Call Center Analytics: Types, KPIs & Metrics (Complete Guide)

Tired of trying to figure out what’s working and what’s not working in your call center? Call center analytics take the guesswork out, putting the spotlight on the metrics that truly matter.

When you analyze your customer interactions, you’ll uncover actionable insights that will boost efficiency and drive revenue growth. And that means shorter wait times, happier callers, and better business outcomes.

In this guide, we’ll dive into the essential call center metrics, how to choose the right tools, and how to leverage data to outpace the competition.

Ready to transform your contact center into a strategic powerhouse? Let’s dive in.

What Is Call Center Analytics?

Call center analytics refers to the collection, measurement, and analysis of key performance indicators (KPIs) and metrics within a contact center to optimize operations. Common KPIs tracked include average handle time (AHT), call volume, customer satisfaction scores, and average hold time.

However, call center analytics goes beyond surface-level metrics to provide insights into the customer experience and agent performance.

Using analytics tools and data, contact centers can identify opportunities to improve efficiency, boost revenue, and enhance customer relationships.

Modern contact centers are taking an omnichannel approach, supporting customer interactions across multiple channels like phone, email, live chat, and social media.

Omnichannel contact centers provide a comprehensive view of the customer journey across these digital and voice channels. This enables companies to connect data points to optimize routing, staffing, and the handoff between channels.

5 Types of Call Center Analytics

With the wealth of data available, call centers need to identify the most impactful analytics to guide operations and the customer experience.

Let’s look at the 5 most essential categories of call center analytics and how to use them:

1. Business intelligence

Customer relationship data such as revenue, churn risk, and past touchpoints provide your agents with insights into the business value of each customer.

Understanding the full customer journey helps agents tailor their interactions and focus on the most valuable relationships.

Use business intelligence to prioritize high-value customers, offer personalized promotions, and identify those at risk of churning.

2. Interaction analytics

Interaction analytics provide real-time and historical data on contact center performance indicators like response times, abandonment rates, resolution times, and call transfers.

Interaction analytics are great for identifying trends but can also be viewed individually to track agent performance.

Use interaction analytics to set performance goals, identify process bottlenecks, and track improvements over time.

3. Speech analytics

Speech analytics software automatically analyzes call recordings to identify positive and negative keywords in customer conversations.

While in the past, speech insights required a team to listen to and analyze hundreds of hours of conversations, today, you can automate the process with conversational artificial intelligence (AI) and machine learning.

Core Components of Speech Analytics

• Sentiment Analysis. This technique analyzes the tone, stress, and emotional state of the caller and the agent. Sentiment analysis helps identify customer dissatisfaction, urgency, and other emotional cues that might not be explicitly stated.

• Speech-to-Text Conversion. Speech-to-text technology transcribes audio into written text. This transcription enables further analysis, such as keyword spotting and topic classification.

• Keyword and Phrase Detection. Speech analytics tools scan conversations for specific keywords or phrases that indicate customer needs, compliance issues, or service opportunities. This detection aids in categorizing calls and identifying trends.

• Topic Classification. By analyzing the transcribed text, speech analytics can classify conversations into topics, providing insights into common customer issues, questions, or complaints.

Applications of Speech Analytics in Call Centers

• Quality Assurance and Agent Training. Speech analytics provides objective data on agent performance, identifying areas of excellence and those requiring improvement. This information is invaluable for targeted training programs and coaching, ensuring agents meet quality standards and improve their interaction skills.

• Customer Experience Improvement. Analyzing customer sentiment and feedback through speech analytics allows call centers to identify pain points and opportunities for service improvement. This direct feedback from customers is instrumental in enhancing products, services, and overall customer experience.

• Compliance Monitoring. In some industries subject to regulatory compliance, speech analytics helps ensure that agents adhere to required scripts and disclose necessary information, reducing the risk of legal and compliance issues. 

• Efficiency and Resolution. By identifying the reasons for calls and common issues, speech analytics enables call centers to address underlying problems, potentially reducing future call volumes. It also helps in developing more efficient resolution strategies for common queries.

4. Customer surveys

Customer surveys serve as a direct channel for gathering feedback on customer experiences, satisfaction levels, and expectations. This approach allows call centers to quantify and analyze customer sentiments, preferences, and potential areas for improvement.

Components of Customer Surveys

Survey Types

• Post-Call Surveys. Conducted immediately after a call, capturing the customer’s fresh impressions of the service received. They measure customer satisfaction (CSAT) and net promoter scores (NPS) to complement your other analytics.

• Email Surveys. Sent to customers following interactions, offering a detailed feedback mechanism on their experience.

• SMS Surveys. Utilized for quick feedback, these surveys are sent via text message and are beneficial for capturing immediate reactions.

