For shoppers, the search bar is the number one option for finding things on your site. They go there first, expecting powerful, helpful online search. If they don’t find what they’re looking for—or the results are irrelevant—they’re gone, likely to never return. That means more stress for you and your team.
So, how does your search measure up? Compare it with these 12 popular query types to find out:
12 must-have query types for ecommerce search
One of the most common query types and the easiest to technically implement, Exact search requires the most customer knowledge. Users often cut and paste other sites’ product titles directly into the search box.
If a product doesn’t appear in searches for the exact title, name, or ID, the customer assumes you don’t carry the item and leaves your site.
Include multiple title spellings, title translations, international brand and model names, variations with other query types, intelligent handling of misspellings, and secondary product data attributes.
12 must-have query types for ecommerce search
The second-most popular query type, customers often use Product Type searches to easily access a particular category of products, or as a shortcut around category-based navigation.
If users don’t see relevant results when searching by product type, they have difficulty finding those types of products on the site.
Include categories that are and aren’t part of your site’s hierarchy, ideally suggesting them as search scopes. Include all product attributes in the search, and support product type synonyms as categories to guide users to the right place.
12 must-have query types for ecommerce search
Users often know the details of a product they already own but not the name of the accessory or spare part they need. There are two types of Compatibility searches: Brand name and product type (“lenovo laptop adapters”) and specific model (“lenovo x 615 adapter”).
Finding accessories and spare parts for products becomes needlessly difficult when a site doesn’t support Compatibility search.
Support both brand and model searches, since shoppers don’t always know what model they have. Help these searchers easily access compatible products by displaying an option to see accessory products on product listings.
12 must-have query types for ecommerce search
With Symptom search, users can find products by searching based on their problems or a symptom they experienced. They typically adopt this query type when they don’t already know the solution.
Without Symptom search, users who are searching based on awareness of a problem are unable to search for solutions.
Interlink any help content related to the symptom, so the user can learn more about available solutions and their differences, as opposed to simply seeing a product list with solutions they may not fully understand.
12 must-have query types for ecommerce search
Customers often search for other types of content such as help sections, store information, and policies. Non-Product search is a helpful shortcut to the exact content a user is looking for.
Some users expect search to include all content on the site, beyond the product catalog to auxiliary content such as help pages and store information.
Include auxiliary content in your search results. Make it a part of the regular search results list, including products, or take the user directly to the relevant content.
12 must-have query types for ecommerce search
Users often try Feature search after an initial generic search returns overwhelming results. They expect their noted product features to be included in their search results and assume a site’s search results will filter out products without the queried feature.
Many users submit search queries with one or more product features, expecting the site to apply these as filters to their search results.
Filter the search results across one or more product attributes by supporting Feature searches together with other query types. Search engines must intelligently parse product attributes, then detect when to use those features in search queries.
12 must-have query types for ecommerce search
Users sometimes use slang, abbreviations, and symbols in their search queries, which many sites handle poorly.
Many users routinely include slang, abbreviations, and symbols in their queries—with little sensitivity to the site’s failure to deliver on such terms.
Slang is constantly evolving. Mine your search logs to reveal what your users are actually searching for. Enlist employees who are members of different audiences, demographic groups, and subcultures to help add and adjust slang entries to your dictionary and synonym files.
12 must-have query types for ecommerce search
Subjective searches can be broken into three rough categories: Interpretive attributes combine multiple attributes (e.g., “high-quality” or “value for money”). Single-attribute degree relies on a single attribute (e.g., “lightweight” or “cheap”). Taste-based relies on non-quantifiable attributes, such as emotion or experience (e.g., “beautiful tables”).
Users often include subjective adjectives (quality, beauty, value, etc.) in their queries, requiring the search engine to venture past accuracy into interpretation and opinion.
Intelligently treat interpretive attribute mix, single-attribute degree, and taste-based searches. A solid data foundation for these subjective approximations and proxies is crucial. Typically, these approximations become more accurate as more proxy attributes are added.
12 must-have query types for ecommerce search
Relational search helps users find products based on the things they love and follow—from people to companies, publishers, events, or even animals.
Users who only know of a product through other entities involved are only able to search based on those relations.
Combine Relational and Product Type searches, as well as ideally any query type. Improve their usefulness by suggesting product types, displaying contextual search snippet texts, and teaching your search engine associated spellings.
12 must-have query types for ecommerce search
Environmental variables can help infer any implied meanings in a search query. Variables can include past page visits on the site, profile information, purchase history, products in the shopping cart, demographic information, how the user entered the site, duration since last visit, duration of current visit, etc.
Some users submit partial search queries with certain aspects implied.
Use all available environmental data to infer any implied components of the user’s search query, and adjust the search experience accordingly.
12 must-have query types for ecommerce search
Somewhat difficult to define, Thematic search queries are vague in nature and often include fuzzy boundaries (e.g., “living room”) or categories of intended usage (e.g., “spring”/“cold weather”).
Some users search for thematic product categories with ambiguous factors or by intended usage.
Help users find products by intended usage, despite conceptually unclear boundaries.
12 must-have query types for ecommerce search
With Natural Language search, the search engine accounts for typical spoken language, ideally interpreting the meaning of a query and returning highly relevant results beyond simple keyword matching.
Some users type their search queries in full sentences. Many search engines have trouble parsing these advanced queries and returning results.
Deliver a next-generation search experience; Natural Language search enables users to submit questions or requests in regular spoken language.
Consumer expectations for search have never been more demanding. The more you understand your users’ behavioral patterns, the easier you can make their shopping and purchasing experience.
Help them find what they want—and present options beyond what they’re immediately searching—by accounting for the myriad ways they seek out your products.
A robust ecommerce search engine will keep customers on your site, earn their trust to return, and boost your conversion rate.
If They Can’t Find It, They Can’t Buy It is based on the ecommerce search usability research of The Baymard Institute.