the empathic chatbot AKA M-Bot
why m-bots work
An Empathic Chatbot or “M-bot” is a simple, clear, otherwise generic chatbot, carefully and consistently scripted with dialogue that gives it the ability to project both cognitive and affective empathy. TCC believes that Empathic Chatbot or M-Bot could transform the way the Public Sector connects with its users, which just happens to be the entire population of England and Wales (Scotland, I hope you’re reading this).
M-Bots have a proven track record in the marketplace, building lasting connections with users in the very competitive therapy, self-care, and wellness category. And a big part of that success is down to the fusion of evolution and technology. We trust and share more with people who “get” our problems, and to a greater or lesser extent, that bonding instinct carries over to Bots, and reinforced by other evolutionary tendencies (such as our propensity to see human characteristics in animals or inanimate objects).
The combined impact of these empathic effects may be very subtle and gradual at first, but as users continue to interact with an M-Bot on public or private sector interface, these subtle changes build on each other, create their own empathic expectations which when met become tangible and real, especially when reinforced by matching empathic language, and a more personalised AI.
The obvious use for these empathic chatbots would be to take the pressure off hard-pressed public call teams, by filtering everything but the knottiest edge cases. But there are a million possible formats, ranging from digital friends for the lonely, to ideabots that people can talk to with suggestions for change, to HMRC bots, where a little empathy would be a long way.
Whatever the use case, the fundamental goal is always to have the citizen complete the conversation successfully. One more tax assessment filed, one more parking ticket paid, one more pothole reported, one more piece of user feedback delivered.
public sector m-bots always "get" the user.
There are numerous win-win, provider-side efficiencies and productivity benefits that come with a conversational layer, but the depth of those benefits depends on the consistency and quality of the bot, which in turn comes down to its ability to empathically connect with users over time. This is where the gradual approach to building empathy comes into play. The M-Bot takes its time developing an expectation-based loyalty with users. It probably needs a series of five to ten exchanges to develop the foundations of strong user loyalty. But once a connection is made there’s no telling how strong it could be.
Rick Blaine, "Casablanca" (1942)
The follow-up post to this one is a case-study of a hypothetical Conversational Strategy for Universal Credit, the UK Government’s mechanism to merge welfare benefits into a single payment.