In our large-scale usability testing of product lists and filters, we observe that filters can turn an overwhelming and unmanageable product list into one that’s much more focused on products relevant to the user — increasing the likelihood of a user finding a suitable product to purchase.
We observe that this is even more prevalent when testing mobile websites, where the screen real estate limits users’ overview — making it even more important that users are able to use filters to get a manageable list.
However, if users don’t fully understand the filter types and values, it can be as damaging as not having these filters in the first place.
In spite of this, our benchmark of filtering interfaces reveals that 91% of sites that show jargon-heavy, industry-specific filters don’t explain them further to their users, with the result being that users often fail to apply filters correctly, as they either ignore the filtering type altogether or apply overly strict or loose filtering values. During testing, we observe that this was the direct as well as indirect cause of abandonments.
In this article, we’ll discuss the test findings from our Product Lists & Filtering usability study related to explaining industry-specific filters, including:
When creating filter types and values, it can, as an industry expert, be difficult to step back and consider how they’ll be understood by a general user.
Indeed, our benchmarking reveals that most sites must simply assume users have a certain amount of domain knowledge for a particular product type, when considering some of the filters users were presented with in testing.
However, this is a mistake, as most users likely won’t have the required domain knowledge to fully and accurately judge the difference between, for example, a TV refresh rate of 60 HZ vs. 120 HZ vs. 240 HZ.
Even users who have a decent understanding of what the attribute is will often benefit from a more exact definition, along with guidance on what values to look for, in order to make an accurate and informed filtering decision. For example, “While a ‘240 HZ’ refresh rate is logically better than ‘120 HZ’, how much better is it? And how common is 240 HZ even?”
Even those users who may have researched the product beforehand will undoubtedly be tripped up by some industry-specific filters, as obtaining an encyclopedic knowledge of a particular product type — especially when brand-specific filters are provided as well (e.g., a “destruction” filter for jeans) — is simply impractical.
Furthermore, users who are stymied by a particular filter type or filter option may decide to go offsite to research it (“I’ll just Google it”) — which of course introduces the possibility that they won’t return.
The end result of having ambiguous filter types and values (both of which can cause understandability issues for users) is that users are less likely to apply the filters, making it less likely that they’ll get a suitable product list — and less likely that they’ll find a suitable product to purchase.
Throughout testing, three approaches proved effective in reducing filter ambiguity:
1) Avoid industry jargon in the first place. This is often the best solution, although also more resource intensive to implement. Rather than industry jargon, instead use filters that more closely match the attribute or common terms that users are more likely to look for and to understand (e.g., “temperature” instead of “season rating”).
2) Offer explanations for industry-specific or ambiguous filters. Sometimes industry jargon is the only option available, or the jargon is of significant value to expert users. If that’s the case then the jargon should be kept (e.g., “diamond clarity”).
However, be sure to then explain the industry jargon further. For example, “’Diamond clarity’ describes the amount and type of visible defects. I1-3 contains visible defects, SI1-3 contains no defects visible to the naked eye, and VS1-2 contains almost no defects when seen under microscope” or “A ‘Bridge Camera’ is in-between a compact camera and a DSLR camera in both size and features, often with great zoom and advanced settings, but without an interchangeable lens system”.
Such explanations may be present at the filter-type level, or in some cases for the filtering values themselves, and are typically best represented in a tooltip (to avoid excessive interface text).
3) Provide visual examples for visual filters. If the filtering values are mainly visually driven, consider displaying the actual differences using thumbnails rather than describing them using text. For example, the tooltip for laptop bag types can show examples of each type of bag.
Judging which filters are ambiguous to users with little or only moderate domain knowledge can be an almost impossible task for industry insiders who have often seen and used the terms for years.
There’s a tendency to overestimate users’ ability to understand jargon, and it’s important to note that, just because users recognize a term, it does not mean that they are able to make an informed filtering decision based on it (i.e., choosing one filtering value over another and fully understanding the implications of doing so).
In practice, the filters most in need of further explanation are best identified by running a few tests with novice users, or with people outside or new to the site’s organization and industry.
Avoiding jargon or explaining industry-specific filters will help turn filtering from what is often an underutilized resource into something that allows users to create powerful filter combinations to get highly relevant product lists. And yet we find that 91% of sites don’t explain their industry-specific filters at all.
This article presents the research findings from just 1 of the 580+ UX guidelines in Baymard Premium – get full access to learn how to create a “State of the Art” user experience for product lists, filtering and sorting.
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For a site devoted to UI, its a shame that you do not associate article preview or share images with your articles. When I share your posts with my dev team in slack, no article preview appears and no share image as you would expect. this leads to near zero interaction with the article, despite its valuable content.
lol. I had to check their source code to double check, and you’re right!
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