Why Current Call Center Analytics Just Aren’t Good Enough

This is the fourth in a series of posts on customer experience in the call center, catch up by reading the first three posts here: The Call Center: Whats Love got to do with it, Happy Call Center Agents Create more Loyal Customers, Why the Call Center Matters more than Ever. 

Call center analytics have traditionally targeted operational efficiency and compliance, with only a limited focus on customers, and for good reason: it is critical to keep hundreds, possibly thousands, of employees working in a cost-effective manner. But customer satisfaction and loyalty are the lifeblood of any company and the ultimate goal of any call center, so traditional analytics have always fallen short of completely evaluating call center effectiveness. Customers do not exist in a vacuum. In the call center, the customer’s experience is directly tied to an agent’s performance on the phone, and it’s the latter element that almost all call center metrics fail to sufficiently capture.

Call centers currently evaluate about 1 percent of any given agent’s phone calls, with agent and supervisor meeting about once a month to discuss performance. That level of measurement leaves agents wanting more objective feedback and supervisors torn between using their limited data to meet management’s efficiency goals and giving agents the coaching they need to do their jobs better.

Furthermore, the 1 percent of calls under scrutiny tends to focus on negatives such as compliance violations, which aren’t usually representative of an agent’s overall performance. Call centers seek to measure both agent performance and customer satisfaction, but traditional measurement techniques, with only the worst calls likely to come up during performance reviews, skew evaluation of both metrics toward the negative.

Customer surveys, involving a Net Promoter Score (NPS) and other customer satisfaction measures, can provide a broader-based view of the performance of a company and call center as a whole, but they are not exhaustive. Customers only take surveys after they interact with agents, not during the calls themselves. And most don’t take them at all.

Surveys have no reliable way of measuring agent or customer experience during a call, nor can they shed any light on the behavior of either party during the interaction. NPS and other surveys are useful but ultimately not enough. They measure only the reactions of a handful of customers and provide little actionable feedback for agents. Their utility in helping companies improve call center experiences is limited.

What’s more, call centers often don’t receive NPS numbers for weeks, according to Harris Interactive. That means that NPS figures are nearly obsolete by the time supervisors get them, and whatever problems NPS analysis might have helped fix persisted in the time between when customers took surveys and when the data actually arrived in the call center.

This is one of the fundamental struggles with legacy call center metrics: they look backward and do not provide sufficient forward-looking or real-time data. More to the point, they don’t help agents with many of the fundamental aspects of their jobs, which also means they don’t contribute enough to actually improving customer satisfaction or building loyalty. In particular, they’re not able to effectively measure empathy and rapport, which are two things today’s call centers need most to succeed.

Call center metrics need to expand to include information that can guide the behavior of agents. They need to measure empathy and rapport both in real-time and in immediately reviewable formats. They need to give supervisors and agents the feedback on soft-skills that enables them to do their jobs better every day.

They also need shed a much brighter light on customers, offering metrics on real-time customer experience in 100 percent of calls that come into the call center.

Behavioral signals analysis offers, in real time, thorough evaluation of agents, measurement of emotional connection, analysis of customer experience and predictive observations, none of which traditional call center metrics have ever been able to effectively track. It provides the analytical pathway to improving empathy and rapport in call centers and ultimately to boosting customer loyalty.