As B2B customers take on complex online purchasing, search systems must evolve to satisfy them. Fortunately, innovation, machine learning and AI are helping to improve search outcomes, writes Kathleen Lewarchick, vice president of marketing at B2B digital services firm Xngage.

Kathleen Leigh Lewarchick_Xngage

Kathleen Lewarchick

With a goal of improving B2B experiences, distributors and manufacturers are leveling up their data, user experience (UX) and search capabilities. B2B end-user customers are maturing from simple to complex use cases, and online commerce systems must evolve. Search systems, in particular, are becoming even more sophisticated.

This smart search system knows what products users bought and what they considered during their last encounters.

Moving From Good to Great Search

Standard B2B search functionality provides ranked results from a basic search query. In more complex B2B use cases, search must also support advanced ecommerce needs, such as:

  • Relevancy – incorporating personalization, like geography or customer buying roles.
  • Boost and bury – highlighting the optimal items, while also downplaying less optimal ones.
  • Product-restrictive views – setting permissions so buyers have a focused, allowable assortment.

Challenging? Sure. But innovation, machine learning and AI are helping to improve outcomes. For B2B leaders, that means customized shopping lists, optimal merchandising, and increased conversions.

How Smart Search Systems Work

As companies outgrow standard out-of-the-box search functionality, newer smart systems use stored behavioral data from previous experiences: when authenticated users return, their history is attached. This smart system knows what products users bought and what they considered, during their last encounters. The system is primed then for improved search results.


The system might, for example, pull standard product data into a commerce-and-content Admin Console. Adding behavioral shopping data via “Merchandising Views” (say, a 360˚ rotation or part number link) will unlock greater buyer potential. More sophisticated systems use data accelerators, commonly called connectors, to improve data flow into the commerce system and back into the ERP. Both systems get smarter with every use.

Consider your own B2B shopping behaviors. If you previously searched for 1/2” drill bits and the system knew the PDPs (product detail pages) you reviewed before making your last purchase, that’s powerful data to incorporate into your next ecommerce journey for similar or look-alike items. And if your view was restricted because of your role or budget, having an “acceptable” shortlist is immensely helpful. It would not only save time in the search process, but it would also drive greater click-to-cart conversions.

Search and product discovery are increasingly important in B2B ecommerce. Artificial Intelligence can help create smart recommendations that act as an assist for your customers. And coming soon, AI will help companies manage multiple ecommerce storefronts and provide greater customization options.

Kathleen Leigh Lewarchick is the VP of Marketing for Xngage LLC, a B2B digital commerce services company with more than 60 clients across the industrial trades. She is the former PURELL® Hand Sanitizer Brand Director, has co-created automated replenishment products with Amazon Business, and created telehealth solutions for a company that she later helped sell to CVS Health. 


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