Everything You Need to Know About Contact Center Metrics: A Comprehensive Guide to KPIs
There are endless contact center metrics and KPIs that can be monitored but how much can they really tell you? Do you fully understand how to interpret them to affect positive change in your business? In this guide, we are going to look at the best contact center metrics and KPIs and show you how to leverage them to make data-driven decisions, demonstrate value to leadership, and reach both contact center and overall organizational goals.
Business Outcomes by the Numbers
You can make strong predictions and gain valuable insights about your business by looking at KPIs and metrics for customer satisfaction and agent performance. Monitoring the right metrics unlocks your ability to make data-driven decisions rather than guessing or going off a “hunch.” Today’s fast-paced and ever-changing business needs leave little room to falter. Staying ahead of customer demands is an important tactical strategy against competitors. Below are three primary categories and their associated metrics to further demonstrate how this data can help your organization rise to the top.
When you monitor the right agent productivity contact center metrics, you can gather critical insights into more than just how individuals are performing. Agent success hinges on proper training, team and managerial support, an effective tech stack, transparency into the rest of the business, and upskilling and career growth opportunities. Choosing the right agent productivity metrics to monitor will help you understand how well your entire customer service machine is running and where you can adjust to create better outcomes for agents and your customers.
What agent-focused contact center metrics should you focus on and what do they tell you? What makes a metric good or useful? The best agent-oriented metrics focus on measuring actual agent productivity while avoiding those that are outside of agent control, and can create unfavorable outcomes as a result. Let’s break them down:
- Average Speed of Answer (ASA) – the average time an agent takes to answer a call within a specified time frame
- Average Handle Time (AHT) – the average time an agent takes on a call, including hold time
- Average Hold Time – the percentage of time spent on hold versus the total time of the call
- Transfer Rate – the percentage of calls that agents transfer to other departments to provide solutions
- First Contact Resolution (FCR) – the percentage of calls resolved by the first interaction with the contact center
- Average After-Call Work Time – the time spent by an agent following up or making notes on a customer’s case after the interaction has ended
- Average Age of Query – the length of time active queries stay open if not resolved on the first attempt
Beyond just understanding the standard definition of these metrics, it’s important to understand how they reflect on both your contact center and business performance. When your agents are struggling, it can mean they are feeling unsupported or lack the tools or training needed. With the right information, you can make data-driven improvements to your staffing, tech stack, or training and mentoring programs.
For example, long after-call work times may indicate that you have inefficient workflows that need to be streamlined or automated. High transfer rates can either be an agent issue or a call routing problem – consider reviewing your phone trees and look for opportunities to implement intelligent IVR to improve routing. And FCR, of course, provides powerful insight into individual and overall agent performance. Low average FCR could be a good indicator that your agents lack the training, tools, and/or easy access to relevant data needed to assist customers.
Making changes means getting alignment all the way up the leadership chain. Using data gathered from these agent performance contact center metrics you can demonstrate the following value points to leadership:
- Improving your tech stack helps make agent workflows more efficient, lowering the time customers spend on hold and speeding up call wrap-up tasks.
- Improving agent cross-functional training, coaching, and support means that agents can handle calls faster and more efficiently, and are less likely to transfer the customer to another department.
- Leveraging opportunities to automate workflows and/or implement AI can improve contact center metrics and improve the customer experience overall.
That said, agent performance only tells us one half of the whole story. Happy agents will often mean happier customers, but how can you be sure? Customer experience contact center metrics are a helpful guide on their own but are even more powerful when correlated and analyzed alongside your agent performance metrics to more precisely identify areas for improvement.
