Paul Kuijs, Lead Business Consultant Commerce - EMEA
As digital channels become increasingly used by business-to-business (B2B) customers, B2B sellers must look towards the ways that they can improve their digital experience during the research and purchasing process. One of the most important facets of an eCommerce website is the site search, which is used to find products, listings, and other content within a seller’s website.
Recent study by Wunderman Thompson evaluated the onsite search function of 23 B2B eCommerce websites across 22 criteria. For each website, the criteria were assigned scores between zero and one, with a score of one indicating full availability of the feature. Eleven B2C eCommerce websites were also scored using the same methodology. The 22 criteria were further grouped into 6 search function clusters: filters, suggestions, semantic search, results page, product data optimization, and mobile search. The average overall scores of these clusters for both the B2B and B2C websites are shown below
This plot shows that the B2B eCommerce sites rated much lower on search function criteria when compared to B2C websites. Indeed, the B2C shops scored 1.31x higher in our ratings than the B2B shops overall. Because the end-users are the same among both types of buyers, it’s important that B2B sellers improve on their site search features, at the risk of providing an unsatisfactory user experience for buyers.
Each cluster of features contributes to a distinctive facet of search functionality. Seeing that the scores in all but one of these clusters are inferior to their B2C counterparts, B2B sellers must improve on features across all categories to present the most comprehensive user-anticipated experience.
The ability to narrow down choices from a large product selection is key to making sure that buyers do not miss the products that best fit their needs. This is likely why a recent survey of eCommerce sellers listed the implementation of search filters as the addition that had one of the biggest benefits to their businesses. Filters are typically well-implemented on B2C commerce sites; the high ratings within the filters cluster for the B2C websites scored in our study support this idea. The B2B websites, on the other hand, were largely not up to par. Although most of these websites provided users with some sort of filtered or faceted search, many had poor execution of filter logic and functionality.
One of the most important attributes of a well-implemented filtering system is inclusiveness of most of the product selection within the filter options. In other words, if a search returns a total of 100 listings, the sum of all selections of any given filter’s options must also approximate a total of 100 returned listings. In this case, if a filter had two options, both only returning 10 products when selected, the total possible number of filter-returned listings is only 20, compared to the 100 products that might be relevant to the initial search.
Unfortunately, over 60% of the B2B websites evaluated had filters with options that returned less than a quarter of the initial search results. Filters should narrow down search results to show the user the most relevant listings; their use should not omit certain listing entirely. A possible cause of results being excluded in this way could be the improper tagging of items for the established filter choices. B2B sellers must fix this issue with their search filters if they want filtering to be helpful, rather than obstructive, to their customers.
Search suggestions, or autocomplete features, help guide users to the products that best fit their needs. This feature helps users find items whose names they might not fully know, showcases products with similar names that a buyer may be interested in, and improves efficiency and speed when searching.
In our research, search suggestions were simply available on almost every site scored. However, specific characteristics of strong suggestion functions were missing from most websites. Product images next to the suggestions can be very useful for searchers who use search suggestions as a way of understanding the search term that best fits their product need. A visual that matches a customer’s idea of what they want to buy can be a very simple addition, and yet it has the potential to move a user much further along in the purchasing process. Unfortunately, this feature was missing from 74% of the websites in our study.
Another beneficial – but often overlooked – factor of search suggestions is the call to action. For searchers who already have a conception of what they want to purchase, including an “add to cart” or “add to favorites” link next to the product suggestions can improve the efficiency of the online shopping experience. Only 4% of the companies we scored had this feature implemented on their website. B2B buyers who routinely purchase the same types of products for their business may convert at a higher rate if these options are made available immediately from the search box, so it may be worthwhile for sellers to consider adding this small feature to their site search.
The most basic search engines operate by returning results based on exact matches to keywords in the query. However, customers might always use the exact same wording to describe a product as the seller. For example, a user might search for a “mobile phone” with the intent of purchasing an item the seller has listed as a “cell phone.” If the search function can only return products with an exact keyword match, it will not be able to provide the user with the correct listings. The customer might then choose to abandon the site completely under the assumption that the site does not stock the product they’re looking for.
Semantic search refers to the ability for a search engine to understand the meaning behind the words in a query. Synonym support is one of the most fundamental factors in this cluster of criteria. Search functions must be able to support searching for synonyms to avoid these issues. Words that are used colloquially to refer to the same items should also return the same results when input into site search. B2B commerce websites often overlook this important feature, with 56% of the websites scored only returning results for searches that exactly match keywords in the product names or descriptions. A listing that cannot be found is a listing that will never be purchased.
