Guide to Interpreting Call Center Analytics

Call Abandonment Rate | 7 minute read

Correctly interpreting call center analytics and KPIs is key to improving your operations and your customer’s experience. But that’s not all. Once upon a time, these important insights were siloed; useful to call centers but not shared with other stakeholders. Today, contact centers are increasingly valued by c-suites for the customer insights they offer.

But without proper data analysis and interpretation, contact centers – and the businesses they serve – may not reach their full potential. Call center analytics provide valuable insights that can help organizations improve their operations and customer experience. Every contact center uses them. But knowing which metrics matter, and how to interpret them, is key to success.

Understanding Contact Center Analytics

Contact center analytics involves the collection and analysis of data related to the center’s operation. Analytics are also called key performance indicators or KPIs. They measure things like call volume, abandonment rates, first call resolution, agent productivity, customer satisfaction, and more. These metrics can be used to measure and evaluate performance, identify trends, and improve the overall quality of customer service.

One of the most basic, and critical, types of call center analytics is call volume. According to Statista, there were over 175 billion customer service calls in the US in 2020. This number is expected to grow even higher in the next few years.

Managers take note: this means that the phone channel remains an essential part of offering great customer service! All channels matter, but the phone channel isn’t going anywhere in the foreseeable future!

Interpreting call center analytics requires an understanding of the data itself. You also need enough operational knowledge to see how metrics identify areas that need improvement. Call center analytics software helps with this process and is typically bundled with modern call center software as a service products. This process begins with identifying the key metrics to track and measure.

6 Key Call Center Metrics

Call volume: The total number of calls received by the call center over a specified period.

Call duration: The average length of time that a call lasts.

First call resolution rate: The percentage of calls that are resolved on the first call.

Average speed of answer: The average time it takes for an agent to answer a call.

Abandonment rate: The percentage of calls that are abandoned by customers before speaking to an agent.

Customer satisfaction: A metric that measures how satisfied customers are with their call center experience.

Using these metrics, call center managers can identify areas of strength and weakness and implement changes to improve their operations.

How to Analyze Contact Center Analytics

Interpreting call volume

Call volume is one of the most important metrics for every call center. Managers need to have a clear understanding of call volume data to ensure they have enough agents available to handle calls during peak times. This metric can also be used to identify trends in call volume, such as an increase in call volume during a particular time of year.

Call centers typically experience a significant increase in call volume during the holiday season, for example, as customers call for help with purchases and returns. By analyzing call volume data from previous years, call center managers can prepare for this increase and ensure that they have enough staff scheduled to handle call spikes while continuing to ensure the customer experience and  call quality.


Call-backs help smooth call volume spikes and act like an insurance policy during busy times. They improve CSat scores and help with agent retention, too!

Interpreting call duration

By analyzing call duration data, call center managers can determine how long calls typically last and identify areas where they can improve call handling times. This metric is especially helpful with identifying calls that are taking longer than expected.

If call duration data shows that calls related to a particular product or service are consistently taking longer, managers can ensure agents have sufficient training and the resources they need to handle these calls more efficiently. If the problem persists, there may be an issue with the product and other departments may need to step in to resolve things.

Interpreting first call resolution rate

First call resolution measures the percentage of calls that are resolved the first time a customer connects with an agent for help. A high FCR rate indicates efficiency: customers are getting their issues resolved quickly and with minimal effort. A low FCR rate can mean there are gaps in agent training or inefficiencies in your call center’s processes.

Analyzing FCR data helps managers pinpoint areas where agents need more training or resources. This metric also helps identify recurring issues with a product or service and can show a company where change is needed. If there is a product defect or a confusing user interface, the problem can be resolved at the source.

Interpreting average speed of answer

Average speed of answer measures the average time it takes for an agent to answer a call. This metric can impact customer satisfaction and indicate how efficient your agents are. By analyzing ASA data, call center managers can identify whether customers are waiting on hold for extended periods of time before an agent can pick-up.

If wait-times are too long, managers will typically implement strategies to reduce hold times, like offering call-back options or other self-service tools like a well-constructed FAQ section.

Interpreting abandoned call rate

Call abandonment rates measure the percentage of calls that customers abandon before ever speaking to an agent. This metric can be an indication of long hold times or poor call routing, which can impact customer satisfaction. Abandoned call rate data helps managers identify areas where they can improve efficiency to ensure that customers have a positive experience.

If call abandonment rates show that customers frequently hang up after being on hold for an extended period, call center managers can implement strategies to reduce hold times, improve the hold experience, or offer call-back options.

Interpreting customer satisfaction

All metrics matter, but many experts say the one you really need to watch is your customer satisfaction rate. Just as the name suggests, CSat rates show how satisfied customers are with their call center experience. This metric can be obtained through customer surveys, feedback forms, or other means of gathering customer feedback.

Analyzing CSat data helps managers identify areas where they can improve the customer experience (often by referring to other metrics like first call resolution and average handle time) to ensure that customers are receiving the support they need.

Artificial Intelligence and New Call Center Analytics

There are also AI-assisted metrics that are shaking things up in the contact center. Some are completely new, and others have been in use before but have had new life breathed into them thanks to AI’s unique abilities.

  • One of the most exciting new metrics is sentiment analysis. This is when AI analyzes the tone and emotion of a customer’s voice during a call. Are they angry, frustrated, or satisfied? This information can help you tailor your response and improve customer satisfaction. Say goodbye to the days of relying on vague feedback like “the customer sounded upset.” Now you’ll have hard data to work with!
  •  Another cool metric is natural language processing. With this technology, AI can analyze the content of customer interactions to extract valuable insights. This could mean analyzing call transcripts to identify common pain points, or even mining social media data to track customer sentiment over time. The possibilities are endless!
  • These two new tools work together and are incredibly helpful in measuring customer effort scores, too. Customer effort score measures how much effort a customer had to put in to get their issue resolved. This could include things like how many times they had to call or how many different agents they had to speak to. The idea is that the less effort a customer has to exert, the more satisfied they’ll be with the experience.

By analyzing call recordings and chat transcripts, you can identify where customers are experiencing the most friction and work to streamline those interactions. Maybe there’s a certain type of issue that always requires a transfer to a different department, or maybe customers are getting stuck in an automated phone tree for too long. Whatever the issue, CES can help you pinpoint it and find ways to make things easier for your customers.

So, if you’re not already tracking customer effort score, it’s time to start! With AI, you can get a more accurate picture of how easy or difficult it is for your customers to interact with your call center.

Analytics Create the Best Possible Experience

Interpreting call center analytics is essential for organizations that want to improve their customer service and efficiency. Call center analytics can provide valuable insights that can help organizations optimize their resources and improve customer satisfaction. By using data to drive decision-making, organizations can ensure that their call center is operating at peak efficiency, providing the best possible experience for customers.

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