Does Adding Availability Numbers To Advanced Filters For Ecommerce Help

29 min read
UX Pattern, Navigation, Due east-Commerce, Usability, UX

Quick summary ↬ When done right, filters enable users to narrow down a website's choice of thousands of products to merely those few items that match their particular needs and interests. Withal, despite it beingness a central aspect of the user's e-commerce product browsing, virtually websites offer a lacklustre filtering experience. In fact, our 2015 criterion reveals that only xvi% of major east-commerce websites offer a reasonably skillful filtering experience. Given the importance of filtering, nosotros — the entire squad at the Baymard Establish — spent the last nine months researching how users browse, filter and evaluate products in eastward-commerce production lists. We examined both search- and category-based product lists. At the core of this research was a large-scale usability report testing nineteen leading e-commerce websites with existent end users, following the recollect-aloud protocol.

When washed right, filters enable users to narrow downwards a website's selection of thousands of products to simply those few items that match their detail needs and interests. Yet, despite it being a key aspect of the user's e-commerce product browsing, most websites offer a lacklustre filtering experience. In fact, our 2015 benchmark reveals that only 16% of major e-commerce websites offer a reasonably practiced filtering experience.

Given the importance of filtering, we — the entire team at the Baymard Institute — spent the concluding ix months researching how users browse, filter and evaluate products in east-commerce product lists. We examined both search- and category-based product lists. At the core of this enquiry was a large-scale usability report testing 19 leading e-commerce websites with real end users, following the retrieve-aloud protocol.

Farther Reading on SmashingMag:

The Current State Of E-Commerce Search
Responsive Upscaling: Large-Screen East-Commerce Design
Creating A Client-Side Shopping Cart
UI Patterns For Mobile Apps: Search, Sort And Filter

Despite testing multi-1000000 dollar websites, the test subjects ran into more than 700 usability problems related to production lists, filtering and sorting. All of these issues take been analyzed and distilled into 93 curtailed guidelines on product list usability, 35 of which are specific to filtering availability, pattern and logic.

More after jump! Continue reading below ↓

We subsequently benchmarked 50 major The states e-commerce websites across these 93 guidelines to rank the websites and learn how major e-commerce websites blueprint and implement their filtering and sorting features. This has led to a criterion database with more than than iv,500 benchmark data points on e-commerce production listing pattern and performance, of which ane,750 are specific to the filtering experience. (You can view the websites' rankings and implementations in the publicly available part of the product lists and filtering benchmark database).

In this article we'll accept a closer look at some of the enquiry findings related to the users' filtering feel. More specifically, we'll delve into the post-obit insights:

Only 16% of major due east-commerce websites provide users with a reasonably good filtering experience. This is often due to a lack of important filtering options, simply from the benchmark data it's clear that poor filtering logic and interfaces are also causal problems.
42% of elevation east-commerce websites lack category-specific filter types for several of their core product categories.
20% of top e-commerce websites lack thematic filters, despite selling products with obvious thematic attributes (season, style, etc).
Of those websites that deal with compatibility-dependent products, 32% lack compatibility filters (for example, selling smartphone cases without a filter for device blazon or size).
Testing showed that x+ filtering values crave truncation — yet 32% of websites either take bereft truncation blueprint, causing users to overlook the truncated values (six%) or utilise what testing establish to be even more troublesome, inline scrollable areas (24%).
Just xvi% of websites actively promote important filters on top of the product list (a prerequisite when relying more than on filters than on categories).
Filtering functioning varies greatly past industry, with electronics and apparel websites generally suffering from bereft filters (for each of their unique contexts), while hardware websites and mass merchants accept the lead in the filtering game.

In this commodity we'll walk through each of these vii filtering insights, showing you the usability test findings, examining the criterion data and presenting best practice examples for creating a good e-commerce filtering feel.

1. Just 16% Of Websites Provide A Good Filtering Experience

When washed right, filters enable users to see only the products that match their private needs and interests, such as products of a particular type or manner or with certain features or attributes. For instance, a user might want to see all products in the "jackets" category for "men" (gender filter), for the "winter" season (thematic filter) and available in the color "blackness" and size "G" (variation filter). It's the e-commerce equivalent of walking into a physical shop and asking a salesperson for "a black, men'southward, winter jacket in size medium."

