When consumers head to their favorite retail e-commerce website and type a typical, specific search term for something they need or want into the site’s search bar, what do they typically find?
If you’re a shopper yourself – and you almost certainly are – you know that it’s very often a very suboptimal experience. Shoppers, even on the websites of the world’s leading brands and retailers, are met with a surprising amount of resistance, in the form of multiple pages of irrelevant results; search results that are close but no cigar; and sometimes, completely blank results pages. It can be a fun and sobering exercise to try it out yourself.
A fun exercise if you’ve got some time is to conduct a search like this of your own on a few retail e-commerce sites. We made one of our Lily AI co-workers conduct a search for the type of shirt he usually wears to work, a search that would probably go something like this: “collared untucked checked men’s large.”
At one high-profile retailer that very much sells men’s clothing in this style, there appeared to be no collared, checked men’s shirts – even though he knew they sold them – yet he did receive these “very helpful” recommendations for items that he might like instead:
What do you think the chances were, had this been real, that his time on this site would have ended right then and there? The point is that every consumer has their own “long-tail” way to describe what it is they’re looking for. As we make our way in the world, we learn different colloquialisms, expressions and features that we use as shorthand to describe what’s important to us.
Yet this “search diversity” is barely acknowledged on a high percentage of modern e-commerce retail sites, and the upshot of this problem is missing results from on-site search that, had they been returned to the curious shopper, could have led to an instant purchase. There’s also the shopper frustration that comes from being recommended hand towels and teacups when the goal is to find a new shirt to wear this Monday.
This guide is for those retailers and brands who understand that there are huge gains to be made in both revenues and customer satisfaction in getting this right. We’ve outlined some of the current challenges, as well as 6 potential solutions that will help the modern e-commerce retailer make on-site search one of the very best things they do, as opposed to a liability that drives customers away. Read on!
say they won’t return to a site that provides a poor search experience.
We now know that search has become absolutely central to the retail e-commerce experience. Up to 43% of visitors to retail websites immediately go to the search bar, and those consumers are 2-3x more likely to make a purchase than site visitors who don’t use the search bar. (1)
In fact, we have a few other stats that underscore the importance of search:
And yet over 60% of sites require users to guess the exact “correct” search term for a product. (5) Let’s take a look at why this is so frustrating for end users, and why it runs so counter to the notion of search diversity.
Consider the synonyms for “Loose Dress” in this example. What you might call a loose dress I might call a sundress; your best friend might call it a nap dress and her mother might call it a house dress:
The key to serving all four of these individuals is to build a robust product taxonomy that takes into account how real people search in the real world. Sure, those who are already focused on a particular brand name can often unknowingly filter their search just to the results from that particular brand (i.e. Vera Wang, Hugo Boss, Topman etc.), and that certainly makes it easier. For the majority of shoppers looking to compare items between brands, however, it’s essential to assign attributes to items that allow for the rich and varied diversity in what we actually call things, and what’s truly important to us when we shop.
Any initiative to improve the digital experience must keep search front and center. Improvements to the search experience can make the most impact because they directly affect what we call high-intent shoppers – the ones who know what they want, but who may describe what it is they’re looking for in a “language” different from that used by the merchant they’re visiting.
If these retailers can instead serve up relevant and comprehensive results for their most promising site visitors, they’ll convert more orders and build brand loyalists. The stakes couldn’t be higher. PwC reports that 32% of customers will leave a brand they love after just one bad experience. (6) Now lets start talking about solutions – the 6 Steps to Better E-Commerce Search that you can begin implementing right away.
Nearly 84% of companies don’t actively optimize or even measure their on-site search, which points to a true opportunity in the marketplace. The retailer who does invest some time and money into optimizing site search can stand out quite quickly. Here are the 6 steps to undertake to make sure your organization is the one that does.
You can’t measure improvement until and unless you understand your starting point, so begin with a cold-eyed analysis of your site’s current search capabilities. Most e-commerce site search fails on a number of fundamentals.
The biggest takeaway, as mentioned previously, is that over 60% of sites require users to guess the exact “correct” search term for a product. Most sites fail to recognize (and account for) typos, different phrasings, colloquialisms, contractions or symbols and synonyms. And all too often, on-site search fails to deliver hyper-specific results from specific, long-tail searches (even though there are a handful of products in a retailer’s catalog that would fit the criteria).
There are several ways to assess how efficiently your search works and potential inadequacies. One is to look at your on-site metrics: If you’re seeing a high level of bounce after one or two searches, that’s a red flag. Another is to create a routine that flags searches that return few or no results, which gives a clear insight into specific searches that don’t work. A third and very important option is to pay actual shoppers — not your web team or your marketing team — to search the site for specific items, noting both how successful they were and how much effort it took. Most importantly, ask them what they liked and disliked about the process (and how it could be improved).
Adding filters and facets to your search tool and product category taxonomy is one of the most powerful ways to enhance your product discovery capabilities. Filters allow your users to choose applicable options from a list you provide them: manufacturer, color, product category or anything else that might be meaningful — getting them closer to the products they’re looking for.
