported by 70% in late 2019.
#8. Slang, Abbreviation, and Symbol Searches
Users rely on a wide range of linguistic shortcuts when they search: typing “RayBan shades”, including abbreviations like “13in laptop sleeve”, or relying on symbols such as “sleeping bag -5 degrees”.
A slang search for “shades for men” at Kohl’s yields many irrelevant results including sun shades, pants, polo shirts, and even t-shirts with sunglass prints, on the first page of results.
At Amazon, the same slang search for “shades for men” is well mapped to sunglasses, offering over 30 men’s and unisex sunglasses on the first page of results.
Slang and abbreviations are by far the easiest to support technically as it essentially just requires mapping between different terms, pairing slang words like “kicks” with “shoes” and “fixie” with “fixed-gear bike”; similarly abbreviations must be mapped so “ml” pairs up with “millilitre” and “HP” with “Hewlett-Packard”.
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.
33% 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 Query Types
The current overall lacking state of e-commerce search shouldn’t be understood as “users cannot use search at all on these sites”. However, it is a clear indication that e-commerce search isn’t nearly as easy to use as it should be and that users’ search success and search conversion rates can be improved dramatically on most sites – even when looking at these 60 major e-commerce sites.
The good news is that this also means plenty of opportunity to rise above the competition. Creating a (comparatively-speaking) superior search experience 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 search experience.
For best results, start out by making sure that your search engine supports these 4 essential query types: #1 Exact, #2 Product Type, #5 Feature, and #6 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 search experience.
Testing revealed that users 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 search usability can not only cost sales in the short- and mid-term, it can also set flawed user expectations for future use of your site. Expectations which can be difficult to shake off even if the search experience is eventually improved down the road.
A summary of all the queries:
I) Query Spectrum – Used to indicate the range of what should be searched
#1. Exact Search (“Keurig K45”) – Searching for specific products by title
#2. Product Type Search (“Sandals”) – Searching for groups or whole categories of products
#3. Symptom Search (“Stained rug”) – Searching for products by querying for the problem they must solve
#4. Non-Product Search (“Return policy”) – Searching for help pages, company information, and other non-product pages
II) Query Qualifiers – Used to delimit the Query Spectrum, specifying conditions for what should and/or shouldn’t be included
#5. Feature Search (“Waterproof cameras”) – Searching for products with specific attributes or features
#6. Thematic Search (“Living room rug”) – Searching for categories or concepts that are vague in nature or have “fuzzy” boundaries
#7. Compatibility Search (“Lenses for Nikon D7000”) – Searching for products by their compatibility with another item
III) Query Structure – How the query is constructed by the user and interpreted by the search engine
#8. Slang, Abbreviation, and Symbol Search (“Sleeping bag -10 deg.”) – Searching for products using various linguistic shortcuts
PS. Whatever you do, don’t show your users an empty results screen. If you can’t figure out the query, show at least something. It could be a list of categories, or a refined query suggestion, or your best sellers. Never an empty screen.