It is very common to have your customer service requests answered by a chatbot in almost every service business these days. As you can imagine, even in the customer service industry, answering the same questions over and over again becomes a little monotonous, so smart techies created a clever little automation system that responds to certain keywords used and then replies with an automated response triggered by those words.
This system allows you to free up your customer service agents to only deal with the meaty stuff.
How does it work?
Chatbots are generally considered at the lowest form of machine learning as it is a rule based system rather than true learnt “intelligence”, or artificial intelligence. However the two are not mutually exclusive.
The more sophisticated chatbots use Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) systems.
The most common chatbots present on websites etc are taught possible user questions using templates that have quantifiers in the description language of dialogs, similar to using regular expressions. The process is very narrow as the bots can only work within the parameters they have been given.
More sophisticated bots are trained with big data to guide a use through a particular journey, as opposed to staying within set parameters. Great examples of chatbots using machine learning and NLP include therapists, chatbot lawyers and chatbot educators.
The more complex systems currently are the likes of Amazon Alexa, Google Home and IBM Watson of course.
But is it a great experience?
It depends on how you set it up.
For the common chatbot experience, it can get really frustrating for a customer when they ask a question, but either it’s “taken out of context” by the bot in the human’s eyes anyway (yes we know bots don’t have *opinions) or it’s just not the right answer to their particular query, or you end up in a never ending loop.
The solution is to ensure that you have catered to most and near possible every outcome. Your little tree-map of possible questions (trigger words) and answers should not be that little to do this properly; but simple enough to create the automation process with your chatbot and not let it get confused with possibilities of multiple answers.
What is a good half-way meeting point is to provide the bot a “way out”. Keep the process simple enough, that it can default to “forward to a human when” scenarios reach a certain point.
Using automation such as chatbot systems is a great way for companies to be more efficient. The tricky part is to ensure that you’ve set it up properly. Using automation or software experts that take the time to really delve into and understand your business processes and CRM system to help guide you through the best set-up or creation of these systems will be your best bet in creating an automated customer service process that works for your team and your customers.
