By Kumar Srivastava, who works in financial services building products that converge big data, analytics, cloud, mobile and digital
Chatbots have been getting a lot of attention recently through a combination of trends: advances in natural language processing (NLP), a resurgence of chat and chat channels (think Facebook Messenger, Slack, Hipchat etc), ubiquitous mobility and advances artificial intelligence — including the emergence of A.I. systems that are beginning to understand the intent of spoken or written words. In addition, consumer and user familiarity with this low-hassle, unintrusive channel makes chat a preferred mechanism for several interaction scenarios.
As enterprises look to capitalize on chat, an option that offers connectivity and always-on communication and interaction channels, they have to be aware of the maturity of the channel and balance that with the need to maintain the quality of customer interactions and experience. This is hard to do, and it can conflict with the allure of a cheaper-to-maintain and scale-out customer experience and interactivity channel.
Non-mature chat systems tend to be too chatty and can be detected quite easily. A chatty chatbot makes it harder for users to get the service they need. A chatty chatbot is unable to understand the user’s intent — or it may interpret the user’s intent incorrectly, leading to a repetitive and frustrating interaction.
‘Time to frustration’
“Time to frustration” becomes a key metric for designing useful and delightful chatbots. This is the measure of the time it takes for users to reach a point of frustration that turns them off from the chatbot — and, possibly, from the company’s product or service. Measuring time to frustration requires mechanisms that detect changes in the usage and interaction of a chatbot by a user in addition to post-chatbot usage activity and engagement.
Frustration can be detected, at a high level, by how a user reacts to the chatbot’s response. If a user resubmits his or her inquiry with slight changes, retries the same request or switches the channel of communication, it is a good sign of frustration. In fact, techniques used by search service providers to understand user queries and determine intent (and failure to do so) have a lot of relevance in the world of chatbots. This also means that excelling in understanding intent and responding to users’ needs with high quality requires a trove of good, highly curated content, interaction data that enables the chatbot to understand intent, and feedback loop mechanisms to measure and improve the quality of the chatbot.
Read the source article at CIO.com.