• IVR (Interactive Voice Response) Surveys. Automated phone surveys where customers respond to questions through voice or keypad inputs.

Question Formats

• Open-Ended Questions. Allow customers to provide detailed feedback in their own words, offering insights into their thoughts and feelings.

• Closed-Ended Questions. Include multiple-choice questions, ratings, and yes/no questions, facilitating easy quantification of responses.

• Likert Scale Questions. Customers rate their agreement or satisfaction on a scale, typically ranging from very satisfied to very dissatisfied, which helps in measuring the intensity of their feelings.

Use survey data to recognize top-performing agents, guide coaching, and resolve common pain points.

5. Predictive analytics

While most analytics detail past events, predictive analytics forecast future outcomes using machine learning algorithms. This predictive insight can significantly enhance decision-making, customer service, and overall efficiency.

For example, call volume forecasting optimizes staffing schedules to match projected demand. With predictive analytics, you can move from reactive to proactive optimizations.

Core Components of Predictive Analytics

Predictive analytics in call centers relies on several core components and techniques:

• Data Mining. This involves extracting useful information from large sets of data. In call centers, data mining helps identify patterns and correlations in customer interactions, agent performance, and call outcomes.

• Statistical Analysis. Statistical methods are used to understand and interpret data, providing a basis for predictions. This includes regression analysis, probability models, and other statistical tests that forecast future call volumes, customer behavior, and service needs. 

• Machine Learning Algorithms. Machine learning enables predictive models to learn from historical data, improving their accuracy over time. Algorithms can predict customer churn, the likelihood of specific inquiries, and optimal responses to customer issues. 

Application of Predictive Analytics in Call Centers

• Forecasting Call Volumes. By analyzing patterns in historical call data, predictive analytics can forecast future call volumes, helping in workforce planning and resource allocation to manage peak times effectively.

• Enhancing Customer Experience. Predictive models can identify customers at risk of churn, enabling proactive engagement to address their concerns. Similarly, predicting customer inquiries and preferences allows for personalized service delivery, improving overall satisfaction.

• Optimizing Agent Performance. Predictive analytics can forecast agent performance, identifying training needs and matching agents with calls where they’re most likely to succeed. This not only boosts agent morale but also enhances call resolution efficiency. 

Pros and Cons of Using Call Center Analytics

The benefit of analyzing calls is that you can scale your call center operation and achieve consistency. But over time, call center analytics has earned a mixed reputation.

Traditional call centers might still be measured by pure call times, handle times, and hold time alone.

Here are some pros and cons of using call center analytics.

Pros	Cons
Measurable: Numbers are easier to quantify than soft skills like “attitude.” There are dozens of meaningful metrics to track and interpret.	Impersonal: Metrics, unlike the callers themselves, aren’t personalized to the customer needs at hand. Some call center performance metrics discourage building rapport.
Integrated: Most cloud contact center solutions offer built-in call center analytics. This means call center managers don’t need to learn SQL or use Crystal Reports.	Isolated: Inbound call trends tend to stay relevant only within a contact center. The downside is that management may be distant from the root causes of customer issues.
Manageable: The ideal contact center analytics are actionable. For instance, ones that can be improved by practicing the right behaviors.	Manipulated: Many call center veterans know how to shorten calls at the expense of resolution rates.

Most challenges with measuring call center analytics and the imperfections involved can be solved with reliable and accurate call center software. Measuring performance isn’t an issue, the quality of your call center service is.

“When performance is measured, performance improves. When performance is measured and reported back, the rate of improvement accelerates.”

Karl Pearson, English mathematician and biostatistician

You just need the right feature set to correctly gauge your performance.

Top Call Center Analytics Software

With so many call center software available, it can be tricky to determine the best fit for your business.

Here are a few of our top picks:

1. Nextiva

Nextiva offers robust and easy-to-use analytics in call center operations.

Key features include real-time monitoring of call volume and wait times, as well as historical reporting on critical KPIs like first call resolution, talk time, hold time, and more.

Nextiva builds visual wallboards so managers can easily track metrics for individual agents as well as call center-wide performance.

2. Talkdesk IQ

A key strength of Talkdesk IQ is its speech analytics capabilities. It can transcribe calls and allow you to perform sentiment analysis to see how positive or negative customer interactions are.

Beyond speech analytics, Talkdesk IQ offers wallboards, custom reporting, and integration with top CRM platforms.

3. InContact Analytics

InContact Analytics from Nice inContact is another viable call center reporting solution, especially for mid to large contact centers. This analytics platform enables businesses to bring together their entire workforce under one unified suite for reporting and oversight.

InContact provides wallboards, custom dashboards, and a broad range of historical reporting capabilities across your whole customer journey.

💡Tip: Assess your analytics needs and budget to determine the best match for your business. When evaluating options, look for capabilities that align with your reporting needs and integrations with complementary platforms you already use.

5 Key Analytics Features in Call Center Software

Call center analytics software should make using data more accessible. Not harder. In the era of remote and hybrid work, you need the right communication tools to help you work smarter.

As you compare options, here are five must-have features you need.

1) Data integrations

Your contact center software needs to be tightly integrated with your CRM, team chat, and email.

In addition to collecting quality data on agent performance and customer satisfaction, it surfaces valuable insights for your team when they need it most.

Cloud contact centers utilize secure APIs to exchange data with other services behind the scenes.

2) Instant access to call center metrics

Call data comes in many different forms and can be overwhelming without the right strategy in place.

Don’t just store data in a hidden repository. Your analytics software should come with pre-built reports that connect to your most important business metrics.

These reports allow you to quickly turn data into actionable insights rather than struggling to create custom dashboards.

3) Real-time call center analytics for agents and supervisors

It’s no longer enough to only look at historical data.

Instead, look for a solution to provide real-time data to enhance the customer experience. These tools aggregate customer sentiment, call center performance, and business outcomes together.

Outside of the call center environment, actionable insights can appear in the form of a CTI-based screen pop when an inbound call arrives at an employee’s phone.

4) Actionable insights along the customer journey

Every business is unique. While reports will help you quickly take advantage of your call data, they might not cover all your KPIs.

Use Contact Center as a Service software that can track the performance metrics that matter most to your business goals, such as:

• First Response Time (FRT)
• First Contact Resolution (FCR)
• Average Speed of Answer (ASA)
• Average Handle Time (AHT)
• Call Volume Trends
• Average Waiting Time

Tracking these metrics along the customer journey ensures you create a customer success process. Go beyond customer calls to achieve deeper customer engagement.

5) Omnichannel approach for customer satisfaction

Lastly, your call center analytics software shouldn’t be siloed from the rest of the business.

A good analytics solution combines cross-channel analytics and takes advantage of an omnichannel approach.

Look for an intelligent solution that integrates with your other contact center data, such as:

• Text analytics from live chat, SMS, and mobile apps.
• Interactive voice response input and AI responses.
• Customer survey responses, including post-call satisfaction surveys.
• Marketing data from campaigns, emails, and website visits.
• Sentiment scores from chat and phone conversations.

The more customer data you can bring together, the easier it is to create automated workflows and provide amazing service that puts you above the competition.

How to Analyze Call Center Data

Collecting customer data is relatively easy these days. But using that data to make better business decisions is still a major challenge.

Research compiled by HBR shows that 72% of companies are falling behind in building a data-led culture.

The problem often starts with legacy technology. Outdated phone systems make it difficult to properly collect and analyze call data.

However, this is changing as more teams adopt cloud-based business phone systems like Nextiva. These systems seamlessly integrate call analytics.

Making sense of call center analytics involves three key phases:

1. Collection – Compile data from your contact center into a central database, CRM, or analytics software. This makes metrics like resolution rates easy to monitor.
2. Analysis – Transform raw data into understandable reports and visualizations for your team. Leverage templates to enhance business intelligence and alignment.
3. Action – Use data-driven insights to optimize agent performance. Set benchmarks for customer satisfaction and other key performance indicators to drive sustainable growth.

Without organization and an actionable plan, data is just noise.

Similarly, making big decisions based on flawed data is a waste of time. An integrated cloud call center gives you the data you need to avoid these pitfalls.

Four steps for success:

1. Start with a strategy – Decide which insights are most vital and why. Are you aiming to reduce handle times or improve CX with predictive analytics? Connect data goals to concrete business outcomes.
2. Get the right analytics resources – Choose intuitive software accessible to your whole team. Customizable wallboards that surface real-time metrics can be very powerful.
3. Prepare your team – Deliver insights in simple, engaging ways rather than boring reports. Foster excitement and buy-in around data.
4. Encourage data-based decisions – Engage staff in using data versus instinct to guide choices. Consider tying incentives to customer satisfaction and retention targets powered by call analytics.

With the right vision, tools, and culture, call center analytics can transform your business from guessing about customers to truly knowing them.

Advanced Analytics to Improve Business Operations

Of course, calls aren’t the only way your customers get in touch with you.

For those with an omnichannel contact center comprising calls, social media, chat, and email, you’ll have access to even more advanced analytics.

Here are a few more to consider:

📊 Business intelligence

The next level up from call center analytics is tried and true customer intelligence. Business intelligence lets you examine your customer base’s Recency, Frequency, and Monetary (RFM) constructs.

RFM analysis enables you to determine if customers are completing more purchases and generating more revenue. This blends your contact center, payments, and CRM data to aggregate customer behavior.

📈 Text analytics

With more users looking for support through real-time chat, you’re sitting on a goldmine of text-based data.

Text analytics collect and analyze conversations and metrics from both live chat and AI-powered chatbots. Use terms and phrases customers already know.

For instance, when giving customers a link to update billing information. So instead of “Update my payment method,” you might want to use “Update my credit card” if that’s what they include in customer service requests.

💬 Self-service analytics

Related to the above, you can mitigate high call volumes with self-service options. The majority of customers will try to resolve issues on their own before reaching out.

By collecting data on your most-viewed help docs, you can identify potential problems and give your agents more context about incoming customer calls.

These data sources help you understand your customers better and provide more efficient support.

The goal is to turn the data behind your customer interactions into business outcomes: more revenue, higher customer loyalty, and decreased service costs.

How to Use Call Center Data

Each piece of data you collect is like a piece of a puzzle. Individually, they don’t offer much value. But when put together, you get a complete picture of the customer journey.

Phone call data is most useful when sharing the results with your team, and call center agents can act on it.

While some analytics software lets you build custom dashboards, it’s a good idea to start with templated reports that make critical metrics easy to understand.

1. Track agent performance

Call center supervisors can use specific reports to check in on one or more agents, find bottlenecks, and identify areas for coaching.

For example, digging into the average handle time by skill report can show if they’re taking longer than average to reach a resolution and need coaching.

Here are a few other standard reports you can use to improve call center agents’ performance:

• Calls by skill report: If you use an automatic call distributor (ACD) for routing inbound calls, you can see the kinds of calls they take. It’s a great way to highlight high-performers and identify additional call queues.
• Agent summary reports: Sometimes, you just need an overall view of an agent’s performance. The summary report covers metrics around the average and longest times spent on various types of calls.
• Activity reports: Keeping track of agent productivity is never easy, especially if you’re working remotely. The activity report gives you a historical view of each agent’s call activity, dispositions, and time spent in different call states.

For routing inbound calls, you can see the kinds of calls they take. It’s a great way to highlight high performers and identify additional gaps.

2. Improve customer relationship

Call reports can also assess broader trends with a call center’s performance. Even when agents aren’t talking to customers, you should monitor that.

For example, the abandonment rate is the percentage of callers that hang up before reaching an agent. In some cases, this is a good thing, like after hearing the hours of operation or the address in the IVR.

But, for most, an abandonment rate of less than 10% is the industry average.

In that case, you might choose to make ‘speed of answer’ your primary metric.

Here are a few more call center reports you can use:

• Incoming calls report: Customers expect a swift response when they call. This report shows historical and real-time customer data on how you’re handling incoming calls, including wait times, queued, and missed calls.
• Service level report: It’s not always easy to know where your support team is missing the mark. The service level report shows how well your contact center can answer a percentage of calls within the defined period. Industry example: 85/30 means 85% of calls are answered within 30 seconds.
• Summary report: Think of this as a health check for a cloud contact center. The summary report can show your average wait time, abandonment rate, speed of answer, and occupancy rate.

For more guidance, Forrester does a nice job of breaking down the different customer experience (CX) metrics and how you should contextualize them in this diagram.

3. Act on customer data in real-time

So far, most of these call center analytics aggregated weekly and monthly. Today, agents can turn a mediocre phone call into an outstanding customer experience.

How? It’s all about real-time customer sentiment. In particular, agents can view and act on survey responses, unresolved requests, and other customer journey data on the account.

With the functionality of cloud phone systems, you can use real-time data for significant customer service improvements — even if you don’t work in a call center.

Employees can view such actionable insights such as:

• Past purchases
• Customer satisfaction score
• Customer effort score
• Customer revenue
• Account tenure/age
• IVR key presses
• Recent support inquiries
• Preferred contact method (email, phone, social media)
• Order preferences (shirt size, flavor, method of payment)
• Self-service attempts

Turn Your Contact Center Into a Differentiator

Zappos made a name for itself for being maniacal about customer service.

And they lived it and proved it over the years by creating a culture of exceptional customer support. I’d be remiss if I didn’t mention that our Amazing Service is atypical for the UCaaS market.

More than ever, customer experience is becoming the key factor that wins deals and drives customer loyalty — not price. To uncover the “what” and the “why” behind customer calls with the correct data.

Looking ahead, the odds are in your favor.

A study by McKinsey suggests that companies embracing call center analytics reduce average call handle time by 40% and optimize conversion rates by almost 50%!

With the right call center solution and a strategy to expedite the decision-making in your company, you’ll be well on your way to standing out from the crowd.

Call Center Analytics FAQs