American Banker: Where Machines Excel in Detecting Customer Emotion
July 8, 2020
Humble Brag: Clarabridge’s text analytics capabilities were recently highlighted in American Banker’s article, Worried? Angry? Where Machines Excel in Detecting Customer Emotions, written by Miriam Cross and published on June 30, 2020. Please find the excerpt below and visit AmericanBanker.com for the complete article.
How does a bank know how a customer really feels?
Angry shouts or effusive displays of gratitude are unmistakable. But there are other subtle—yet important—markers of a customer’s emotional state that materialize on phone calls, chats or tweets and convey even more about how that customer is feeling.
Banks and credit unions have increasingly been using artificial intelligence that discerns and analyzes emotion to pick up elusive signals over text, audio and video. They’re using this emotion AI in two ways:
- Relaying the information to customer service agents so they can mend spiraling interactions in real time, perhaps by smiling more, chitchatting less or regaining focus.
- Providing the data to management, so they can detect patterns, draw broader conclusions and take proactive measures to improve the quality of customer service interactions.
Either way, emotion AI can lead to a deeper understanding of what customers are feeling and fill in the gaps where a human’s ability to analyze interactions falls short—especially important at a time when customers are contacting their banks amid mounting stressors related to the coronavirus pandemic.
“Some people are better than others at reading situations,” said Lisa Huertas, chief experience officer at Texas Tech Credit Union in Lubbock, which uses an emotive recognition feature from video platform POPi/o (the tech company considers this a form of emotion AI). “This levels the playing field.”
Overall, the effectiveness of emotion AI technologies “depends on how well you use, test and evaluate them,” said Seth Grimes, principal consultant at Alta Plana, an information technology strategy firm.
That means basing the models on data that is as free of bias as possible, and cross-validating results with other measures of customer service, such as net promoter scores and customer satisfaction surveys. Combining signals, such as the tone of voice, facial expressions and words, leads to higher accuracy. And a layer of human analysis is still vital.
As for concerns that a machine could never sense moods as well as a person, “humans are far from infallible,” Grimes pointed out. “When people evaluate AI technology, sometimes they evaluate it against a 100% standard, meaning it has to be right all the time. It’s a better idea to evaluate AI technology against a more realistic standard: Can it improve the overall situation? I believe it can if it’s monitored and has human oversight.”
[American Banker examined three providers with financial services clients who are bringing emotion AI to video, text and voice, with Clarabridge featured for its text analytics solution.]
The text analytics firm Clarabridge counts 38 financial institutions, including Bank of America, Capital One and U.S. Bank, among users of its technology, which is meant to help reduce complaints and compliance risk and improve digital experience and market research.
The Clarabridge Analytics platform looks for linguistic markers in social media, surveys, messaging and chat platforms, review sites, call transcripts and more to extract useful data. When audio is involved, the platform will consider how volume, interruptions and more offer additional context.
Word choice and grammatical construct can provide clues to a customer’s feelings. For example, if someone says, “Wow, this is the third time I called for [X topic] and I’m getting really frustrated,” Clarabridge will pick up on the fact that it’s the third time the customer has called (indicating an inordinate amount of effort), and how the word “really” intensifies the feeling of frustration.
Sometimes the takeaway can be counterintuitive. For example, Clarabridge is researching contextual indicators that would help an agent discern situations where empathy is not the desired response.
“In crises, speed is much more important” than empathy, said Fabrice Martin, chief product officer at Clarabridge. “You don’t want an agent to say ‘I’m sorry,’ you want the agent to solve your question really quickly if you’re about to go into foreclosure.”
While Clarabridge will perform some analysis while a conversation is happening, the company thinks clients can derive the most value from reviewing the data in aggregate—like treating the whole disease rather than an isolated symptom.
For example, if callers are throttling the contact center with the same questions, a bank can proactively change a policy or waive certain fees to address the underlying issue, retrain agents, or get an overall sense of how customers perceive their brand.
The technology has also served Clarabridge’s bank clients who are increasingly sensitive to customers’ anxiety, frustration and fear during the pandemic.
“Our financial customers want to understand really well what topics might be sensitive, such as [Paycheck Protection Program] loans, and make sure staff is well trained and prepared to deal with those questions in an empathetic and helpful manner,” Martin said.
While there will always be outlier situations where the human touch is necessary, scale is where Martin argues that the machine wins every time. “Even humans don’t necessarily recognize emotions in the same way,” he said. “And there is no human team that would be able to analyze tens or hundreds of thousands of conversations every hour of every day.”
Visit American Banker to read the full story. To learn more about how Clarabridge helps financial institutions maintain industry compliance and enhance experiences online, in person, and in the contact center visit our Banking & Finance Solution page.