When companies measure customer satisfaction with their service functions, they look at factors such as how long a call center agent spends with a customer on the phone or in chat, whether the issue was resolved on the first call/chat, and what the results of follow-up customer surveys were. Meanwhile, they overlook how much actual effort the call required on the part of the customer or how the customer actually felt.
“It doesn’t help if you call into a company with an issue and you have to repeat your name and your account number every time you are escalated to another customer service rep, or if you have trouble understanding the rep because of a language barrier,” said Dr. Skyler Place, vice president of behavioral science at Cogito, which provides real-time emotional intelligence software.
Now because of advances in artificial intelligence (AI), machine learning, and big data handling and manipulation, customer service agents are beginning to get help with some of the intangibles of customer calls–like when a customer’s frustrations rise and they begin to raise their voice or when there are long pauses in the conversation that could indicate rising anger. Because the AI has been trained to operate in multilingual and cultural contexts, it can also be deployed in countries that have different linguistic and cultural styles that can influence whether anger or delight is being felt by a customer.
“What we are talking about is a way for the AI technology to analyze the tone of voice, or even the cadence of the language, to detect what the caller’s mood is,” Place said. “The artificial intelligence algorithms in the software actually stream in real time as the call takes place. The AI measures pauses in the conversation, how many times the agent interrupts the customer, the tone of voice of both the customer and the agent, and whether the voice is dynamic and interested or monotonal and disinterested. As the AI is doing this, it gives live feedback to the agent so they have this insight into how the customer is feeling as the call is taking place.”
In other words, if you’re a customer service agent and you’re on a call with a customer, the AI is going to send you popup messages on your display, like, “You’re talking too much.” At the same time, the message can get forwarded to your supervisor for training and intervention purposes.
“The idea behind the technology is to help customer service agents develop their empathy factors with customers and to make the calls go smoother,” Place said.
For both AI and voice-based big data, this is an exciting breakthrough.
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How AI can help
First, voice-based, unstructured data has been underexploited as more firms have gone for incorporation of video-based and IoT sensor data in their big data initiatives. Second, the voice-based AI is an attempt to harness an intangible element–sentient data in the form of voice cadences, inflections, etc., that can tell us a lot about how customers are feeling–and whether they are likely to go away happy after a call to a customer service agent.
The AI technology is especially useful for customer service agents who are inexperienced or who don’t have a natural ability to detect whether a customer is pleased or angry during a call. Place referenced one of the company’s clients that, after implementing the technology, saw a 28% improvement in its net promoter score, which measures the willingness of customers to recommend a company’s products or services to others. At the same time, the company achieved shorter calls in its call center because problems were getting resolved early–before callers got worked up.
These points are important because dissatisfied customers have a tendency to go somewhere else–like to a competitor.
“What the company learned was that when it used AI tools that could detect customer sentiment, it got improvement in its customer service,” Place said.
By relying on such solutions, companies may also see enhanced revenues and greater brand loyalty–as AI helps reinforce the old adage: “It’s not what you say–It’s how you say it.”
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