How Advanced Site Search Delivers a True Omnichannel Shopping Experience

As consumers return to in-person shopping, businesses everywhere are moving quickly to include the best parts of physical retail in the online customer journey. But retailers looking to take features from brick-and-mortar shopping and port them over to the online experience are thinking about it in the wrong way. Success in this new age of retail isn't about borrowing from in-person and adding to online — it's about breaking down the barriers between those two spaces to create an integrated, omnichannel shopping experience.

As a former colleague of mine used to say, there isn't in-store commerce and online commerce. There's just commerce.

Of course, building a truly integrated experience is easier said than done. One of the key challenges is building the technological means to reliably and efficiently answer customer questions online. In stores, shoppers can easily get trusted information from on-floor sales reps, but most retailers have failed to extend that experience to the digital space. A recent analysis of 150 top e-commerce websites found that 42 percent of on-site search engines perform below an acceptable UX threshold, and nearly one in 10 are "downright broken."

Shoppers who can't find the products they're looking for will take their business elsewhere. Fortunately, artificial intelligence-powered technology is enabling retailers to deliver reliable answers wherever people ask questions about their business, building continuity between in-store and online shopping and delivering a true omnichannel experience.

Salespeople have always provided the in-person shopper with an easy, efficient way of gathering trusted information about a product. Replicating this human ability to quickly answer complicated shopping questions in a digital space poses a significant technological challenge.

Relevant product information is frequently siloed in various databases and applications or locked in unstructured formats, making it inaccessible to traditional search engines. A 2022 study by the Baymard Institute found that roughly a quarter of top e-commerce sites require users to search by exact product terms, meaning a user searching for "blow dryer" might not see a product labeled "hair dryer." And nearly half of sites don't support symbols or abbreviations, so a shopper using the search term "inch" might not see a result that uses "in."

Consumers who can't find the information they need on-site are forced to scour Google, YouTube, social platforms, and other digital directories, lengthening the shopping funnel and stunting conversion rates. And by sending potential customers to third-party sources, companies lose control of the information shoppers receive about their products and brand.

Fortunately, emerging technology is enabling machines to process and deliver information in a more intuitive, human way.

This begins with knowledge graphs, which structure complex and disparate data sets in a way that allows AI systems to draw connections between different things in the world around us — including different products in an online store. Powered by this brain-like technology, a search engine can process the query "best jackets for fall jogging," build associations between those terms, and deliver product results.

Pairing knowledge graphs with natural language processing (NLP) technology — which allows machines to "read" and process unstructured data like whitepapers or product descriptions — unlocks previously inaccessible information that can be delivered to shoppers.

Tech giants like Google and Amazon.com have been using knowledge graphs and NLP to power their search technology for years, but only recently has this technology become accessible to the wider retail community. Yext's search product, for example, is a usage-based solution designed to be accessible to enterprise clients and small and midsized businesses alike.

And while local retailers can't reasonably expect to necessarily beat out the likes of Amazon when it comes to advanced search technologies, they do hold a secret weapon: brick-and-mortar stores. Aside from groceries, Amazon has yet to offer physical retail outlets at scale, meaning it can't yet leverage local searches like "furniture near me."

By creating SEO-friendly landing pages optimized for popular local search terms, retailers can attract local customers looking for an integrated in-store to digital experience, then give them an advanced search tool similar to Amazon.

Here we've arrived at a truly seamless retail experience. A shopper searching for "furniture stores near me" arrives on a brand's custom landing page, where they use the advanced on-site search function to gather information on "metal frame full daybed" and find the product they need. They can then go into the store to speak to a sales rep and examine the daybed in person. Or, say, their kid's soccer practice runs late and they don't have time to make it in-store, they decide to order the daybed from the website.

For as much as the term "personalization" is thrown around in retail these days, there's a fundamental misunderstanding about what's meant by the term. Personalizing a shopping experience isn't just about delivering automated "you might also like" and "frequently bought" offers, which are often used as a crutch for poor on-site search technology. These suggestions can be valuable tools at the right time — in the cart or post-purchase — but a search query should deliver a precise answer.

Today's consumers are savvy enough to ask the right questions; brands just need to be savvy enough to answer them. Whether in-store or online, true personalization is about providing shoppers with the exact information they're looking for to reach a purchase decision.

By delivering the right answers at the right time, advanced on-site search breaks down the traditional barriers between in-store and online to deliver a seamless, consistent omnichannel experience.

That's personalization at scale.

*This article was originally published on Total Retail.