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Unlocking customer interests with intent-based AI

Are you able to strike up meaningful conversations with your customers at every touchpoint? Modern day customer engagement has expanded from basic interaction to understanding the intent and motivation of the customer, along the customer journey.

Brands that can pull this off are carving out a competitive edge for themselves in today’s customer-centric marketplace. A recent survey found that consumers wouldn’t care if 74 percent of the brands they engage with, simply vanished.

To avoid becoming disposable, companies must take an encompassing approach to meet their customers’ needs in real-time. Companies need to formulate meaningful insights from the data they gather from customer interactions to drive results throughout the organization.

The ideal customer experience puts customers at the center and connects their journeys to actionable business outcomes and operational transformation.

Customers today possess numerous choices and have higher expectations more than ever. They demand an unparalleled experience that is predictive, personalized, frictionless and instantaneous. This can be achieved with intent-based AI, which is driven by a powerful combination of big data, machine learning, and intelligent automation. It enables organizations with cognitive self-service platforms to continuously elevate the customer experience and operational efficiency.

Why is intent-based AI deemed to fuel the next wave of customer engagement?

Intent-based AI can enable specialized service desks to elevate customer support to the next level, allowing for pre-emptive, relevant, frictionless and personalized customer engagement. Traditional customer service processes lack this level of contextual thinking and cognitive intelligence. With intent-based AI, companies can make more informed decisions and move towards a business model that is AI-powered.

Fundamentally, intent-based AI is its ability to capture and interpret behavioral and conversational data to uncover the “intent of customer,” which says what customers are trying to achieve.

A use-case for this could be a user asking the digital assistant on his phone what the weather will be tomorrow, to which it responds with the forecast in his location. The user then asks, “how about the day after?” Though the user didn’t explicitly mention the weather in his query, the context is based on the conversation up to that point. A digital assistant using intent-based AI should remember the recent context of this conversation and be able to answer appropriately.

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Natural language understanding (NLU) and natural language processing (NLP) must be included in the AI architecture to process unstructured data, like language. By feeding with labeled data that provides known intentions and example sentences corresponding to each, AI can classify the intent of each customer interaction.

Once trained, the model can categorize new sentences into one of the predefined intents. Additionally, entity extraction allows for the recognition of key information pieces—time, place, names, etc.—in a given text, providing more context.

Over the next five years, advances in AI will make conversational capabilities of computers more sophisticated, paving way for a sea change in computing. In fact, Gartner reports by 2021, 15 percent of all customer service interactions will be completely handled by AI—a 400 percent increase from 2017.

The treasure trove of customer insights from a contextual process facilitated by AI can improve responsiveness and effectiveness of a company’s customer service function and enhance its top line and bottom line. But first, the machines must master that one critical element of all effective conversations: intent.

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