The criteria within the results page cluster have to do with the presentation of products and information after
a search is submitted. These features include the sorting of listings by multiple parameters, automatic redirects to categories or content, and the results of searches for non-product content.
The cluster also includes the zero-results page, which provides guidance when the search function is unable to return any result. When a user is not offered any content that is relevant to their initial query, it can be a frustrating experience that may push some customers to abandon that site completely. Given that 90% of B2B customers research 2-7 websites before making a purchase, it is extremely important that zero-results pages always offer users a way to move forward instead of providing reasons to switch to a competitor’s website.
Zero-results pages can be helpful rather than harmful by offering search tips, category links, product suggestions, and customer support contact information. These are all simple additions that can have an impact on conversion rate, yet almost 80% of the B2B websites scored in our study did not offer an adequate zero-results experience. All these sellers are putting their customers at high risk for site abandonment should they make a search that doesn’t return results.
Another important, but often overlooked, aspect of the results page is non-product content results. This content can include information pages, such as return policies or account information, supplementary product information, such as manuals and safety sheets, as well as marketing content, such as blog posts. Of course, the most apparent goal of e-commerce websites is to showcase the products being sold, so it’s no surprise that product categories typically receive the prime navigation spots. Although this practice makes it easier for a user to find the products they want, it comes at the expense of convenient access to non-product content, which is consequently relegated to smaller, less visible sections of page layouts and may be difficult for many users to locate.
A simple compromise is to support searches for non-product content. Users will be able to easily search for the information they’re looking for, and the aesthetic minimalism of navigation and landing pages does not need to be sacrificed to do so. Customers have even begun to expect this feature on e-commerce sites, with usability testing showing that 34% of onsite searchers attempt to search for non-product content.
Many B2B websites, however, have failed to provide their users with this feature; our testing revealed that almost 40% of the B2B commerce sites scored have no ability to perform content searches at all.
Mobile usage in the B2B purchasing process is becoming increasingly commonplace, with 80% of B2B buyers using their mobile devices to research products, and more than 60% reporting that mobile played a significant role in a recent purchase.
On mobile websites, the search function is often even more important to users, as smaller screens may make it more difficult to navigate websites by browsing. Our research showed that 27% of the websites scored had underperforming mobile search experiences. Therefore, it’s vital that these sellers optimize their mobile site search to avoid missing out on opportunities with smartphone and tablet users, who make up the majority of B2B buyers. Companies should offer the same optimized search features that are available on the desktop versions of their websites, while modifying the interface to be user friendly on mobile devices.
Product Data Optimization
The criteria in the previous clusters focused on the improvement of search functionality and experience. However, the completeness of the product data is just as important for a good search experience. Even the most robust search engine with every feature in place will not be able to return results if the listing data is lacking. The product data optimization cluster encompasses the criteria that have to do with ensuring that product data is exhaustive, so it doesn’t hinder search user experience in any way.
One of the most important components of this category, especially for multinational B2B companies, is the localization feature. Localization refers to the process of adapting a website and its data to the correct language based on the locality of its users. For onsite search, this also means that product data must be fully translated into the correct language, so listings are not excluded from search results if they contain data that is in a different language from the initial query. Of the websites in our study that presented their products to different localities in different languages, more than 65% had some sort of issue with the languages of their product data, whereby listings were omitted because of the language mismatch. This is a huge problem for any international company that should be addressed, as whole target markets may be unintentionally excluded from purchasing certain products offered.
Across the board the site search of B2B platforms is not up to par when looking at the rising expectations of their users. The end users already experience the high standards that are offered by B2C Commerce platforms in their personal life and bring those experiences when judging the performance of the B2B platform and its site search.
The positive side is that most of the missing or basic features can be introduced or extended quite easily as they come out-of-box with the platform. They ‘simply’ need to be configured and as such will not cost too much. The biggest pain point for a lot of companies is that they did not go through a digital transformation and as such the product data is either missing or inconsistent or not suitable for the online purposes. Although the investment (time, energy and financially) might be substantial, the benefits over time will be much higher.
Note that these improvements are going to provide your platform the foundation it can build upon towards the future. Once the foundation is in place it will open doors towards more advanced features like personalized search and product recommendations (based on historical and behavioral data in combination with Machine Learning).
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