The man behind A*STAR chatbot AI startup—INTNT.AI—explains why knowing what customers care about is the secret to operating successful chat and voice bots.
Leveraging best-in-class AI technologies, A*STAR spinoff INTNT.AI has remarkably boosted chatbot customer satisfaction by three times for financial services companies. Manuel Ho, CEO of INTNT.AI explains why he’s so passionate about a customer experience future powered by understanding buyer intent.
In the last decade, fuelled in part by a global pandemic, the business has gone online. Increasingly, competitive advantage has transitioned from differentiated products and services, towards customer experience, brand sustainability promises, and pain-free user journeys. In this rapidly evolving digital story, a fundamental challenge facing all businesses is how to turn buyer interest into commitments and, having purchased a product or service, how best to empower and personalize services so that customers enjoy the anytime, anyplace, self-service access they increasingly demand.
Inevitably, firms are eyeing up the potential of machines—in the form of artificially intelligent chat and voice bots—to transition human-in-the-loop customer service experiences to something more digital, with more longevity, that’s repeatable and monitorable.
The promise of chat and voice bots is to consistently intercede in inbound and outbound communications—across all social and web channels—to guide interested site and app visitors to the next logical stage of their buying journey (or enquiry). Bots are available 24/7 and, when suitably trained, can easily process questions from customers. This means the number of customers to be serviced at any one time is not limited to the availability of human customer service agents. Furthermore, with more than 50% of sales emerging from social media channels these days, the ability to provide customer journeys consistently across all marketing channels has never been more important.
And yet, the promise of chat technologies has so far failed to impress. Anyone who’s tried them knows, the average website chatbot experience is woefully poor, leading many execs to turn away from the technology until someone proves this new wave of innovation does more to contribute to customer satisfaction and results, than work against it.
AI innovator, Manuel Ho, argues that organisations under pressure to gain a competitive advantage through a stand-out customer experience might not have to wait much longer for the game-changing results they anticipate. “Chatbots have a notorious reputation. Many of them cause frustration for both the company and its customers. To create useful chatbots, they need to be trained to empower the voice of the customer. Chatbots must interpret what the enquirer wants at any point in time in their buying journey to satisfy their needs. We augment machine learning with products from the fields of linguistics, emotions and intent recognition to achieve this.”
Ho believes the X-factor component of this next stage in bot innovation will come less from technology, but more in the science of understanding what humans are thinking.
Ho explains, “We’ve been working for some time with the scientists of A*STAR a Singapore-based tech innovation factory. They’ve performed pioneering linguistics research that combines Artificial Intelligence (AI) and Natural Language Programming (NLP) to learn how words used in conversations signpost the underlying intentions of the enquirer. Put plainly, chat and voice bots have been able to read the text for some time, but understanding what humans are asking for has been beyond them. Interpreting text is limiting without an appreciation of WHY someone has engaged in the conversation. Mishandled 1intents, insufficient intents, and inadequate training are reasons why chatbots show poor performance and frustrate customers.”
A*STAR spinoff INTNT.AI has implemented an intent classification technology engineered from learnings that come out of A*STAR’s Institute for Infocomm Research (I2R). At the heart of INTNT.AI’s suite of tools is CrystalFeel™, an emotion analysis tool developed by A* STAR’s Institute of High-Performance Computing (IHPC). The software analyses how the customer communicates to quantify their emotions, mood, and attitude; applying unsupervised clustering to large groups of unannotated utterances at a granular level, to originate new intents. A deep learning model for multi-class and multi-label classification is trained by using algorithms to process human language data and extract meaning from it.
INTNT. AI’s trained chatbots offer more powerful AI functionality using machine learning that allows them to understand the unstated intent behind the vast permutations of possible user inputs. For example, INTNT’AI’s chatbots can learn that words such as ‘buy’ or ‘get’—are often associated with the intent to purchase. INTNT. AI’s clients have seen chatbot customer satisfaction grow by as much as 300%, chatbot fails decline by 85%, and requests to speak to live agents cut by 72%. In one client’s case, customers surveyed their experience with their chatbot before and after INTNT.AI’s improvements. The customer satisfaction scores went from 1 to 4, with 5 being the highest score.
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