Demystifying Contact Center Performance Data and KPIs in Today’s Data Gold Rush

Making sense of contact center performance data has
always been a huge undertaking and investment for any organization, but this challenge has been intensified by the influx of data unlocked by today’s AI and ML tools. In this blog, one of XSELL’s data science experts shares some strategies for demystifying the immense amount of data generated by call centers to find the sweet spot for measuring agent performance across dozens of KPIs.

Contact centers have long struggled with data – The question of how to report performance data hangs over each center, as leaders balance the need for usable metrics against the risk of appearing as a transactional cost center. But today this problem is even greater, compounded by the vast amount of data that new technologies put at our fingertips.

The intensification of this challenge in data right now is illustrated by the “Gold-Rush Paradox.” In a blog by the CEO of the data pipeline management platform Orchestra, he describes the Gold Rush Paradox as “the tension between the high value placed on data and AI (akin to a modern-day gold rush) and the substantial difficulties in making data truly valuable for business.”

We are seeing a rush of talent and investment dedicated to AI, but companies still struggle with data quality, governance, and how to actually use the data that AI tools can now process and generate. Data is seen as incredibly valuable (and it is!), but when we’re inundated with such vast amounts of it, businesses fail to refine it into something valuable.

Of course, this is not to say that the wealth of data in today’s “Data Gold Rush” is bad. It just makes it important for call centers to be as focused on what they are measuring and why they are measuring it as they are on building the right KPIs and dashboards. What we really want from performance reporting is not simply an accurate accounting of contact center performance metrics alone – the ultimate goal is better results, not just insights for the sake of insights.

This blog breaks down a few ways to cut through the data noise and measure the right KPIs and performance metrics to reach your contact center’s goals, along with real-world examples of how. Let’s take a look:

Go beyond the metrics to capture qualitative data and agent behaviors

Call center data is uniquely human – Because it is based on many person-to-person interactions, we must look at it differently than other types of inputs. This data is full of connotations and behaviors that cannot be captured in numbers, making more nuance and deeper analysis necessary to understand it.

In call centers, qualitative data refers to non-numerical insights about the quality and effectiveness of customer conversations. Incorporating qualitative performance indicators into your agent KPIs can help go beyond standard metrics like average handle time, calls per hour, customer satisfaction score, and net promoter score to reveal richer, human-centric insights into what's happening inside calls.

Definition source: https://www.g2.com/articles/qualitative-vs-quantitative-data 

However, capturing qualitative data is much less straightforward than hard numbers. It relies on evaluating tone, empathy, or conversational context making it harder to standardize and interpret, which often requires manual analysis or advanced tools. 

So, how do you approach qualitative data? Rather than parsing through a sea of metrics that show past performance, focus on the human. Start by looking at individual agent behaviors that do and do not work towards achieving your organization’s goals. Which performance attributes have priority in measurement should be focused on those things that cause results, which can drive results – not just things that happen alongside results.

Looking at a Real-Word Example

For an example of this, let’s look at one of XSELL’s clients in the Medicare industry who partnered with us to help their contact center better establish trust with patients over the phone. We began by having our AI model analyze thousands of the client’s call transcripts, focusing not only on quantifiable metrics but also on the specific language and agent behaviors that set top performers apart. These behaviors, which we call top differentiators, are actions and strategies with a high correlation to success.

By identifying these top differentiators at the agent level, we uncovered key conversational techniques and phrasing patterns that high-performing agents used to build trust and rapport. For example, while most contact centers prioritize reducing handle times to increase call volume, our analysis revealed that when this client’s agents exceeded the five-minute mark, they engaged in more genuine consultative conversations. These longer conversations allowed agents to address patients’ healthcare pain points more effectively.

This is just one small example of how when you dig into qualitative insights at the conversational level to understand what exactly the top agents are doing that others are not, you can pinpoint differentiating behaviors and replicate them across your contact center to amplify its success (we’ll talk more about this in the following section of this blog…).

Use Real-Time Quality Monitoring

As customer expectations of service channels increase in a fast-paced, digital world, the need to deliver exceptional service in real-time is growing. Between the tools we can offer agents to successfully manage interactions in real-time and the data and insights we can supply the business on what is happening now, speed is the new priority – and the new reality. 

While post-call quality monitoring continues to play a crucial role in long-term strategy and performance improvement, real-time data offers a unique advantage by allowing call centers to influence outcomes as interactions unfold. By understanding the “why” behind customer actions and using that knowledge during the moment of engagement, call centers can proactively enhance live interactions rather than solely relying on dashboards and reports to review past performance.

According to a recent IBM blog on AI in the Operations Management field, Bouygues Telecom used generative AI to extract and analyze call center data, enabling workers to make personalized suggestions and solutions in real-time. By focusing on instantaneous quality monitoring, the company was able to reduce pre- and post-call operations by 30% and is projected to save $5 million.

The days of depending on lagging reports of the past are quickly fading into the rear view. By leveraging real-time quality monitoring, call centers can shift from merely reacting to past data to proactively enhancing live interactions. These insights offer a full 360-degree view — not just of KPIs but of the nuances happening in every customer interaction as it unfolds. 

Returning to Our Real-World Example

Let’s revisit our previous example of XSELL’s Medicare industry client from above. First, our AI processed thousands of calls to find that that longer call handle time is a key KPI for one of the contact center’s main goals – building patient trust. Based on this, we used our AI to identify context-based actions and strategies that the client’s best agents consistently use on these longer calls like educating the patient on benefits, addressing common concerns, and credentialing themselves in the first 30 seconds of the call.

But now that we’ve pinpointed these differentiating behaviors, what do we do with this information? This is where real-time quality monitoring comes in. We then transformed and collated these insights into conversational talking points and created a coach that would feed them to agents in real-time at the right time, guiding agents to the desired trust-building territory where they’re consulting with the patient about their healthcare.

Not only does the AI-powered coach give real-time suggestions, but it continuously improves by analyzing every interaction to identify new strategies and provide better agent guidance. Implementing the real-time coach led to 75% of this organization’s calls going past five-minutes long, allowing the agent and the member to have a better experience from these extended engagements.

While dashboard summaries and post-call reviews remain essential for strategic planning, pairing them with real-time monitoring creates a more dynamic and responsive approach. In today’s digital-first landscape, where immediacy and personalization drive customer satisfaction, adopting real-time quality monitoring isn’t just an upgrade — it’s a necessity. It’s time to rethink the way we measure success and embrace tools that empower us to act in the moment.

The key demystifying call center performance data isn’t just focusing on high-level KPIs like handle time or customer satisfaction, but digging deeper into the behaviors and micro-interactions that drive these metrics. By identifying what top-performing agents are doing differently and understanding the nuances of their interactions, you can refine KPIs to better reflect what truly matters. This layered approach brings historical performance data and real-time insights together, creating a strategy that’s both dynamic and responsive.