In today’s time-starved world, retailers and brands are just one of the many industries competing for consumers’ attention on a daily basis. And as more and more shoppers experience increased time constraints throughout their day, the ways they search for items they need to buy online in that instant will become increasingly specific, in much the way they shop in an actual store. As Karen Katz, former CEO of Neiman Marcus Group said recently at the 2018 NRF BIG Show, “The next frontier is trying to figure out how to replicate the in-store experience online. Really try to bring a human touch to online so consumers feel they have a connection. As online grows to plus 40 percent, it’s very important to have a human touch.”
While a majority of e-commerce retailers have implemented a strategy for the online search offering as part of that “human touch,” such as brand, price and ratings, shoppers are often looking for a more enriched experience during their online journey. As such, retailers need to ensure shoppers’ search queries lead to the right product, whether a shopper has a particular product in mind or is simply browsing. Giving shoppers the ability to have access to specific searches is not only critical to fulfilling consumers’ needs and having them return to a website, but can also lead to increased spending and more profit for the retailer.
To help shoppers find what they're looking for, implementing multiple search option capabilities within categories can help refine product searches even further. For example, a shopper might want to look at a specific brand of jeans, or only brown shoes instead of any other color. Through increased search precision, multiterm results can be restricted by the identified attributes. This allows for a retailer’s products to be properly detailed and appear in front of the shopper. If a specific search doesn't bring back the ideal specified results, shoppers may become annoyed with the lack of proper results and abandon the site, leading to a loss in sales.
Natural Language Preferences
When a shopper's time is incredibly valuable, increasing the ability to input specific search preferences will lead them to find an item more quickly. However, more often than not, consumers enter search terms using the natural language they communicate on a daily basis, which doesn't always match data in a product catalog. By using a direct semantics search, traditional keyword-based searches and more complex natural language searches can be supported. Therefore, if a shopper searches for a “cheap dress,” the results will be sorted as “Price: Low to High.”
Shopping by Concern
Additionally, providing search preferences that include product capabilities can deter a shopper from choosing to search products just by price. For example, our research indicates that allowing consumers to shop by concern can help lower their price sensitivity and consider other more important aspects of a product. For a category such as jackets, for example, the ability to search “staying dry” or “keeping warm” can drive customers to consider the more important critical features of the product. Ultimately, by eliminating price sensitivity, retailers can raise the average order price — and typically gross margin — by 80 percent or more.
Today’s shoppers are searching and finding the perfect products faster, which means retailers must be innovative and efficient in their search optimization to put the perfect product in a shopper’s path. Offering specific search preferences can satisfy the time-limited shopper and provide more detailed products to someone who is browsing or in an information-gathering stage. However, implementing confusing or limited search results can lead to frustration and site abandonment. By creating search terms in which shoppers can specifically and successfully look for products, retailers can see an increase in customer satisfaction and profits.
Roland Gossage is the CEO of GroupBy, Inc., a data-driven e-commerce platform with data enrichment, advanced search, merchandising and analytics. (Read More)