In this guide, we’ll explain:
- What faceted search is.
- Why faceted search is so important.
- How faceted search needs to evolve to meet customer needs.
What Is Faceted Search?
Faceted search is a core feature of search applications that enhances traditional search functions with a faceted navigation system.
More precisely, faceted search leverages customers’ natural tendency to organize information with categories and allows users to tweak the search results to their interests by selecting a range of different attributes.
For example, if I search for “women’s dresses” on Macy’s website, faceted search allows me to narrow down the number of search results using multiple dimensions, or facets, to get to the desired item or product.
What Faceted Search Is Not
The terms “filter” and “facet” are often used interchangeably, but filters are generally broader in scope and only one generic filter can be applied at a time. This means that each filter may produce hundreds or thousands of products, which can be frustrating for users to manually explore.
Faceted search, however, allows users to refine their searches more intentionally using multiple filters at the same time, which can be applied to specific product attributes. This means the shopper can locate the exact product they’re looking for more easily.
A Brief History of Faceted Search
Academic research led the way in advancing our understanding of faceted search as a technique to move beyond the limitations of previous approaches to information retrieval.
Yet, the biggest success stories of faceted search have come from its use in commercial applications.
To be sure, trying to condense the history of commercial applications of faceted search into very few sentences may sound overly ambitious. But it actually isn’t so. Why? The simple truth is that most vendors offer solutions that are still based on what was considered to be cutting-edge technology 20 years ago.
In particular, during the early 2000s faceted search moved from being just a research topic to a ubiquitous feature of commercial search applications. Endeca earned a strong reputation for its “Guided Navigation,” which made faceted search a core feature of Ecommerce and site search, providing faceted search for major Ecommerce websites. Moreover, the emergence of open source options such as Solr made basic faceted search a commodity feature.
Since then, so many things have changed in the Ecommerce landscape and in consumer behaviour, from the increasing importance of mobile commerce to the rise of drop shipping.
Yet, when we consider the solutions offered by most vendors, faceted search applications haven’t evolved enough to keep up with consumer needs.
Why Faceted Search Is So Important
There are several key reasons why faceted search is so important.
Your customers’ time and attention aren’t endless.
As Herbert Simon, the Nobel Laureate who pioneered the study of bounded rationality, said, “a wealth of information creates a poverty of attention and a need to allocate that attention efficiently.”
Managing information overload is critical. In fact, it has also been shown that an overwhelming number of options to choose from may lead to adverse consequences, such as a decrease in the motivation to choose.
Ecommerce search engines traditionally aim to maximize the number of relevant products returned to a user, assuming that the more items they return, the more likely customers will be able to find just the right thing. For example, JD returns almost 700 products for my “Nike shoes” query.
This number could look intimidating, but here is precisely where faceted search can become such a powerful tool. It helps mitigate information overload in situations where the size of the search result set is intractable.
Faceted search allows your customers to easily narrow down results and access products using filters and facets, managing complexity and making even seemingly staggering result sets manageable.
Helping Navigate the ‘Endless Aisle’
While your customers’ time and attention may be limited, Ecommerce product catalogs often aren’t. Which means that empowering your customers to drill down into the result sets using faceted search is fundamental.
Back in 2004, Chris Anderson popularized the concept of the Long Tail. This refers to the strategy allowing companies to realize significant profits by selling low volumes of hard-to-find items to many customers—instead of only selling large volumes of a reduced number of popular items.
The theory has been criticized, but one of its key concepts is actually more relevant than ever.
Specifically, the long tail theory is also commonly referred to as the “endless aisle” or “infinite shelf space” because it allows retailers to offer a larger variety of products and services to consumers.
It is indeed true that we see an ever-growing number of SKUs to choose from on most websites, a trend which may even accelerate further in light of the increasing popularity of drop shipping.
Yet, SKU proliferation becomes a real problem if you don’t have solid faceted search in place. After all, more is better only if you can find what matters. Having the right facets to help customers navigate your product catalog is critical to achieving that.
In Ecommerce search, unlike web search, users’ queries tend to be broad and generic. To provide some numbers, while the average number of words per query is 2.3 for within site searches (compared to 3.4 for web-wide searches, according to Nielsen Norman Group), the average number of words for short head queries is lower and definitely shorter than two words.
For example, think of queries such as “shoes,” “Nike shoes” or “tennis t-shirts.” With such broad and generic queries, the amount of information lacks the specificity required to return results that match the shopper’s preferences and needs.
Faceted search can help in situations where the desired and optimal product isn’t known in advance and queries are so generic: customers can navigate easily by selecting facets for color, size, brand and so forth. In the below example, Amazon is helping customers narrow down the results and access the most relevant and optimal product by using a range of sensible facets.
Limited Domain Knowledge
Faceted search can help in contexts in which customers have limited domain knowledge.