Contact Center KPIs
So, which metrics tell you the most about the customer experience? Tracking contact center KPIs across the customer lifecycle gives you an end-to-end view of how your customers interact with your business, and how they feel about it. Let’s break them down:
- Average Call Abandonment Rate – the proportion of customers who initiate a support call and hang up before an agent connects with them
- Percentage of Calls Blocked – the proportion of customers who make a support call but receive a busy tone that stops them from connecting with an agent
- First Response Time (FRT) – the percentage of calls where the agent completely resolves the customer’s inquiry or issue without increasing the call transferred rate, escalating, or returning the call
- Repeat Calls – the rate at which customers have to call more than once to resolve an issue; Closely related to FRT
- Customer Satisfaction Score (CSAT) – indicates how satisfied or unsatisfied the customer is
- Churn Rate – the number of customers you lose over a particular period of time
- Customer Effort Score – how much effort a customer has to make in order to interact with your product or service
- Net Promoter Score (NPS) – how likely the customer is to recommend your company or product to others
- Lifetime Value (LTV) – predicted or measured profit gained from a customer over their lifecycle
As you can see, some of these metrics are contact center-specific and some are broader, looking at the customer journey as a whole. When a customer is reaching out for support, monitoring call initiation metrics is key to ensuring they have a positive experience. But how they interact with all other areas of your business can make or break their loyalty.
CSAT, and churn are great high level metrics that tell you how your customers are feeling, with the rest helping to paint a more complete picture. Call initiation metrics are especially telling – customers commonly reach out for support because something is already wrong. When they must wait an excessive amount of time to get through, if they get through at all, they are much more likely to have low satisfaction and are more likely to churn.
For leadership, the benefit of improving customer experience scores is an increase in NPS, LTV, and overall customer loyalty. With the right metrics, you can understand where your challenges are –– at the contact center level, or elsewhere within your business. The more you know about the customer experience the better you can adapt and respond to changing customer needs.
Contact Center Operations and Digital Transformation
Operational metrics are important for understanding staffing as well as where and how your customers communicate with your business. This data can help you understand the maturity of your digital processes and highlight areas where a deeper investment may be warranted.
- Service Level Usage – the percentage of agents’ answered calls within a certain period
- Occupancy Rate – the time agents spend on calls or performing after-call work during their shifts
- Agent Utilization Rate – the number of hours an agent works divided by the agent’s work availability
- Calls Handled – the raw measure of calls touched by an agent (or contact center) during a specific duration; includes both agent- and IVR-handled calls
- Active Waiting Calls – the number of calls agents handle versus those on hold
- Call Arrival Rate – the number of calls received by the contact center in a specified amount of time
- Peak Hour Traffic – time and volume of calls at their highest daily peaks.
- Channel Switch – the ability to resolve an issue in the same channel through which a customer raised it
- Self-Service Deflection – the proportion of customers who resolve their issues via an online knowledge base, forum, or any other self-service portal
- Cost Per Call (CPC) – the cost per customer contact to the contact center
So, what exactly do these numbers tell you? High service level usage (~80%) tells you that your contact center has excellent performance. However, extremely high occupancy rates can indicate understaffing –– monitoring this allows you to predict the likelihood of agent burnout and adjust staffing levels accordingly. Channel switching and self-service are both helpful in understanding not only where and how customers communicate, but how well you are able to assist them through those channels.
The value of understanding, and addressing, these metrics is how deeply they are tied to metrics in the other categories. For example, allowing customers with basic questions to self-serve means agents are more available to address customers’ more complex needs, reducing agent burnout and increasing customer satisfaction.
Staying On Track
The most important takeaway when assessing contact center metrics is to understand that no metric or KPI exists in a vacuum. The results of one lends insight into others. Correlating this data to drive effective, value-focused decision-making not only keeps customer satisfaction and agent performance high but it also helps you stand out amongst your competitors.
Found this guide helpful? Stay tuned for the next installment of this series, where we’ll dig deeper into the analytics.
Praphul loves to build what’s next. He has a passion for solving customer problems by leveraging design, technology and data science in a SaaSy way. Praphul brings over 20 years of experience in Customer Experience, AI and Analytics space and held leadership positions in many organizations like Microstrategy and Genesys where he built and launched many products in the market.
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