Monitoring and reviewing call centre agents’ work was always an incomplete and unsatisfactory process in the past, explained CallMiner’s VP of international, Frank Sherlock. How could it be otherwise, when it has historically been a manual and unscalable job, reliant on chance sampling and listening?

“As an agent, say I make a couple of hundred calls in a week. Traditionally, maybe three to five of my calls are assessed on a weekly or a monthly basis. That is, someone will sit and physically listen to the call — a human being listening to another human being talking to another human being.

“But who would be happy to be evaluated based on 2 or 3% of their actual work? And added to that, manual listening is absolutely subjective — two supervisors could listen to the same call and one could conclude that it was really good, while the other thinks it’s pretty poor. In fact, it’s so inconsistent, ratings from the same person can vary wildly from one day to the next, depending on many factors.”

From random humans to machine-learned consistency

Automated agent performance monitoring solutions, like CallMiner’s Coach, offer in-depth insight and rich analytics to enhance key metrics — while also providing fairness and motivation to the agents themselves. Agents receive real-time feedback on their performance and ranking, including areas for improvement, based on ALL their calls.

“On the surface, you might get a reaction of, ‘oh this is Big Brother…’” Sherlock continued, “but when agents experience these benefits, they really appreciate the insight. Not only are they getting direct feedback on how they can improve and fix specific issues, some of their calls that were previously lost in the noise may get showcased as great examples to their peers. It’s a way of democratising data, making it available to the agent immediately after each call, so it’s all actionable and useful.”

Parsing the content of call transcripts at scale, to meaningfully analyse the way sentiment and emotion is expressed, requires artificial intelligence trained on the nuances of human conversation. “For example, in UK contact centre parlance, there are about 200 different ways that dissatisfaction is expressed,” Sherlock reflected. In addition to identifying the negative feeling — anywhere on the spectrum from profanity to mild sarcasm — the system analyses the agent response, and how the emotional tone of the whole interaction varies and modulates.

Optimising excellence

“As part of the complete contextual analysis, it rates how the agent takes ownership, expresses empathy, and ultimately resolves the issue and delivers the best outcome for the customer.”

In addition to optimising agent performance and regulatory compliance, CallMiner Coach empowers supervisors to work tactically on areas for agent improvement, and spend their time directly supporting agents, rather than listening to recordings to find needles in hour-long haystacks.

With enhancements to AI and other capabilities, Coach deeply analyses the emotional content of calls, enabling mapping of sentiment from one scenario to another — enriching insights and available actions.

For example, completion rates of post-call NPS surveys may be low. But if there’s a sample of detractors, the system can analyse what happened on the call to identify the cause, and then compare this to other records where no rating has been received.

“If we know what happened on the call to make me a detractor, then what other calls display similar DNA, what other calls have similar characteristics? With the right insight, organisations might identify another 2000 potential detractors that they didn’t know didn’t complete the survey, and then they can proactively reach out to them and put things right”

The potential actions and insight to be uncovered as the database of recorded calls grows and becomes an ever-deeper repository of knowledge is a vital asset to any contact centre seeking to enhance success metrics, way beyond scaling supervisor costs.

Maya Middlemiss

Automating Agent Performance Management