For anyone new to the notion of machine learning, the idea of using fully automated solutions to handle much of your customer workflow can seem a little … well, unsettling.
Considering the horror stories that many customers face when trying to use robotic 800 number menus to reach call center representatives – or the increasingly impersonal and sometimes impenetrable digital gauntlets they have to battle to find some online solutions – you too may feel a little skeptical about business-level artificial intelligence having a real upside for your own customers.
The good news is that by integrating machine learning’s predictive analytics tools into your operation, you’ll still be able to maintain the personal touch your customers expect. The secret is you’ll be able to do it in a more efficient way, allocating resources to the calls and emails (tickets) that really require it.
Consider the volume of relatively straightforward requests or questions that don’t actually need a human touch, but still require your customer service team to manually process. By integrating an automated solution to deal with those easy, repetitive tasks, your professional staff can have more time to focus on the more complex cases.
And for your customers, that’s not a one-way ticket to impersonalized service. Rather, the ability to sort that incoming data and spot the patterns contained in their requests will better allow you to address their real needs and intents, instantaneously. For easy-to-handle issues, a courteous email or digital response will likely suffice. If the request is of a more complicated nature, machine learning can escalate the issue automatically and get the customer the human help they need.
Machine learning is able to focus in on the heart of the issue and then, with a high level of confidence, choose the appropriate response. Furthermore, it can provide a measure of how confident it is in the response, so that you can set thresholds on how automated you want the response to be. As a result, the system can drastically reduce the volume of tickets that agents are required to process, concentrating instead on the cases that truly do require a human touch.
Your agents might actually be the biggest supporters of a workflow optimized by machine learning, as the most routine requests can be fully automated, rather than burdening them with another load of busywork. Machine learning can also help them do their jobs better by picking out the support representative most suited to a more complex ticket and letting their customer service skills shine.
In the end, the adaptation of machine learning can help reach two seemingly incompatible goals; allowing customer service teams to deliver high quality - at a scale that was unreachable before. By concentrating energies on more detailed and complicated tasks, customer service representatives have the opportunity to demonstrate their skills and ultimately provide the best quality results to the clients who really need it. At the same time, the simple requests can be handled automatically, upping the overall quantity of tickets handled. And that might be the best of both worlds.