All the same, a prerequisite to these wonderful powers of filtering is having a vast range of filters available for the user to drill into the particular features and production aspects that are of import to them and their particular interests. Most e-commerce websites already fall brusque here. However, a expert filtering experience requires the necessarily filters not just to be present, only to exist presented in a way that'south piece of cake for the user to grasp and interact with and whose logic follows user expectations.

Benchmarking the 50 peak-grossing US due east-commerce websites across the 93 product list guidelines identified in the usability study revealed generally mediocre performance. Analyzing the i,750 functioning scores specific to filtering availability, filtering logic and filtering interfaces reveals that:

34% of websites have a poor filtering feel, severely limiting their users' ability to browse products — fifty-fifty when they take the most bones of product requirements;
50% of websites offer a passable filtering experience — by no ways practiced and with several areas that could be improved;
simply 16% of websites provide a good filtering experience, with sufficient filtering types available, a counterbalanced filtering design and a filtering logic that aligns well with user expectations (although, even among these few good websites, most still accept room for refinement).

In sections ii, 3 and 4 in this article, we'll walk through the exam findings for three of the core filtering types that typically cause issues: category-specific filters, compatibility filters and thematic filters — considering 60% of major e-commerce websites lack 1 or more of these.

During testing, the filtering logic and filtering user interface ofttimes led to a poor experience, even on websites that accept invested resource in product tagging (i.e. filter availability). Users need to exist able to locate and use relevant filtering values and to make their desired filtering combinations in order to describe value from a website's filters. Notwithstanding a notable 40% of test subjects were at some indicate during testing unable to detect a website's filtering options — despite actively looking for them. This is disquisitional, because that unnoticed filters are — to the user — effectively the aforementioned equally nonexistent filters. In section 5 and 6, then, nosotros'll walk through two filtering design patterns that proved effective at solving some of these user interface issues.

2. 42% Lack Category-Specific Filter Types

Nigh of the fourth dimension, users are interested in filtering a product list beyond category-specific attributes, and not but the website-wide attributes (such as make, price, user ratings, etc.). An example would exist filtering a list of cameras by photographic camera-specific attributes, such as megapixels, zoom level and lens mount — attributes that aren't particularly meaningful for other types of electronics, such as TVs.

For instance, sleeping numberless would need a temperature rating filter, while piece of furniture would need a color filter, and hard drives a chapters filter, and so on. A massive 42% of summit e-commerce websites lack such category-specific filtering types for several of their cadre product verticals.

A good rule of thumb is that any product specification that is important enough to exist shown in a production list item should also be available equally a filter. Moreover, past virtue of displaying the data in front of the user, the website is reminding the user that that specification is important (or, in the case of users new to the domain, teaching them that it is). The very display of the specification, and so, encourages users to filter by it.

Notice how Williams-Sonoma displays the capacity of its food processors (measured in cups) — reminding users that this is an important metric — but then offers no way to filter the food processors by capacity. (View big version)

Aureate states the material for well-nigh jacket types, but without a materials filter. Users who are interested in wool jackets would have to get through all 295 jackets. (View large version)

Staples lists the printing speed of the majority of its printers but does not allow users to filter its 2409 printers by press speed. (View large version)

During testing, when users encountered websites that lack basic category-specific filtering, they would give up because they realized they would have to manually locate the items they want by browsing a generic production list containing hundreds of items (for example, to detect jackets made of wool, food processors with capacities greater than 14 cups, etc.). Users oftentimes took quite a while to fully grasp that a website doesn't offer such filters, with well-nigh simply bold that "Information technology must exist at that place somewhere," and not believing that the website could neglect such basics — and being forced to expect through hundreds of products.

When a product listing is a set of search results, faceted search should nowadays the user with the all-time-matching product-specific filters, without the user having to specify a category. We touched on our examination findings and the topic of faceted search (and how only 40% of superlative websites offering this) in section 6 in "The Current State of East-Commerce Search."

Primal Takeaway

E'er ensure that each category has a unique ready of filters specific to the blazon of production. At a minimum, the product specifications included in the list items will need to exist available as filters as well, just a wider array of filters will well-nigh always be needed.

