The State of E-Commerce Search Functionality
User-experience research published in 2018 by the Nielsen Norman Group showed that on-site searches were successful on the first try 83 percent of the time, as opposed to 51 percent and 64 percent of the time, respectively, in their earlier studies from 2000 and 2011. Similar research published in 2020 by the Baymard Institute also found a steady improvement in the quality of search results over time. Great news, right?
Yet upon closer examination, both groups of researchers found that the picture was less rosy. Researchers at Nielsen Norman identified a number of widespread user-interface issues that could sharply decrease the chances of your customers finding products and increase the likelihood of them abandoning their search.
Similarly, Baymard found that the likelihood of your shoppers finding the product they’re looking for varied sharply depending on the type of search they performed. Researchers also noted that while search has objectively improved, users’ expectations are also on the rise. Viewed in that light, it may be high time you upgraded your website’s on-site search experience.
Some Searches Work Well, Others Don’t
Instead of generating a single number for search-result success, the Baymard Institute broke it down by the type of query. This more granular approach makes for better, and more revealing, research: most sites handle some searches much better than others. For example, searches based on a tangible, specific feature (“leather jacket,” “red dress”) worked quite well, showing site testers appropriate products 86 percent of the time. Yet a similar search based on a less specific product types (i.e. simply “dress shirt”) was successful only 31 percent of the time.
Strikingly, even searches using an exact search term — where researchers entered in the name and model number of a product they knew was on the site (something like “Nike Air VaporMax Evo CT2868-003” ) — returned a correct result just 71 percent of the time.
If that’s not enough to catch your attention, consider this additional piece of information: Baymard’s research was confined to 60 of the top e-commerce sites on the planet. These aren’t anonymous merchants from the middle of the pack, they’re massive players like Amazon, Walmart, Target, Costco, Wayfair, H&M, Sephora and L.L. Bean. If these titans of the industry struggle to get search right, despite the massive resources at their disposal, clearly all e-commerce businesses would be well served to re-examine their search performance.
Specific On-Site Search Problems
Both sets of researchers identified a consistent set of issues across the retail websites they studied. Some were simply a factor of website design, such as poor location or sizing of the search box itself. Others were more directly related to the search function itself.
The reason exact searches often fail, for example, is that they’re not exact enough. On-site search routines often won’t return a hit unless your shopper manages to guess the precise phrasing used in the product description. That means no typos, no shorthand spellings, no synonyms (you get the idea).
Almost half of the sites Baymard tested fared poorly on themed searches like “spring jacket,” as well as those focused on broader types of products like the aforementioned “dress shirt.” These are all searches that are — or should be — the bread and butter of any fashion brand. It’s important to get this right: shoppers who use your site search stay longer, view more pages and convert more often.
The Common Thread? Your Search May Be Letting Shoppers Down
When you step back and look at the larger picture, the problem immediately becomes clear: conventional search struggles to make connections between customers and your products. It may not know that a “twenty-five inch inseam” is the same as a “25″ inseam,” or that a top is also a blouse. More importantly, it can struggle to understand why a given group of garments and accessories might specifically feel like spring, or be “beach-ready.”
In a worst-case scenario, search might return no results at all or inaccurate results that have nothing to do with what someone entered into your search bar, leading shoppers to abandon your site. The end result is lost sales for you and an unsatisfying experience for your shopper. The good news is that having better product data can help.
The Lily AI Approach
Our sophisticated AI algorithm — trained on the world’s largest proprietary data set — analyzes the garments and accessories that make up your inventory and applies a deeper, richer set of product attributes to each one. This gives your on-site search the data it needs to return the correct search results to keep shoppers engaged.
The unprecedented surge in online shopping represents a remarkable opportunity. If your on-site search hasn’t positioned you to take maximum advantage — no matter what your current sales figures say — you’re losing ground to nimbler competitors.
Contact us today to learn how your search can begin to draw on all the product details that matter.
Nielsen Norman Group: The State of Ecommerce Search (2018)
Baymard Institute: Deconstructing E-Commerce Search: The 8 Most Common Query Types
CXL: Internal Site Search Optimization: Best Practices for Your Site
Nielsen Norman Group: UX Guidelines for Ecommerce Product Pages
Search Engine Journal: On-Site Search & SEO: Everything You Need to Know