Trying to determine the "best" bolding pattern for predictive results in the search dropdown on an e-commerce site.

Option 1: bolding the search query as the user types it in

Option 2: bolding the predictive results (everything but the user's query)

Is there a way to test which method is more effective? Or is it purely subjective?


2 Answers 2


I don't necessarily agree with the Google/Bing answer.

Search is a tool, not a use case. Expectations of tools and patterns are wideley different depending on the use case.

Google/Bing search is a use case where you have an open ended querry and the bold font communicate clearly what you could add. Because open ended querries often are exploratory, and you can search "anything", it is more important for Google to suggest your phrasing to be able to handle the input better. This is in fact a feature more important for them than you.

But! Searching for specific objects, it is not necessarily the suggestion that is most important (bolded and visually prominent). Over the years, designing both e-commerce search and desktop software tools, many users state that they want to know as soon as possible if what they search for exists and match. That is something very different from Google/Bing.

While Google/Bing is an open-ended querry, searching for specific objects could be quite the opposite.

There is no right or wrong here, I just want to advise against seeing all search interactions as the same use case and then compare it with Google. That is very unlikely to be applicable to scenarios in different types of experiences.

My source? Years of testing. Both solutions work, depending on what kind of experience you're designing for. The only certain answer I can give you is that search is more than Google. If search worked exactly as Google when I browse Taco bell restaurants on their website, that would probably be extremely confusing. :)

TL;DR Are you helping the user phrase their search better to explore an extreme amount of possible roads that could lead to a relevant finding, or is the search deterministic and should let the user as soon as possible when they found what they were looking for?

  • Ok, so don't compare it with Google search, but what about Amazon? They do practically the same. I do agree on that it depends on what kind of e-commerce site it is, as Amazon is big enough to have a search that works like Google, smaller webshops probably benefit more from precise suggestions instead of generic predictions.
    – jazZRo
    Commented Jul 9, 2020 at 14:44
  • My point is that search is not a generic interaction pattern with a predefined set of user expectations. On Google, you very rarely know what the best querry or "answer" will be. There may not even always be an answer, but instead an open ended user journey with no definite "answer". On e-commerce, users instead often have a much stricter plan and expectation on what to find, and the context is rarely about optimizing your input for maximizing a broad enough querry to find interesting answers. You just want to know as soon as possible if whay you want exists or not.
    – CatMeow
    Commented Jul 10, 2020 at 2:53
  • @CatMeow great points, but how do different bolding patterns play into those two different search paradigms? It seems like you're talking more about the algorithms these companies use and less about how they bold things? Commented Jul 10, 2020 at 19:59
  • Use case and problem statement should drive the design solution, not the other way around. What I'm trying to shine light on is that it is potentially counterproductive to try forcing predictive search/bolding, etc., on this when what the user needs is something else. If the users want to understand as quickly as possible when they found a matching item, is predictive search or bolding the way to go, from the beginning?
    – CatMeow
    Commented Jul 13, 2020 at 9:01
  • This happens all the time in UX. Borrowing industry standard solutions is a good thing to correspond with past experience and user expectations, but Jacob's Law is often used without caution. It only applies as long as the problem scope, user target group, contextual settings, etc. match. Otherwise, it is not longer helpful. I'm, not questioning the research and effort that has gone in to your work, I'm just curious about alternative approaches. What will the bolded predictive search solve to begin with? :)
    – CatMeow
    Commented Jul 13, 2020 at 9:06

Since most people come to your website with experience of ubiquitous design patterns, you can build on what they are used to.

Jacob's Law is a useful usability heuristic:

Users spend most of their time on other sites. This means that users prefer your site to work the same way as all the other sites they already know.

So unless you have tested and are confident that you have a better solution, you can piggyback on well known patterns:

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  • Thanks Mike. Makes sense. Any thoughts on how to test it, though? Commented Jul 8, 2020 at 16:38
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    I would probably just use as is, since Bing and Google account for the overwhelming share of search, and invest my resources on other areas of improvement. But...you could run a test asking users to select a specific predictive result, and test how fast (and accurately) they make selections.
    – Mike M
    Commented Jul 8, 2020 at 16:46
  • It's somewhat interesting to see how your Google, Bing, and Amazon images show about the same increase in font-weight, then Bed, Bath and Beyond really seems to turn it up a lot. I'd be curious to see how much of an increase is "the right amount". The suggestions by G, B, and A appear more "cohesive". However, with BBB, I find my eyes completely skipping over the cuttin text, and I can't tell if that's helping me to quickly scan my results, harming me by distractingly breaking up the text, or having little effect either way. Commented Jul 10, 2020 at 12:59
  • @maxathousand guessing it's based on what font weight each company decides to load into their design system. Everyone's looks like the pattern is "regular > bold" but BBB kinda looks like "light > bold" Commented Jul 10, 2020 at 19:56

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