Filters for Industries: Explain Industry-Specific Filters (91% Don’t)

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:

  • How industry-specific filters can be ambiguous and reduce the effectiveness of filtering in general
  • Ways to address filter ambiguity to ensure users are able to take advantage of all the available filters

How Filter Ambiguity Limits Their Effectiveness

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.

Here, a user during testing didn’t know what a “Bridge camera” was, so she deselected all other filters, applied the “Bridge” filter, and tried to deduce its meaning this way — only to remove “Bridge cameras” again, because she determined she wasn’t interested in that type of camera at all, and then she reapplied her previous filters.

“Oh, here I see season 1, 2, and 3…but…but I don’t really know what “season 3” is? If I just knew season 1 was for winter, or for spring. Or it might just be that it works for just one season? But what season is it then? I really don’t know this”. At Go Outdoors, the sleeping bag “season” filter caused issues for every single user during testing. It simply wasn’t obvious to them what was meant by the “season” filters. Some didn’t even connect that they related to a sleeping bag’s temperature rating.

“I’m not quite sure what this is?…“Carat” might be okay, but how about “Color”, “Cut”, or “Clarity”?…This might be a place where jewelers themselves go and buy stuff. This is way too difficult for me”. While the filter name “Diamond Color” was clear enough, the color values to choose from, “J I H G F E D”, were nonsense to most of the users during testing. Others were in doubt if an “Ideal” cut was better than a “Very Good” cut.

“I clicked it to see what each name stands for”. A user at ebags clicked “Laptop Attaches & Briefcases” to try to understand what bags would be included in the list by applying this filter. While each name isn’t downright unclear, the selection can be difficult for users not acquainted with the exact details that distinguish all these different types of laptop bags.

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.

What to Do Instead — Avoid or Explain Industry-Specific Filters

Throughout testing, three approaches proved effective in reducing filter ambiguity:

1) Avoid Industry Jargon in the First Place

Instead of a “Season Rating” filter, as shown in an earlier example (which is industry jargon), REI uses the more relatable and common term “Temperature Rating”. As a result, all of the users during testing looking to filter sleeping bags by warmth immediately found the correct filter.

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

Here on the user is guided towards common filter value selections, and is also offered a detailed explanation of the filter with concrete examples (e.g., the typical capacity requirements for a “family of two”).

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.

Help Users Find the Products They’re Looking for with Better Filters UX

“‘Continental’. Not necessarily sure what ‘Continental’ would refer to if I’m honest. So, I wouldn’t be looking for that as a feature”. Unclear filters are often “thrown away” by users if they aren’t immediately understood, as was the case here at Herschel. Another user struggled with “RFID” and, consequently, similarly decided to ignore it: “I don’t know what ‘RFID’ means so, probably a bad call”.

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.

“‘Type of furniture’…I don’t know why it would say ‘table’. I don’t know, this site…it doesn’t feel easy to me”. Mass merchant sites such as Staples need to be extravigilant when it comes to filter names, as their massive product lists will be difficult for users to wade through (especially on mobile) without applying some filters to help narrow the list. Sometimes, it’s not even that jargon is used for a filter name, but rather that the filter name is simply unclear when considered in the context of a particular product type (e.g., the relationship between “Office Chairs” and “Type of Furniture”).

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 650+ UX guidelines in Baymard Premium – get full access to learn how to create a “State of the Art” e-commerce user experience.

Authored by Edward Scott on December 11, 2018

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