Don’t overlook the fact that this also allows users to filter out specific characteristics. It’s more important to deliver accurate results than to get as many products in front of the customers as possible. A search that returns 10,000 hits is almost as useless as one that returns none, so this is important.
On a similar note, you should categorize your products as much as possible, and in as many ways as possible. For example, dresses could be categorized by style, color, size range, type of fabric, embellishments, or any other attributes meaningful to your product catalog. You should also think in terms of thematic searches, something only about half of the companies in Baymard’s research handled well. These categories are drawn conceptually rather than by the characteristics of the garment itself. Examples might include “spring outfit,” “party dress,” and “work shoes.”
These categories, and the searches and toggles mentioned previously, work together to speed and empower search. For example, the phrase “work shoes” might mean very different things to a nurse, a factory worker or a senior partner at a law firm. Adding a well-thought-out set of filters to the basic “work shoes” category allows your users to further filter search results based on their style preferences.
One limitation you’ll inevitably encounter in your quest to improve product discoverability is inaccurate and incomplete product data. Sure, you can simply import the primary data points from your suppliers – what are often called “legacy, out of the box attributes” or general attributes, sourced from manufacturers and distributors. From that base level of searchable characteristics — size, style, color, fabric and so on — you decide how many additional tags or attributes are attached to the product. But this is often done manually, so it’s costly and prone to inconsistency and inaccuracy. The more products you have, the more quickly you’ll reach the point of diminishing returns.
Lily AI’s product attributes platform breaks that limitation. Our proprietary machine-learning algorithm analyzes your product line using the language of customers (not merely suppliers, distributors or merchants) and applies deep tagging, potentially creating dozens of new data points for your products. Better yet, because it’s automated, this isn’t a massive, time-consuming project. Your site’s search capabilities will be able to utilize all of this new data to deliver accurate search results based on long-tail, subjective and objective searches within just a few weeks.
At this stage of the process, with the high-level “broad strokes” fixes in place, you can begin to focus on finesse. One way to do this is through the use of improved autofill suggestions in your search tool. Your search tool may base its suggestions on site-wide search history, so a user typing in “spring dress” will see suggestions for all spring-related categories after the first word but only spring dresses after the letter “d” is typed.
Autofill and autocomplete are much more powerful with an expanded, relevant product taxonomy (“the language of the customer”), because now you’re able to make suggestions that capture the many long-tail searches that your shoppers actually use, then deliver them exactly what they’re looking for: “spring dresses easter”, “spring dresses floral pattern” and so on. It’s a virtuous cycle. It makes users more confident in the search and encourages them to add more details, leading to more precise matches and better search conversion overall.”
An important point to remember is that improvement and optimization of your website is not a one-and-done task to be checked off of your list, but an ongoing process to build into your workflow.
Each step in this process informs the others: Reducing bounce gives you more user searches and interactions to observe; user feedback helps you improve your search tools; clickable filters and categories tell you about your users’ search priorities; deep tagging empowers the creation of more finely tuned filters; and so on.
As you repeat this process, your grasp of your users’ search process will improve immeasurably. You’ll know which filters they use, which search tools they find intuitive and which changes to your search made the biggest differences to your conversion rates and sales figures.
It’s a virtuous cycle, and a very powerful one.
It’s no longer acceptable to return 10-plus pages of results that drop off dramatically in relevance after the first click. If you read customer ratings and monitor mentions of your company online and on social media (and you should, it’s important), you’ll know when your efforts are bearing fruit. How? Because your customers will rave about your brand. They’ll share, they comment, they show excitement. They’ll convert where you want them to – on your website, not someone else’s.
It’s not just in search, either. Speaking in the language of customers, rather than that of manufacturers or distributors, has hugely beneficial ramifications across the entire retail value chain. A product with two to three attributes can, through visual, AI-driven tagging and a carefully-designed taxonomy, immediately unlock its sales potential to become a product with 10-15 attributes.
When product attribution is done correctly in this manner, it instantly drives better site search, filters and facets, product recommendations, and demand forecasting. On-site conversion goes from an anemic retail industry average of 2.5% to 4%, to 5% and more – and just as importantly, the ability to sell at full margins is enhanced because demand is now forecast using that language of customer-driven attributes – not legacy, out-of-the-box attributes. This increases the ability to sell to customers what they’re looking for right now, and decreases the need to mark that inventory down later. “It’s the way we’ve always done it” becomes a thing of the past, and retail customers have a new set of forward-looking, customer-focused brands to pin their shopping allegiances to.
Neil Patel, 2021, https://neilpatel.com/blog/site-search-killing-your-conversion/
Neil Patel, 2021, https://neilpatel.com/blog/site-search-killing-your-conversion/
Forrester Research, https://www.addsearch.com/blog/shockingly-high-cost-poor-site-search/
eConsultancy, Is site search less important for niche retailers?, https://econsultancy.com/is-site-search-less-important-for-niche-retailers/
Baymard Institute, Deconstructing E-Commerce Search: The 8 Most Common Query Types, https://baymard.com/blog/ecommerce-search-query-types
PWC, Experience is Everything: Here’s how to get it right, https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf
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