Faceted filtering for better ecommerce experiences

Imagine you’re online shopping for a new summer swimsuit — how would you approach the process? You’d probably go to your favorite store’s website, select the “swimwear” category, then specify the colors and sizes you’re looking for to browse relevant options.

What if this kind of refinement wasn’t available? You’d have to sort through pages of options to find even one swimsuit, and it would be nearly impossible to make quick comparisons between swimsuits you like (unless you opened each option in its own browser tab).

Luckily, many ecommerce websites know how frustrating it is to have to through all their product offerings, so they provide ways to find items you’re interested in through faceted filtering:

Faceted filtering optimizes the search experience by allowing the user to select attributes from predefined categories so they only have to choose from the ones that meet their constraints. Not only that, faceted filtering helps the user discover more relevant options than they would with a standard search query.

With search, the user has to know what they need to type in their query to get the right results. For example, a user searches, “trench coat,” and only trench coats appear in the results. However, maybe the user actually wants to find top coats but their search limited the results. Faceted filtering helps the user understand categories to quickly find what they want – even before they know what they’re searching for.

But what exactly is faceted filtering and how should designers best use it in their designs? This article examines the key differences between facets and filters, reviews best practices and drawbacks of faceted filtering, then looks at its advanced methods.

Editor’s note: This blog was updated 27 March 2025 by Allie Paschal to ensure updated information and provide new insights relevant to designers in 2025.

Key differences between filters and facets

Faceted filtering (also known as faceted navigation or faceted search) supports users in quickly navigating and searching through a lot of data. It involves two separate concepts working together — facets and filters. Although both concepts help the user refine results on different search parameters, they have slightly different functions and are often used simultaneously.

Filters

Filters are used to refine a set of data (like product listings or booking hotels) according to specific attributes. Once the user sets an attribute, all the options that don’t meet the criteria are eliminated from the results.

Differences of filters include:

• Limited refinement — In this case, filters are simple and typically allow the user to refine a search or web-page according to one specific attribute at a time. For example, a user can select “tops” from the navigation menu to refine the results to only show relevant tops
• Static content — Here, filters are less smart or flexible compared to facets and don’t change between searches or web-page content. Each time the user can filter the web-page’s content, it’s for the same attributes
• Begins the user’s search — Because filters need to work for all content on a web-page, they’re quite general and broad. Users adopt filters to narrow down their initial search results, so as the results get more specific, less filters apply

Facets

Facets, on the other hand, categorize information or data from a search or on a web-page. They give the user a summary by organizing data into buckets, such as product type or color. For instance, if the user selects “shorts” and “pants” for the product type and “blue” for the color, only blue shorts and pants will show in the results.

Differences of facets include:

• Detailed refinement — Facets allow the user to refine the data with multiple attributes at a time, such as the brand, color and price, allowing the user greater control over what they see in the results
• Dynamic content — Facets can change depending on the search results or web-page content. For example, when a user views the search results for “tops,” they can refine the results more with additional attributes such as “blouses” or “button-down shirts”
• Narrows down the user’s search — When users review results after conducting an initial search or using a filter, the user can use even more fine-tuned attributes to their search to explore precise results

Best practices for faceted filtering

Use the following best practices to ensure your facets and filters allow users to quickly find what they need on your site:

• Use clear labels — The user should immediately understand what the facets and filters represent when skimming the options. It’s best to avoid technical jargon or vague terms; for instance, use “print resolution” instead of “DPI”
• Prioritize relevant facets and filters — Although you may give the user many different facets and filters to refine their search, they’ll use some more than others. Position the facets and filters they’re more likely to use at the top of the list so they’re more accessible
• Display applied filters — After the user selects filters and their results have been modified, they need to know what filters have been applied. This makes it easy for the user to recall and remove any unnecessary filters
• Limit facets and filters — Too many facets and filters can overwhelm the user. However, having too many options can make it difficult for the user to decide what filters they should apply
• Provide a search function in facets — If the number of filters within a facet is long (15+ items), provide a search for the user to efficiently find the correct filter. For example, some ecommerce sites offer a “brand” facet that can contain hundreds of options; a search allows users to quickly apply the filters for the specific brands they want to view
• Allow the user to save filters — If your website has loyal users who visit frequently, you may want to save their preferred filters, such as clothing sizes. This prevents them from having to apply the same filters each time they visit the site
• Test facets and filters with users — To make sure your facets and filters are effective, you need to regularly test them with real users. By doing so, you can ensure the labels are understandable and the right filters are being prioritized
• Consider mobile users — While filters are shown in relation to your web-page’s content on desktop devices, you can’t provide the same experience on mobile devices. Establish an easy way for users to access and navigate through the filters, such as providing collapsible facets:

Common pitfalls to avoid with faceted filtering

Now that you know the best practices for faceted filtering, this section covers common pitfalls that you may encounter and how to navigate through them:

• Performance issues — If you have too large of a dataset, your users may experience lags in response times when selecting filters. To mitigate loading issues, progressively load filters by prioritizing the most relevant ones and hide the rest behind a “load more” button
• Overlapping facets — If the dataset includes many facets to organize the filters, there’s a chance one facet may coincide with another. For instance, the facets “brand” and “product line” can be confused with each other. To make sure users understand the facet labels, conduct card-sorting exercises and user tests to verify the labelling
• Non-persistent filters — If the user refreshes the page or navigates to a product detail page then returns, it’s frustrating if their applied filters disappear. It’s better to maintain the filters so the user can continue their search after navigating away
• Zero results — The user may select so many filters that they eliminate all options, which can lead to confusion. Prevent the user from getting zero results by disabling filters that yield no results and remove filters that conflict with one another. If the user still encounters no results, show suggested filters to find more results
• No reset option — Since faceted filtering allows the user to apply many filters at once time, there needs to be an easy way to clear all filters. Instead of forcing the user to remove each filter individually, provide a “clear filters” button near the display showing the applied filters

Advanced methods of faceted filtering

Beyond the basic concepts of faceted filtering, you can also apply more advanced methods to help automate the process and give users additional control. These include:

Dynamic faceting

Here, instead of the user seeing the same facets and filters regardless of their initial search, the facets and filters adapt based on the context. For example, if the user searches for “jackets,” facets such as “inseam” and “product category” are no longer relevant.

Dynamic faceting can also be used to prevent zero results. As the user selects multiple filters and fine-tunes their results, filters that don’t have results that meet the selected criteria should be disabled or removed. If the user selects “heels” when shopping for shoes, brands like Nike and Adidas should be eliminated from the “brands” facet – this way, users can’t select a filter that doesn’t yield any options:

AI-powered filtering

You have an opportunity to incorporate AI into your filtering process by using the historical data of your users. Once the user applies one filter, AI can suggest the next filter based on their past choices or even what similar users have chosen.

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AI can also be used to generate filters for the user. For instance, the user could ask an AI agent, “Show me blue button-down tops under $40,” and the AI agent could apply filters for the user. Or the user can upload an image of an item they like and ask the AI agent to find them something similar.

Here, the user doesn’t have to know which filters to apply to get the results they want — AI does it for them. Better yet, the user still has control over the filters applied, and can remove a filter if it’s impacting the results.

Interactive filter controls

Typically, filters are applied using standard checkboxes, but you can give the user even more control with UI components like sliders, toggles, or color swatches. For example, instead of providing checkboxes to filter prices (like “Under $50”), allow the user to specify both their minimum and maximum price range with a range slider component.

For binary options, such as “eligible for free shipping” or “on sale,” use a toggle so users can quickly turn a filter on or off (versus throwing the filters into a miscellaneous facet). This also differentiates filters that are stand-alone options and don’t easily fall into a facet. Lastly, for colors, provide a color swatch that also acts as a button so users can visually reinforce the color with its label (you still need to apply a label next to a color swatch so it’s accessible):

Final thoughts

There’s a reason why so many ecommerce websites use faceted filtering — it helps users control how they navigate through information to efficiently find what they’re looking for. Users don’t have the time or patience to go through all the listings in a website; without facets and filters, users get quickly frustrated and abandon the website.

To review, faceted filtering involves (you guessed it) facets and filters. Although they seem like they describe the same thing, filters describe a single attribute the user can use to narrow down their search, while facets describe the main buckets the content is organized into. Filters are broad and used when the user begins their search, and facets are specific and used when the user wants more precise search results.

If you decide to use faceted filters, use clear labels, don’t include too many, and regularly test them with real users. As faceted filtering has become common in ecommerce websites, advanced methods, such as dynamic faceting, have begun to pop up. These advanced methods give the user more control in the results they see and automate filtering for them.

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