To formulate a good search query, users need to know fairly well what they are searching for. They need to understand the search space and put in the right keywords. Sometimes that’s easy and users can quickly come up with a reasonably good query.
But when a domain novice is trying to buy a new oven, what types of attributes are relevant?
Facets provide a great opportunity to educate customers about the range of features and attributes that are most relevant to the specific domain and query. For example, not everyone is aware of a Shabbat mode option.
The Best Of Both Worlds
Faceted search combines the best of both search and navigation experiences.
We know that search converts higher than navigation. For example, based on our data, those who conduct a search are between two to four times more likely to convert than visitors who don’t.
It’s also a widely accepted truth that website navigation is less demanding for customers than search. Even when users are familiar with the search space, search requires them to recall information from memory.
To create a meaningful query, a user needs to include attributes that are relevant to their goal. This isn’t the case with navigation, which replaces recall with recognition: instead of forcing users to come up with a complex query, they can recall only a minimum and then use recognition to augment their query with relevant terms.
Interestingly, faceted search allows companies to keep their customers searching, which converts higher, while also giving them better experiences in light of the reduced cognitive effort required.
Why Faceted Search Needs to Adapt to Stay Relevant
Faceted search is critical, and so it’s critical to get it right.
The Current State of Ecommerce Faceted Search
Too often, customers have poor, irrelevant faceted search experiences on Ecommerce websites.
While the Baymard Institute reports that only 40% of websites offer faceted search capabilities, the truth is that even those that do have faceted search in place fail to meet rising customer expectations.
Even on major websites (like Puma’s, shown below), faceted search may deliver irrelevant experiences. For example, if I search for “hats,” the facets indeed include size, gender, or price, as they should. But I can also encounter a facet for “product type,” which isn’t relevant.
Traditional Requirements for Faceted Search
Mastering faceted search capabilities isn’t easy. As discussed by leading facet search expert Daniel Tunkeland, facets for search queries should ideally meet the following conditions:
Facets and their values should represent result aspects that searchers who perform that query care about.
For example, if customers cared about the fit of Ralph Lauren’s shirts, then it would not be ideal to display only the facets shown below, as users aren’t empowered to drill down into the result set. Ideally, in that scenario, customers should be able to select, say, “slim fit” as a relevant attribute.
Facets should have high coverage among the results. For example, shirt size has high coverage if the results are all shirts, but has lower coverage if the results also include pants and shoes.
Selecting a facet value should significantly reduce the number of results, and it should filter out a large fraction of top results. For example, color is a useful facet for “shirts” but not for “white shirts.”
It’s important to present facets by relevance and importance to the current user. When facets selected for navigation tend to be “static,” (that is, they often don’t change with different keywords) the UX suffers significantly. A static ordering is less effective when some facets only apply to particular query contexts.
For example, on an Ecommerce site that sells consumer electronics, some facets only apply to subsets of the product catalog, such as wattage for audio speakers or megapixels for digital cameras. Those facets should only be displayed when the user is looking at narrow result sets. In general, the same kinds of utility measures used for filtering can also be used for ranking.
Reimagining Faceted Search to Remain Relevant
But while these standards may have been enough a few years ago, nowadays companies that wish to remain relevant need to ensure that their faceted search solutions follow other best practices as well.
Faceted Search for Shorter and More Personalized Journeys
Streamline the customer journey to require the fewest clicks necessary to access the content they crave. After all, we live in the “Era of the Now,” as Forrester dubbed, with customers increasingly impatient and eager to access content in a frictionless fashion.
On one hand, it’s true that the success of faceted search has resulted in debunking one widely held view based on the “three-click rule.” That is, the web design rule of thumb that no piece of content should take more than three clicks to access.
On the other, all things being equal, it’s still true that reducing the number of clicks needed to access content is important. That’s why preselecting the most relevant facets based on user history and search trends can help customers reach relevant results faster and more easily.
Mobile Friendly Faceted Search
A modern faceted search solution needs to be mobile friendly. Faceted search was originally designed for desktop and laptop users. Translating this experience onto a mobile device is difficult, as the NNGroup rightly pointed out, because the very same feature that makes faceted search so helpful to users, namely being able to see the filters and the results at the same time, is difficult to achieve on a small screen. While this varies from vertical to vertical, 80% of online traffic is now on mobile.
Amazon themselves used to suffer from this problem, taking you to a separate page when refinements are applied, thus making you lose the instant feedback you get with a desktop design.
Now they have greatly improved the user experience on mobile devices.
Amazon now offers a solution to address this problem by having not only a horizontal filter tool for faceted navigation on mobile, but also a handful of filters optimized for users in light of their profiles.
Interested in learning more about best practices for delivering personalized ecommerce experiences? Download our ‘Guide to Delivering Intelligent Shopping Experiences.’