As customer/brand conversations increasingly start taking place via text, and increased datasets allow NLP bots to start handling more and more of the conversations, we are going to see the consolidation of “voice” within the brand. Rather than having hundreds or thousands of low-paid customer support agents, each with their own personalities (and susceptible to human emotions), the call center staff will be greatly reduced to expert bot managers. The NLP engines that replace the workforce will be trained with a common dataset, and in that dataset will be a consistent corpus of creative content representing how the brand wants to project itself to its consumers.
There are two ways to interact via the web. Passive interaction leaves it up to the visitor to initiate contact with your brand. Proactive interaction initiates the contact with the visitor. When a brand initiates contact with the visitor; for example, asking the visitor if they need assistance, the majority of visitors will respond. The evolution of artificial intelligence combined with unlimited resources of bandwidth, processing power, memory and space are going to put chatbots front in center for marketers who are interested in proactive interaction.
There are many use cases where chatbots, as an example of CUIs, have the promise to make things easier for us, especially when it comes to consumer-to-business communication or conversational commerce. After all, who in 2016 wants to download an app for every new vendor they do business with? For chatbots to really reach their potential, all of our service providers — restaurants, banks, hospitals, pharmacists, airlines, florists, lawyers, etc. — need to provide APIs for chatbots to tap into.
While chatbots have been around since the early 90’s, this year they became completely synonymous with Facebook Messenger. Facebook brought automated, chat-based customer service into the mainstream at a time when businesses and buyers are obsessed with improving the customer experience. The author believes that businesses hopping on the bandwagon and directing all service interactions to Facebook will soon regret that decision.
Using differentiated instruction, students would be grouped based on reading ability and then given different assignments. While this seems ideal for learning - it is very resource intensive in a time when resources to schools are scarce. A free chatbot that could be accessed on classroom or library computers would allow a teacher to group her students based on reading ability, but the chatbot would customize the instruction for her while she circulates around the room, providing individual support.