3. xx% Lack Thematic Filters

Thematic browsing patterns are quite common in concrete retail stores, where any sales banana would exist able to assist visitors with common requests, such every bit "a casual shirt," "a spring jacket," "a high-terminate pocket camera" or "an LED TV with practiced value for the money." Yet, this is no piece of cake task on most due east-commerce websites.

While TVs, cameras, jackets and shirts tin can all exist easily located on most east-commerce websites, viewing products that match a certain "theme" tin can exist nearly impossible. Despite such thematic attributes often existence both common and central aspects of the user's purchasing decision, our benchmarking revealed that twenty% of top due east-commerce websites still lack thematic filters (although support for it has grown to 66%, upwards from 48% since our last study and benchmarking of eastward-commerce search).

"I'm too impatient for this kind of thing. They would have lost me. If at that place were multiple pages, I would never take gotten through it," one subject explained as he looked for a jacket for the spring season on Gilt. "Commonly y'all can choose betwixt winter jackets, spring jackets or the type of jacket." He ended up abandoning the website.

"I'd look at these to run into what the fashion is similar. And then I'd think, 'Ayhh, these are all ugly.' And then, I go upwardly once more, to meet if I tin can sort a little [filter, ed.], by 'manner' or something," one subject explained while she looked for a way to filter by mode. With only a "pillow type" filter available on Pottery Barn, she had few options to try and concluded up applying a random pillow blazon to come across where that would take her — hardly a reliable way for users to notice relevant items on a website.

Macy'due south offers a thematic "style" filter, which concluded upwards being used past 60% of test subjects. Above, one subject is seen applying a "Coat Style: Casual" filter. (View large version)

Without thematic filtering options, viewing only the products of interest to them was often unreasonably fourth dimension-consuming for users. This was particularly the case when it came to actually selecting which item(s) to purchase, considering the relevant products would be randomly scattered across a product list. During testing, a lack of thematic filters oft led to website abandonment because the subjects prematurely concluded either that the store didn't behave the type of product they wanted (for example, spring jackets) or, more often, that finding the few relevant items that might exist subconscious somewhere in a vast production list would be nearly incommunicable. On websites that practice take thematic filters, the filters had very loftier usage rates, oft higher up 50%.

The easiest way to technically implement thematic filters is by manually tagging products or groups of products. Typical examples of thematic types are style (casual, romantic, modern), flavour (bound, holiday), usage conditions (outdoors, underwater) and purchase-selection parameters (cheapest, value for coin, loftier end). Some types are well suited to manual tagging (for case, manner and flavor will often be both fast and accurate for a human to tag), whereas other filters require extensive domain cognition to manually tag (for example, value for money).

Key Takeaway

Identify and offering central thematic filters unique to the website and production-type context. These will oftentimes need to exist category-specific (see section two). Common omissions are style, usage context and buy-selection parameters.

4. 32% Lack Compatibility Filters

Some products are compatibility-dependent — that is, a product's relevance is determined entirely past its compatibility with another production that the user already owns or plans on ownership. Typical compatibility-dependent products are accessories (for case, a case for a laptop that has to fit), products used in conjunction with other products (an audio organisation that needs to plug into a Boob tube and media players), spare parts (a laptop adapter that needs to have a charger tip and power rating that matches the user'southward laptop) and consumables (ink that has to fit an exact printer model).

Finding a spare adapter for a laptop or buying a camera and matching instance might sound like petty tasks, merely it turned out to exist extremely difficult for our test subjects, who had a completion charge per unit of only 35%. This means that 65% had to surrender or, worse, ended up purchasing a product that they believed was uniform only was in fact non.

"Oh my gosh, I wouldn't do this — not on a website which is this difficult to navigate. I would go to a camera store with my camera and find a case that fits. I wouldn't become about looking into all of these options," one subject explained while trying to find a camera purse and realizing there was no manner to narrow the list of 253 bags by size. The subject elaborated, "I'd need to get back and forth between this and the camera to compare the dimensions. And then information technology too has to look overnice."

