Call center interactions have moved away from more meaningful interactions and more toward agent research. We know how it goes: we have a less-than-ideal phone call with a customer service agent after having been on hold for half an hour, then at the end of the phone call, we’re asked to leave feedback. Of course, that’s the last thing we want to do. We want to get off the phone call as soon as possible. Is the manual feedback requested at the end of a customer service interaction the best way of gauging performance and customer point of view? Intelligent automation seems to have a good answer.
What drives customer satisfaction?
Research into call center interactions has attempted to manually uncover what makes customers tick: by having them report exactly how they felt about their interaction after a call, reaching out for surveys, and so on. However, these types of feedback have not yielded much comprehensive or workable data.
The “feedback” a customer leaves at the end of a customer service interaction isn’t very reliable on its own. Where one person who might be dissatisfied might leave a good review no matter what, another might have a different style of ranking. It’s difficult to put numbers on a system without a reliable metric. Also, if any language barriers exist in the conversation, this makes the customer’s behavior even more difficult to analyze.
While those methods haven’t been effective, the advent of automation in the realm of customer service has started to uncover ways to pinpoint how customers truly feel.
How can automation analyze human emotion?
That’s probably one of the biggest curiosities surrounding the implementation of automation in general. While automation today obviously doesn’t mean we interact with robots daily, it does mean that we will eventually see more research and evidence surrounding more meaningful exchange between humans and robotics.
But in the case of intelligent automation, technology can uncover the distinctions in vocal tones and the structure of language through machine learning and natural language processing. There is no interaction with the automated system, rather, this intelligent automation (which is a combination of artificial intelligence—for example, machine learning—with robotic process automation, or RPA), works behind the scenes to analyze the customer’s tone, cadence of language, and disposition.
The IA system in the case of a call center would be performing a variety of tasks at once: collecting data on the customer, inspecting the conversation, and directing information outward accordingly. Among other vocal features, the system would analyze pauses, tones, the energy of the speaker, and any enthusiasm or otherwise. Conversely, the IA analyzes the same variables in the customer service agent as well.
However, it’s absolutely crucial to execute the IA system correctly to nail down what end-to-end processes can be changed or refined using the system. Understanding your goals and implementing the insight of individuals across your entire organization.
By applying intelligent automation to your call center, you’ll be opening up the potential of your customer service experience.
When you uncover how your customers truly feel about your customer service, this will open you up to focus on continually improving the experience.
Check out our capabilities in the area of intelligent automation and customer engagement here.
