ABOUT SEARCHING AND SEARCH PRACTICIES

The below 300+ e-commerce UX articles are all based on Baymard’s 130,000+ hours of UX research. The articles represent just 5% of Baymard’s Premium research database. Join 37,000+ UX professionals and get a new article every 2nd week by email.

Search autocomplete features are provided at 80% of e-commerce sites, yet only 19% achieve the highest performance. See our latest large-scale test findings for on-site search.

Our testing reveal that it's often best to autodirect or guide users who search for a product type (e.g. "mens shirt" "led TVs") to the category scopes, yet 46% of e-commerce sites don't have this feature..

We find that on e-commerce sites up to 5-12% of all search queries include a color. Yet, 54% of sites display the same static product thumbnails regardless of what color the user searched for.

Our large-scale usability testing reveals there are many E-Commerce UX improvements that can be made that are COVID-19 related. See 6 issues and solutions here.

The backbone of a good search experience is handling the queries that a user throws at it. See our latest test findings and if your site is able to handle the 8 most common search query types.

Our usability benchmark of 50 major e-commerce sites show that Macy's filtering design is among the highest performing. In this article we share our test findings on why that is by exploring 7 concrete filtering implementations.

Our usability testing reveals 6 essential elements for a "No Results" page that help users get back on track with product finding — yet 45% of e-commerce sites don't include at least half of these. See our latest test findings on "No Results" pages.

Our usability testing reveals that as many as 34% of users try to search for non-product content on e-commerce sites — yet 15% of e-commerce sites don't support it at all. See our latest test findings on user behavior relating to non-product search.

Semantic search is a set of information retrieval techniques to deal with complexities and ambiguities of human language. Online retailers have to deal with those complexities in product discovery to boost relevance and drive conversion

In this article, we'll track the history of retail web search from its roots in general-purpose full text search engines to deep learning semantic vector search

In this blog post, we describe a practical blueprint of building a concept-oriented query parser based on open source search engines, such as Solr or Elasticsearch.

This article describes semantic vector search, the next major step in the quest to provide relevant and timely search results.

This article describes the best practices to build a smart autocomplete feature to increase user engagement by producing relevant listing results for retailers

At Kensho, we often build tools to help analysts make sense of financial information. Much of this information is expressed in natural…

Framing the Hurdles to Relevant Ecommerce Search Every e-commerce shop owner wants to achieve ecommerce search relevance with the search results on their website. But what does that really mean? As the German singer Herbert Grönemeyer once stated: “It could all be so easy, but it isn’t”. It could all be so easy, but it […]

Why Abstraction is Necessary for Modern Web Architecture Why abstraction, and why should I reconsider my web-shop architecture? In the next few minutes, I will attempt to lay clear the increase in architectural flexibility, and the associated profit gains. This is especially true when abstraction is considered foundational rather than cosmetic, operational, or even departmental. […]

Almost any person working with search is somehow aware of Synonyms and their importance when optimizing search to improve recall. Therefore, it will be no surprise to say that adding synonyms is one of the most essential methods of introducing domain-specific knowledge into any symbolic-based search engine.

One of the biggest causes of website failure is when users simply can’t find stuff on your website. The first law of e-commerce states, “if the user can’t find the product, the user can’t buy the product.” Why is Measuring and Continuously Improving Site-Search so Tricky? This sentence seems obvious and sounds straightforward. However, what […]

Series: Three Pillars of Search Quality in eCommerce In the first part of our series, we learned about Search Quality dimensions. We then introduced the Findability metric, and explained the relationship of this metric on search quality. This metric is helpful when considering how well your search engine handles the information retrieval step. Unfortunately, it […]