No matter how enticing the price, how smashing the specifications, how perfect the customer reviews pronounce the product to be or how appealing the production'due south design, the finish user volition not exist interested if the product is incompatible. This could be a dealbreaker, regardless of the product's other attributes. This makes compatibility filters one of the most important filtering types (for compatibility-dependent production types only, of course). Giving users admission to a list of products that are compatible with the particular they already own is vital, then.

Despite compatibility filters existence a prerequisite for finding and purchasing compatible items, 32% of websites that sell compatibility-dependent products have no compatibility filters.

While nearly websites accept a "brand" filter, tests showed that this is completely inadequate as the merely blazon of compatibility filter. Get-go, brands ofttimes take multiple series or products with dissimilar compatibility aspects. For example, all Lenovo adapters will not fit all Lenovo laptops; so, simply applying a filter for "Lenovo" would not give the user a list of all products compatible with their particular Lenovo laptop. Secondly, for several compatibility dependencies, third-party products are a major consideration. For instance, a "manufacturer" or "brand" filter would not provide the user with a full list of matching sleeves for their MacBook laptop.

Key Takeaway

Whatsoever product category that contains compatibility-dependent products (accessories, integrated systems, spare parts, consumables, etc.) will need a compatibility filter. This will often exist a filter that allows the user to specify their model name and number, but it could also be a filter for a more generic specification, such as for size, capacity or power.

(Encounter sections 4 and vi of "An Due east-Commerce Study: Guidelines for Amend Navigation and Categories" for more on compatibility-dependent products, including a discussion of complete interlinking to compatible products on production pages.)

5. 10+ Filtering Values Require Truncation, Nonetheless 32% Do It Poorly

We tested three ascendant patterns for displaying lists of 10+ filtering values:

displaying all filtering values in i long list,
using inline scrollable areas,
truncating the filtering values.

All iii methods caused severe usability issues. The commencement ii performed the worst, while truncation proved to be the best performing of the 3 methods — but only as long equally information technology was implemented with corking attention to details of the user interface. Before diving into the details required to achieve a well-performing truncation design, let's briefly present the core bug with the first two methods.

A. Displaying All Filtering Values

The problem observed with displaying all filtering values in one long list is that it makes it impossible for the user to get an overview of the dissimilar filtering types available.

Displaying all filtering values in 1 long list makes it difficult for users to get an overview of the other filtering types. Hither, L.L. Bean is existence viewed on a 900-pixel-tall brandish (minus the browser and Bone chrome). (View large version)

During testing, users would see, for instance, a brand filter with one to three screens of brand filtering values within — making it impossible to get an overview of the additional filter types offered below. The bulk of test subjects completely disregarded the boosted filter types beneath the long list of filtering values and were mostly overwhelmed past the long filtering sidebar stretching two screens or more than. On a positive annotation, our product listing and filtering criterion shows that only a small fraction (2%) of major e-commerce websites currently use this pattern.

B. Using Inline Scrollable Areas

Some lists of filtering values are given their own scrollable area (i.e. the area can be scrolled independent of the rest of the page), causing several interaction bug for the bulk of test subjects, likewise equally conceptual challenges for a smaller grouping of subjects.

Inline scrollable areas, as seen here on Staples, caused multiple interaction problems for test subjects, both conceptual and interaction-wise. (View large version)

Implementing inline scrollable areas is far more common — 24% of major e-commerce websites utilize this pattern. It did not, still, turn out to perform any better, considering information technology comes with a host of bug on its ain. The about significant issues (which are also hard to solve) are the following:

Scrolling within scrolling (i.e. nested scrolling panes) turned out to be not a peculiarly easy concept for users to grasp. The inline scrollable area would be placed within the larger scrollable surface area of the web folio — requiring the user to sympathise the difference in order to avoid problems.
Users who wanted to apply a filter could not go an overview of all filtering options because the scrollable area was constrained in summit. The usability problem, thus, shifted from not getting an overview of filtering types to not getting an overview of filtering values within each type.
Inline scrollable areas often acquired "scroll-hijacking," whereby the user would scroll the web folio when they wanted to curl the filtering list, or vice versa. The user had to exist constantly aware of their mouse cursor'due south position whenever they wanted to coil. In other words, a dominant page-browsing blueprint on the web, vertical page scrolling, would be hijacked. (On bear upon devices, wide inline scrollable areas can trap the user, making it almost impossible to scroll the page instead of the inline curlicue area.)

