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Pegasystems acquires Qurious.io for Pega Customer Service

Pegasystems acquires Qurious.io for its customer service-oriented AI, natural language processing and self-service automation tools.

Pegasystems has acquired San Francisco-based AI and speech analytics company Qurious.io for an undisclosed sum. The technology will be used to extend self-service and agent-assist features for Pega Customer Service -- at least at first.

Qurious.io brings real-time natural language processing (NLP) speech-to-text features tuned for customer service uses, such as coaching agents during live calls on how to determine next best actions and solve customer issues.

The same technology enables chatbots to get a more accurate grasp on what a customer says and potentially make self-service more successful. Qurious.io is the second company Pegasystems acquired for Pega Customer Service in two years; Pegasystems acquired In The Chat to enable service over messaging channels in 2019.

"Qurious gives them NLP capabilities they did not have," said Predrag Jakovljevic, principal analyst at Technology Evaluation Centers.

New features from the acquisition are planned to appear in Pega Customer Service in time for the company's PegaWorld user event in May, said Don Schuerman, Pegasystems CTO and vice president of product strategy and marketing.

Pegasystems bought Qurious.io as opposed to partnering with another company or building its own because of the technology and the talent Qurious.io brings to Pegasystems, specifically for AI voice recognition, Schuerman said.

Customer service graphic
Qurious.io fills in some technology gaps in Pega Customer Service lineup, including self-service and phone channels with its agent-assist tools.

Pandemic rewires customer service

Prior to the pandemic, Pegasystems had planned to beef up its agent-assist features in Pega Customer Service and add emotion detection -- also known as sentiment analysis -- to pick up on a customer's state of mind. That's what Qurious.io's technology brings.

When social distancing rules forced customer service agents to work remotely, it became even more important, Schuerman said, as contact centers didn't have experienced agents who could coach newcomers in close proximity. For many sectors, contact volumes increased, too, which called for tools that fostered efficiency.

Technologies which contact center managers would have considered down the road suddenly became necessities that needed to go live quickly.

"The volume and demand on the agent have grown so much, and I think clients anticipate it's going to continue to grow and shift for many, many years to come," Schuerman said. He added that long after the pandemic ends, he believes contact centers will rely on distributed workers who will be managed remotely.

Bots, humans to work together for now

Even though companies like Pegasystems continually augment their customer service platforms with specialized AI tools to automate the work that humans do, they're not likely going to replace human agents anytime soon.

AI tools like sentiment analysis can't solve complicated customer service issues on their own. But they can help determine when the right time is to hand off an incoming customer call to an agent, and to take that customer's emotional "temperature," so to speak, as an agent picks it up.

Sentiment analysis AI is important to detect aggravation, and to know when exactly to transfer an irate caller to a human.
Predrag JakovljevicPrincipal analyst, Technology Evaluation Centers

"The more a robot can handle and relieve the human, the better," Jakovljevic said. "But there is a time when we all want to talk to a human. Sentiment analysis AI is important to detect aggravation, and to know when exactly to transfer an irate caller to a human."

Schuerman also said that AI-powered, self-driven customer service is still far from a reality. But sentiment analysis and agent-assist tools can make processing customer problems more efficient for humans -- who deal with the thorny customer service issues chatbots can't handle.

"We're not ready for pure AI on its own," Schuerman said. "But I think when you combine the AI's ability to detect things and use that as guidance for an agent, the combination of the AI engine and the [human] agent can be really effective."

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