You Understand What Your Customers Want. Does Your Search Engine?
Understanding what customers wanted was pretty simple in a physical store. They could tell a store associate, “I need a coat for a ski trip,” or simply point to an item to say, “I need something like that.” With the introduction of online shopping, the e-commerce search bar became the store associate. A customer simply typed “coats” and waited for the site to do the rest.
For a while, that approach kind of worked. Product catalogs were smaller, customer searches were more direct. E-commerce search engines weren’t always perfect, but they were good enough, and that was sufficient for retailers and shoppers alike.
As time passed and online product catalogs grew, customers began searching differently and e-commerce grew to the dominant channel it is today. It became clear that “sufficient search” wasn’t enough. It was actually becoming a problem.
More Than 'Sufficient'
The issue with e-commerce search today is that it doesn't really understand what customers want. Even in a world of “intelligent” search solutions, most e-commerce search engines are only capable of ingesting keywords from a customer’s search and returning matching results.
This lack of understanding creates frustrating customer experiences, like when you search for a “shirt dress” and instead get 100 results for “dress shirts.” These small frustrations add up across a customer base. Over 40 percent of retail shoppers go directly to the search bar and are 2.4 times more likely to purchase than someone who skips the search bar. When consumers don’t find what they need — or don’t find it fast enough — they quickly move on. Multiply one missed sale by millions of consumers. Just how much has “sufficient” search left on the table?
Context is Critical; AI Can Help
To move beyond “sufficient,” retailers need an e-commerce search engine able to understand context. This capability is becoming particularly important as customer searches grow more nuanced and younger generations search with language that matches how they speak.
Semantic understanding is search artificial intelligence (AI) that determines intent, in essence deciphering the “why” behind a customer’s search. This is critical in understanding nuance: that vanilla can be a product or flavor or that a “large battery pack” differs from a “pack of large batteries.” With context fueling every search result, retailers can deliver on another critical element of the search experience: personalization.
Customer Experiences Built by Customer Data
If context is driven by understanding the “why” motivating a customer search, personalization is driven by an understanding of “who” is really searching. Today, e-commerce search engines figure out who is searching by using cookie-based data, which is limited, or the data of visitors who have logged in, typically a small portion of site traffic. That approach, however, means a large portion of retailers’ traffic actually gets a pretty generic experience.
By connecting the search experience with customer data — particularly real-time data — retailers eliminate generic experiences. They can group customers into segments, then create product grids filled with highly relevant search results for each. From the start, every customer receives a more relevant shopping experience based on their segment, even anonymous visitors. And as they continue to shop, the experience grows more tailored to their identity. Ultimately, it’s backed by enough data to create the one-to-one personalization that all retailers aim to offer their customers. It’s a better experience from start to finish.
Enter: Generative AI
Of course, that experience will only improve with the recent innovations in generative AI — and context and customer data will play a key role in enabling that improvement.
Generative AI is about to drive a seismic shift in e-commerce. Retailers have heard about conversational commerce for years, yet its promise has long remained unfulfilled. With generative AI, e-commerce search has the ability to become truly conversational, offering customers a two-way experience and allowing them to speak to your site as though it were an in-person store associate.
But this type of AI won't reach its full potential for retailers if it’s not trained on the context of their unique product catalog or their customers’ real-time data. Generative AI can respond to a query asking, “Can you find me a white vest in my size?” and likely give a good response. But without context, will it understand most of your customers are shopping from England, and by “vest” meant “tank top”? Without customer data, will it know their size? Or that a larger size would be a better fit based on their recent returns?
On its own, generative AI can certainly deliver a better experience for shoppers. However, it’s the addition of context and customer data that creates a better experience for your shoppers, tailored to their unique needs and your unique business.
It’s time for retailers to stop settling for “sufficient” search. Driven by better context and a true understanding of the customer, e-commerce search can transcend “sufficient” — and become pretty magical, actually. And that type of experience opens the door to e-commerce that drives better results today, tomorrow and in the new era of AI-driven commerce just around the corner.
Raj De Datta serves as co-founder and CEO of Bloomreach, an open-source web content management company.
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Raj serves as co-founder and CEO of Bloomreach. Before launching the Company in 2009, Raj was entrepreneur-in-residence at Mohr-Davidow Ventures, served as Cisco’s director of product marketing, and was on the founding team of telecom company FirstMark Communications. He also worked in technology investment banking at Lazard Freres. Raj serves on the Council for Player Development for the US Tennis Association, as a Founder Partner at seed-stage venture capital firm Founder Collective and an individual investor in over 20 Silicon Valley start-ups. He holds a BS in Electrical Engineering with a certificate in Public Policy and International affairs from Princeton University and an MBA with distinction from Harvard Business School.