(If you want to further explore the problems of inline scrollable areas, we examine the findings in depth elsewhere.)

C. Truncating Filtering Values

The terminal design we tested turned out to perform better than the other two. Truncation has the benefit of giving users an overview of the different filtering types. This is important because a lack of i often acquired our subjects to make poor filtering selections simply because they were inclined to interact with the filtering values that were offset in the very long list of filters. The other master benefit of truncation is that, when users find a filter type of interest, they also have the pick of getting a full overview of filtering values inside that type (by clicking the truncation link). Truncation, therefore, combines the benefits of the other two methods.

Truncated filtering values gives users an overview of both the filtering types available — as seen here on REI — and all available values within a type (when the truncation link is clicked). (View large version)

However, the superior performance of truncation was observed only when the risk of users overlooking the truncation link was actively addressed in the interface. In fact, on the tested websites where the truncation link wasn't sufficiently singled-out, information technology performed (at least) equally poorly every bit the two other patterns, because some users causeless that the truncated list showed all available filtering values. Currently, benchmarking shows that only 6% of major e-commerce websites have a truncation link that is inadequately designed. While that's not many, it would still be worthwhile to touch on some of the implementations of truncation that testing showed to exist effective:

Depending on the blueprint of the filter, up to x filtering values tin be displayed before the boosted values are truncated. On websites that display too few values before truncating — for example, fewer than vi values — users would ofttimes be confused by the reason for the truncation. When more than 10 values were displayed, the subjects' overview of the filtering types began to drib rapidly. (These numbers were not found to be difficult limits, but depended on the pattern of the filter and the number of filtering types bachelor.)
Earlier truncation sets in, the filtering values should be listed in gild of popularity, not alphabetically or by number of matches. Users will oftentimes browse for the name of a specific filter value, rather than the name of a filter type. For example, they volition browse a folio of laptop chargers for a "Lenovo" filter, rather than for a filter blazon named "uniform with." Consequently, the untruncated values are "representatives" of the filtering type and should therefore be the options that users are most likely to recognize when glancing at the page.
The truncation link should exist conspicuously styled, distinguishing it as an interactive chemical element unlike from the filtering values right in a higher place information technology. Important clues include the following: using the website'due south default link styling (color and/or underlining), using spatial indicators such every bit a plus sign (+) or arrow icon, indicating the number of matches in the link'south name ("View 23 more"), indenting differently than the filtering values (i.e. breaking the vertical alignment), and visually fading the last value in the truncated listing.

More test findings on proper truncation pattern are explored further in this article.

Key Takeaway

Truncate long lists of filtering values (10+), rather than displaying all values or using inline scrollable areas. To ensure that users discover the truncation, display upwards to x values earlier triggering the truncation, display default values that users are about probable to recognize (i.east. the nearly popular), and style the truncation link to gear up information technology apart from the filtering values.

Some categories take sure filters that are highly important and benign for the user to consider. However, displaying these merely as traditional filters in a filtering sidebar runs the run a risk of users either overlooking these options or non understanding the importance of making a selection.

Generally, during testing of due east-commerce websites, we observed that users view categories as something the website suggests they select, whereas the traditional filtering sidebar options are perceived by most users as being purely optional. Following the principles of persuasive blueprint, virtually websites, therefore, take a number of categories that need to promote certain filters or filter combinations. Luckily, a clear design emerged during testing for how websites can finer promote a single set of highly important filters — although implementation requires a number of filtering design details to be in place.

When test subjects searched Amazon, certain scopes would have highly relevant filters promoted atop the product list. This promotion nudged the exam subjects towards more informed filtering decisions, instead of browsing overly broad product lists. Besides being promoted atop the production list, the filter values are kept intact in the filtering sidebar (an important item). (View large version)

For example, if a user navigates to a "movies" category, a highly of import filter type to consider would be "format," with filtering values such as "DVD," "Bluray" and "digital download" as the types that would be of import to most users' process of selecting a product.

Another example would be a "digital cameras" category, where "camera type" would be a highly important filter to consider, with filtering values such as "indicate and shoot," "DSLR," "mirrorless" and "span."

