Ecommerce Search UX — 8 Query Types
Source: https://baymard.com/blog/ecommerce-search-query-types Author: Baymard Institute
Baymard’s taxonomy of the query types users enter into e-commerce search, derived from large-scale usability testing across desktop sites, mobile sites and apps. Each type is reported with the share of benchmarked sites that fail to support it — the house style described in Baymard Institute. The reference taxonomy for Query Types in e-commerce.
The 8 Query Types
Percentages are the share of benchmarked sites with issues supporting that type.
| # | Query type | Sites with issues |
|---|---|---|
| 1 | ”Exact” searches | 12% |
| 2 | ”Product Type” searches | 20% |
| 3 | ”Feature” searches | 39% |
| 4 | ”Use Case” searches | 43% |
| 5 | ”Abbreviation and Symbol” searches | 54% |
| 6 | ”Compatibility” searches | 44% |
| 7 | ”Symptom” searches | 37% |
| 8 | ”Non-Product” searches | 66% |
The ordering is informative on its own: the types sites handle worst are the ones furthest from a catalog’s own vocabulary. Exact and product-type searches map onto a category tree directly and are largely solved. Non-product, abbreviation/symbol and use-case searches have no natural node to map to, and two thirds of sites fail the worst of them.
One worked example from the article: a “Product Type” search for “chairs” at CB2 returns a search results page rather than a category page, but includes “Related Searches” that narrow 522 items down to a specific chair category, with no irrelevant items in the list.
Benchmark Context
The article summarises 10,000+ usability scores across the 5 search topics that make up the e-commerce search experience, plotted over 170+ benchmarked sites and apps.
- Only 44% of desktop and mobile sites and apps achieve a “decent” or “good” overall Search UX performance.
- 46% of desktop, 58% of mobile and 64% of app experiences are “mediocre or worse”.
- A handful of exceptions exist — 4 desktop sites and 1 mobile site scored “perfect” — so the ceiling is reachable.
Baymard’s stated framing is that abandonment is rarely caused by one severe issue; it accumulates from medium-level issues encountered across several searches.
Why This Matters for Classification
A query-type taxonomy is a label set, and this one is defined by observed user behaviour rather than by catalog structure. That is the useful property: it names the demand a catalog-derived taxonomy cannot express. Query types that no category node covers are exactly the ones that end up in a classifier’s residual bucket — see Query Classification.
Related Concepts
- Query Types — the concept note this article anchors
- Query Understanding
- Search Intent
- Zero Results — where unsupported query types surface
- Faceted Search
Related Articles
- Targeting Broad Queries in Search
- Metadata - The 3rd Kind of Retrieval
- 13 Design Patterns for Autocomplete Suggestions — the other Baymard study in this vault