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Using Statistical Performance Analytics for Call Center Agent Scoring

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Last week Kumaran Ponnambalam of BroadSoft unveiled our Statistical Performance Analytics approach to predict contact center agents’ future performance and customers’ needs, based on historical agent and customer activity data. The result is not only an enhanced customer experience, but also improved sales, customer retention and satisfaction.

By using Statistical Performance Analytics, contact centers can now accurately understand the different abilities of their agents based on real-world data.  With Statistical Performance Analytics, managers can better manage their agent pool, identifying and nurturing top performers while helping to improve low performers.

As Kumaran explains, the scoring and ranking process for agents should be an ongoing, automated process. Because company needs change continuously, the most efficient way to evaluate changing agent performance is through automation, as opposed to having individual managers evaluate overwhelming amounts of customer and employee data themselves.

Here is how Statistical Performance Analytics was applied for one company:

1. First CC-One collected and connected three months of the company’s data, which amounted to approximately 5 million customer-agent interactions, including agent activity data from their ACD, and business outcome data from their CRM.

2. Then, CC-One analyzed the data for insights. Agents were scored into ten distinct performance groups.

3. Following the scoring, CC-One formulated recommendations by modeling and simulating calls sent to agents based on their performance, in order to determine potential ROI.

4. Finally, CC-One proposed improvements into the model, showing a 14% conversion improvement over the previously longest-available agent routing tools.

To learn more visit the BroadSoft CC-One Analytics-Driven Contact Center page.

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