Promoting a limited and select number of filtering values makes sense but if the vast bulk of users either have an interest in or would benefit significantly from applying them. Because a promoted filter encourages users to apply it, use the technique intelligently and sparingly, and avoid luring users into overly narrow filtered lists. For instance, don't just utilise the technique website-wide for whatever is the most popular filter in each category. In practise, you volition often demand to manually curate those categories that accept a structure that warrant the utilize of promoted filters.

Walmart takes the technique one footstep further and promotes a mix of laptop-size and input-type filters that align well with key purchasing parameters for users looking to buy a laptop. (View large version)

Promoted filters don't necessarily all need to be of the same type. They could simply be a combination of the most of import product filters that users can use earlier spending further fourth dimension investigating the actual product listing. Indeed, promoted filters could even apply multiple filters at once to provide the user with a shortcut to popular filter combinations.

Two additional implementation details to consider:

Go on the promoted filtering values in the filtering sidebar, too (i.e. in add-on to the "promotion" placement). Because users are trained that a filtering sidebar contains all bachelor filters, the promoted filter must be represented in the filtering sidebar too, since some users will look for the filtering value there.
Never promote filters using banner-like graphics. A few of the websites we tested had promoted filters that were visually boxed. This caused some of subjects to completely overlook them, even when the boxes contained the very filter blazon they were looking for — all due to imprint blindness.

Key Takeaway

For select categories where an initial filtering selection would exist relevant and would do good the vast majority of users, consider promoting those few filtering values in a higher place the product list (for example, using buttons, text links or thumbnails).

7. Filtering Functioning Varies Profoundly By Industry

If nosotros look at filtering performance within the major east-commerce industries, nosotros see that operation varies greatly. Beneath, the seven almost ascendant eastward-commerce industries have been plotted equally stacked bar charts. The row "acceptable operation" is for reference and depicts the threshold for an "acceptable" (but not good) filtering operation — a minimum based on the typical issues that test subjects encountered. Note that the performance difference takes the industry into business relationship; for example, an dress website needs fewer filters than an electronics website due to the blazon of products information technology carries and, therefore, needs a less advanced design for its filters to accomplish a higher score.

Despite having the everyman bulwark to provide a skillful filtering experience, wearing apparel websites notably have the worst operation of all industries for filtering, due to an unfortunate combination of inadequate filtering options and poor filtering interfaces. The subpar filtering interfaces are likely due to a deliberate prioritization of aesthetics over a clear and informative interface (a example of false simplicity). Despite dealing with a product type that requires just a limited number of filtering types (compared to other industries), many apparel websites lack fifty-fifty basic filtering options, such as for product material and user ratings.

Sports and hobby websites suffer from poor filtering functioning as well. While part of the reason is a prioritization of simple website aesthetics, similar to the clothes industry, another cause may exist the mix of visual- and spec-driven product verticals in the industry. Many products on these websites tend to exist fairly visual (toys, outdoor goods, sports equipment, hobby equipment), still many also have two to three technical attributes that could completely invalidate themselves if they don't match the user'due south criteria, such as operation, weight and historic period. Consequently, users will have more than complex filtering needs for sports and hobby products than they typically do for regular wearing apparel websites.

The electronics and role industry has historically been ane of those e-commerce industries that offer users a broad diversity of filters, simply because finding many products would otherwise be nearly impossible for users. When looking closer at the lacklustre filtering performance in electronics and office, the problem is often poor filtering logic and interfaces. Particularly common flaws include the post-obit: allowing but one filtering value to exist selected at a time, no user-divers ranges for numeric filters, and a lack of explanation of industry jargon. Despite a mostly loftier number of filter types being offered on several electronics and function websites, the products' technical nature — several attributes of which are vital to the user's purchasing determination — all the same result in a lack of compatibility filters (see section four of this article) and a lack of category-specific filtering types (see section 2).

Abode and hardware websites offer decent filtering performance. This aligns well with the technical nature of the industry, and the score tin can exist explained by a historical focus on offer sufficient filters (in particular, compatibility filters), which enables users to observe the item washing auto or cordless drill that meets their specific criteria. However, poor product data and a widespread lack of structured production specifications hold back filtering performance.

