2016 saw endless chatter about chatbots, some good and some not-so-good. The year may be remembered as “the rise of the bots.” Nuance posits that 2017 will mark “the battle of the bots” – a year which will begin to separate “good” bots from the “bad,” when some bots will succeed while others fail, and during which best practices will be established.
Brands, vendors, industry analysts and experts share a common view that bots are here to stay. Gartner even predicts that by 2019, 20 percent of brands will abandon their mobile apps and that by 2020, the average person will have more conversations with bots than with their spouse. But, not all bots are created, or perform, equally.
What does Nuance consider the critical success factors of building a “good” bot? It all comes down to customer experience. This sentiment is echoed by Forrester Research: “Today’s customers reward or punish companies based on a single experience — a single moment in time. This behavior was once a Millennial trademark, but it’s now in play for older generations. It has become normal.”
According to Nuance, successful bots require:
Conversational artificial intelligence
To be successful, a bot must be capable of holding an intelligent, two-way conversation with a consumer. Like a human, the bot must be able to maintain context as the consumer changes subjects or uses colloquial, conversational expressions and words. Today, most bots are not sophisticated enough to do this. Some might be able to successfully respond to one, basic inquiry, such as, “What is the temperature in Miami?” (answer: “It’s 80 degrees in Miami.”), but if the consumer follows up with the conversational, “How about Beijing?” most bots today cannot maintain context that the question is about the weather.
Cognitive artificial intelligence
This is the reasoning side of the bot’s brain and its ability to take action and even predict a customer’s needs. Whereas traditional speech recognition systems understand what people say, today’s sophisticated natural language systems understand what people mean and want to do. Both speech recognition and natural language understanding rely on big data and on having “big knowledge” of domain-specific customer intents.
Human assisted artificial intelligence
This is what Nuance calls supervised AI. By using human customer service agents as partners with bots, machine learning is accelerated and, importantly, bots learn “the right things” from humans rather than learn on their own and potentially make critical judgment errors that make news headlines like we saw in 2016.
Successful bots will not be standalone applications, but rather a set of common tools that operate like a central cognitive brain and which can be deployed across all of the channels consumers use – messaging applications, mobile applications, phone systems, Web, chat applications and social media. An integrated, omnichannel strategy will ensure customers have a consistent experience regardless of the channel they use. It also reduces the cost of siloed technology stacks for brands.
Intelligent authentication and security
Voice biometrics allows consumers to easily and naturally authenticate their identity without having to type in a password or PIN and by simply speaking a short passphrase such as “My voice is my password.” This eliminates the need for hard to remember PINs or worse, the need to answer a series of security questions such as “What was the name of your best childhood friend?” or “what was your most recent transaction?” Furthermore, voice biometrics significantly improves security over legacy authentication methods and fraud.