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Pegasystems Enhances Its Customer Process Manager
The company adds collaboration, predictive features for customers and support agents.
Posted Feb 28, 2013
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Pegasystems has just released the latest version of its Customer Process Manager solution, with added chat and social collaboration features, including team discussions, surveys, and real-time alerts.

The collaborative function is groomed for agent-to-agent and agent-to-back-office interactions with product managers or business analysts. Also new are personalized, real-time suggestions and offers for support agents, which draw on Pega's predictive Next-Best-Action-Marketing algorithm, to guide agents when making complementary offers or suggestions.

In doing so, Steve Kraus, Pegasystems' senior director of product marketing for CRM solutions, says "the system dynamically retrieves only the appropriate data for that contact, presenting a scripted dialogue...personalized offers, tailored screens, context-sensitive knowledge, [and] coaching tips" for agents.

To help guide interactions, the Customer Process Manager deciphers the channel in which a customer prefers to be serviced, such as a Web browser or Twitter. If a social channel is not optimal for the particular case or resolution that's needed, Pega's system can automatically trigger a tweet with a click-to-call link to connect the social customer with a phone support agent. 

Because the Customer Process Manager is rooted heavily in business process management, agents can deliver a personalized experience drawing on past customer interactions. "By infusing predictive analytics in the mix, we improve the context that the rules are based on," Kraus notes. "For example, as opposed to only using explicit fields like ZIP code (and) account balances, the rules can now leverage...predictive models like propensity to churn, propensity to buy, or the likelihood that a certain resolution will match what they asked for in the IVR."

The result, Kraus says, is a faster, more intelligent experience drawing from past successes and failures. 

According to Kraus, the release was inspired by the many large enterprises that were looking for ways to be more productive and to generate automated and personalized customer interactions.


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