Pegasystems acquires Qurious.io to apply speech analytics to customer service
Pegasystems announced today it has acquired Qurious.io, a provider of a cloud service that analyzes voice calls in real time to enable customer service representatives to better determine their next best course of action. Terms of the deal were not disclosed.
Qurious.io combines speech-to-text, natural language processing (NLP), and emotion detection capabilities to analyze conversations as they occur. Pegasystems intends to embed these capabilities in a portfolio of tools for managing customer service interactions that the company has been steadily infusing with artificial intelligence capabilities for several years now, said Pegasystems CTO Don Schuerman.
Speech analytics is only one of a collection of related AI technologies, added Schuerman. Pegasystems, for example, has already made available a Next-Best-Action Designer for AI tool that enables organizations to better optimize workflows along with AI and robotic process automation (RPA) engines for its customer relationship management (CRM) software that was first made available in 2017.
Qurious.io was founded in 2016 by CEO Sabrina Atienza and CTO George Ramonov. Schuerman said the speech analytics service, however, will no longer be made available as a separate software-as-a-service (SaaS) application.
In the wake of the COVID-19 pandemic, Schuerman said many organizations have been doubling down on customer service investments as interactions with customers via contact centers have spiked. Rather than simply reading from the same script for every engagement, Schuerman said the goal is to leverage AI technologies in a way that enables customer service representatives to better resolve unique issues as they move from one call to the next.
That approach should result not merely in higher levels of customer satisfaction; customer service representatives will also become more effective at identifying meaningful cross-selling and upselling opportunities, added Schuerman. Today most customer service representatives are simply provided with a list of products that they should recommend during each customer service engagement, he noted, adding, “Customer service representatives shouldn’t be randomly pushing products.”
Organizations shouldn’t have to spend so much time and effort on training customer service representatives when the staff turnover rate for those positions remains high. AI technologies should increasingly enable customer service representatives to navigate complex product portfolios that are continuously updated. In many cases, it’s simply not possible for customer service representatives to know how every element of a product portfolio might be applied in any given instance.
Instead, said Schuerman, AI technologies will provide customer service representatives with the appropriate guidance in real time.
In theory, that guidance should enable each customer service representative to increase the number of calls they can effectively handle. It might also reduce the turnover rate among customer service representatives by proving guidance that leads to significantly fewer stressful interactions with customers. When customer service representatives do leave the company, however, AI models will also make it easier to retain much of the knowledge the organization has about previous interactions that former employees — for better or worse — might have had with any given customer.
It’s not likely that AI will replace the need for humans to interact with customers any time soon. However, there is a larger number of low-level customer support tasks that AI technologies will increasingly automate. Customer service engagements involving human support staff will eventually be reserved for more complex interactions that will typically benefit from some level of AI augmentation. The challenge going forward will be defining exactly where the customer service handoff between man and machine should precisely lie both today and tomorrow as AI models become increasingly sophisticated.
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