Today, call centers using human scoring score less than 5% of volume
due to cost and technological limitations. With automated call scoring,
machine learning algorithms train using results defined by humans
(hot lead, rude agent, upset customer, etc.) to score calls instantly.
As access to sensitive consumer information proliferates, so does the number of attempts to compromise this data.
No matter your specific vertical, maintaining an intimate understanding of every voice interaction between customers and agents is integral for call centers to maximize revenue, retention rates, and customer security.
Contact centers are on the front lines of shaping a customer’s experience, which gives them the perfect platform to enact predictive analytics in order to improve customer satisfaction, loyalty and retention.
In the era of big data, and now big voice, successful enterprises are taking advantage of data visualization with open BI tools like Yellowfin or Tableau.
Recently we’ve seen a shift happening in the telecom/unified communications industry where enterprises are now hiring developers and creating in-house solutions for gathering data and managing analytics. And the better the data, the better the results.
Human scoring was the only option for enterprise call centers to score, monitor, and manage their agents and call interactions for a long time. At that volume of agents and calls, many call center managers quickly discovered they could not afford to re-listen to every call, but they also couldn’t afford not to. So they compromised.