February 9, 2023 by Shane Connelly | 6 min readRead Now
Natural language processing can ease common frustrations while delivering relevant answers and unexpected insights.
November 28, 2022 by Ed Albanese
Recent advances in natural language processing (NLP) powered by neural networks and LLMs enable organizations to bypass common search frustrations and offer an optimized customer experience. By adopting this groundbreaking technology, leaders can reap the benefits of streamlined customer journeys and workflows while opening the door to new business models. As consumers shift toward online purchases, they increasingly expect highly relevant, instantly available results. And they even more frequently click away at the first inconvenience.
The difficulty customers face when trying to find the correct information stems from keyword-based search systems. These tools match words from a customer’s query with labeled product attributes or indexed data and then rank the results based on how other searchers interact with the content. This approach works for simple searches but loses precision as requests become more complicated or more detailed.
Neural network-powered search technology, which leverages NLP to help computer systems understand concepts and context as humans do, delivers relevant content that corresponds with the underlying meaning of the query. Rather than scanning for matches in a manually-populated database, neural search systems map the relationship between ideas by converting the complete end-user query into numeric vectors and clustering similar concepts together. Even when someone misspells a term or uses acronyms, the system can easily detect the semantic meaning and deliver accurate results.
Neural search does more than solve common customer pain points. The technology radically reimagines the search experience, whether users are combing through massive public websites or looking for obscure data points in enterprise databases. It’s a new way of seeking knowledge with thousands of use cases for consumers and businesses.
Just as eCommerce disrupted brick-and-mortar, neural networks will forever change how people make purchases. Keyword-based search isn’t attuned to how customers make queries today; they use words in natural language, in different combinations, or with varying contextual meanings. These requests often return irrelevant results on eCommerce stores and other business pages because they don’t precisely match product labels. For example, looking for a “teal shirt” may not turn up related colors or patterned shirts.
Of course, many customers don’t know exactly what they want. That’s why recommendation systems are critical for customer conversions. But these features still miss the mark, often because search algorithms employ collaborative filtering that prioritizes the most popular items at the expense of other options. The definition of a “blue work shirt,” for instance, may vary depending on the gender, profession, or interests of the buyer, but most sites will show office-friendly collared options. Google estimates that businesses in the U.S. forfeit up to $300 billion annually because shoppers can’t navigate their sites.
Neural search recognizes that those looking for a teal-colored garment may also be interested in similar hues and understands the distinctions between categories of related products. What’s more, neural search provides a better overall product discovery experience because these systems consider the entirety of customer data and product attributes, including non-text information and unstructured data like product reviews. It’s the difference between browsing a catalog and talking to an informed, experienced salesperson.
The challenges associated with keyword search extend to every stage of the customer experience. For example, many companies tout their chatbots as hassle-free ways to solve common complaints. Yet until recently, these conversational agents have been only marginally more effective than a simple Ctrl-F search of FAQs, primarily due to their reliance on prior language configuration, such as synonyms, stop words, and stemming rules. Surveys show that 50 percent of consumers believe chatbots make it more challenging to resolve issues.
Neural search is creating a new class of conversational agents that derive meaningful value from the complex web of support chat and call data in their knowledge base. That means next-generation chatbots consider the context when surfacing solutions, such as a customer’s geographic location. Because neural search goes beyond hard-coded rules, it can answer uncommon questions by making predictions based on embeddings of past queries and interactions.
Of course, humans have to solve trickier requests, but NLP technology will help them align their responses with customers’ needs. First, AI can route calls toward the agent best suited for the task. Once the human agent takes up the ticket, support tools with an NLP backend can summarize possible solutions and provide real-time sentiment monitoring. These neural search tools promise to turn a cost center into a revenue driver through positive brand experiences, competitive differentiation, and nuanced relationship building.
Chatbots and AI-supported human agents are delivering more intelligent customer service. But neural search can help customers go a step further, toward self-service that allows them to quickly solve their own problems, no matter how they ask or even the language of the query. These solutions significantly reduce the number of agent interactions or support tickets altogether.
Neural search directs customers to the exact location in a piece of content that answers their questions. This helps avoid the cumbersome task of scrolling through PDFs or videos. And while we all want to find recipes faster, this functionality is revolutionary in enterprise contexts. Business users sometimes navigate between hundreds of SaaS applications and waste significant time simply searching for information buried in technical documentation, training materials, product pages, community forums, and other unwieldy databases.
Helping reduce the burden of customer service is only one use case. For example, NLP solutions can also make sense of the other kinds of data and metadata companies collect and produce self-service analytics to inform decision-making on everything from finance to marketing. By identifying the relationship between entities in a knowledge base, neural search can generate actionable insights before users even think to ask the question.
The race to provide a superior search experience is on. Search leaders like Google and marketplaces like Amazon are devoting significant sums and talent to bringing neural capabilities to their products. Meanwhile, everyone else has been stuck making the best of traditional search, which still requires intensive effort to implement and maintain.
Fortunately, advanced NLP content discovery is now available to nearly every organization. Turnkey solutions like Vectara are easy to integrate into existing tech stacks through APIs without any need for historical data, training, or machine learning expertise. With neural search as a service, small and medium-sized businesses can instantly serve more customers and engage more employees at a higher standard of quality. In a world where we demand instant, personalized responses and always-available support, neural search creates an unparalleled opportunity to impress, delight, and build lasting loyalty.