Health and beauty websites have decent filtering performance as well. In fairness, wellness and beauty products have fewer key product attributes (quantity existence an exception), which means the websites can get away with much simpler filters than ones with highly spec-driven products. E-commerce websites in other industries, therefore, should not model their filtering experience on wellness and beauty websites because their filtering needs are likely different.

Mass merchants have vast and diverse production catalogs that have strict requirements for production information structures, processing and categorization — all things that can be incredibly difficult to get right. Combine that with a mixed catalog of highly spec-driven and visual production types, and mass merchants have the almost complex filtering needs. Yet, information technology is clear that most mass merchants are aware of these challenges and have fabricated very agile efforts to resolve them, often through avant-garde filtering logic and data post-processing. This leads to a broad variety of filters being offered (including category-specific ones), which is one of the chief reasons mass merchant websites achieve the all-time filtering performance — fifty-fifty taking their users' more complex filtering requirements into account.

Improving Eastward-Commerce Filtering

Overall, the filtering performance of the websites we benchmarked is passable at best. When it comes to filtering, the majority of fifty-fifty the top e-commerce websites come up brusk compared to physical retail, where a customer request such as "a light casual spring jacket in size medium" or "a rugged example for this digital camera" isn't out of the ordinary.

Some websites do actively focus on filtering and spend resources on product tagging. For those websites, many of the lingering filter-related usability bug have to do with aligning user expectations and website implementation (specifically, filtering design and logic). Filtering thus represents an opportunity to vastly improve the return on investment that most large e-commerce vendors take already made in product tagging and information collection.

Filtering on e-commerce websites is a major topic that plainly cannot be fully explored in a single article. However, the filtering insights covered in this article hopefully lay the foundation for understanding the current land of e-commerce filtering and for creating a skillful filtering feel:

While mediocre filtering performance is often due to a lack of important filtering options, benchmarking too reveals that filtering logic and filtering interfaces crusade astringent issues for users. When looking at the users' entire filtering feel, merely sixteen% of the superlative 50 United states e-commerce websites offering a good experience, while 50% offering a passable filtering feel, and 34% have a poor filtering experience, without filters for users' nigh basic product preferences.
To ensure filtering availability, always ensure that each category has a unique set of filters specific to the type of products it contains. At a minimum, the product specifications included in the list items will need to be available equally filters too, but a wider assortment of filters will virtually always be needed. Currently, 42% of the meridian e-commerce websites lack category-specific filter types for several of their core production verticals.
Identify and offer central thematic filters unique to the website and product-blazon context. These will often need to be category-specific, and common omissions are a lack of style, usage context or purchase option parameters. Currently, twenty% lack thematic filters.
Whatsoever product category that contains compatibility-dependent products (accessories, integrated systems, spare parts, consumables, etc.) will demand a compatibility filter. This is often a filter that allows the user to specify a model proper name and number, only information technology could also be a filter for a more than generic specification, such as a filter for size, capacity or power. Currently, 32% of websites that sell compatibility-dependent products lack compatibility filters.
Long lists of filtering values (10+) should be truncated rather than be displayed in full (equally 2% practise) or use inline scrollable areas (24%). To ensure that users notice the truncation, do a few things: display up to 10 values before the truncation sets in; make sure the default displayed values are the values that users are most likely to recognize (i.e. the nigh popular); and style the truncation link itself to prepare information technology apart from the filtering values.
For select categories where an initial filtering selection would exist relevant and would benefit the vast majority of users, consider promoting those few filtering values above the product list (for example, using buttons, text links or thumbnails). Currently, only 16% actively promote highly important filters on top of the product list.
Filtering performance varies greatly by manufacture, and the key players in your industry might non be a good source of inspiration. Even when adjusted for the different levels of filtering needs, websites in the apparel, electronics and sports industries are significantly backside in the filtering experience offered by mass merchant and hardware websites.

If you want to further explore the filtering implementation and functioning of the 50 benchmarked websites, yous can exercise and then in the interactive version of the product lists and filtering benchmark database. (Note that the benchmark database also covers e-commerce product lists and list item blueprint — areas we'll cover in a carve up follow-upwardly commodity.) You may want to start out by exploring some of the few websites that offer a adept filtering feel: