How to Use Call Center Analytics

Optimize your customer service with 4 key types of call center analytics

August 2023 - 5 minutes

Call center analytics are a critical tool to help you monitor and improve contact center performance and the customer experience. Analytics help you make informed decisions about the best ways to enhance the service experience and drive business success. Since 88% of people say good customer service experiences make them more likely to make another purchase, you want those experiences to be top-notch.

88% of customers say a positive customer experience makes them more likely to purchase again.

Source: "State of Connected Customer," Salesforce Research, August 2023

But how do you use call center analytics to improve customer service? The first step is to choose the right type of analytics for the metrics you want to monitor. In this article, we’ll explore what call center analytics are, what you need to consider when choosing the right analytics for your situation, how to use four different types of analytics, and how to get started.

What are call center analytics?

Simply put, call center analytics means collecting and analyzing data to uncover valuable insights about your service organization’s performance. For example, you might use analytics to turn up information about customer satisfaction (CSAT), revenue, customer retention, customer effort score, or service-level agreement (SLA) performance. You can then use that information to identify areas of strength and areas to improve.

How to approach call center analytics

Many contact centers struggle with having a real-time, holistic view of call center performance across all channels. This is not surprising, considering what happens daily – service outages, customer complaints, and underperforming agents. It’s hard to keep up, but this is how call center analytics can help.

Successful contact centers use advanced call center analytics software to monitor and review agent performance, not only from a customer lens but also from the perspective of both employees and management.

Each type offers its advantages and comes together to provide a comprehensive understanding of call center performance. To choose the best call center analytics system, determine what area of your business needs to improve and then see how analytics can help.

Before you get started, it’s important to identify exactly what you want to know. That way, you can choose the right metrics to focus on. Here are some important questions to consider:

  • What do I want to learn?
  • What metrics will help me get the insights I need?
  • Which metrics will be easy to measure? Which will be difficult?
  • Who will be responsible for collecting measurements? How will they do it?
  • Can I have too many metrics?
  • What are my targets for these key performance indicators (KPIs)?
  • What are my goals to improve these metrics? What is the timeline for achieving those goals?

Types of call center analytics and how to use them

These types of call center analytics may be helpful: omni-channel analytics, predictive analytics, voice analytics, and self-service analytics.

Omni-channel analytics

Customers today often reach out through more than one channel: 71% say they prefer prefer different channels depending on context. To deliver an excellent omni-channel service experience, your agents need to see all of a customer’s interactions with your business. And at a broader level, service managers and leaders need visibility into what’s happening across all the customer engagement channels.

71% of customers prefer different channels depending on context.

Source: "State of Connected Customer," Salesforce Research, August 2023

This is where omni-channel analytics comes in. Omni-channel analytics crunches all the data about how your customers interact — and where your team needs to improve its response — and serves it up as useful facts. For instance, you can review CSAT, handle time, or case volume by channel. You can look at agent work volume, average speed to answer, and other helpful metrics. This information helps you react to real-time situations and also plan for the future.

Predictive analytics

Predictive analytics are exactly what they sound: they use data to predict the future. In customer service, predictive analytics uses artificial intelligence (AI) to analyze call center data and apply logic from past solutions to solve current or future problems. Predictive analytics builds on the data in your customer relationship management (CRM) system, producing relevant and actionable insights. These data points help agents manage individual customer service cases, and also help managers direct the contact center as a whole.

For example, you might use predictive analytics to determine the category or severity of a case as it is logged, for more effective case routing. Once a case is in process, predictive analytics can surface the likely CSAT for this customer based on the issue – then provide suggested next steps. Predictive analytics could be used to determine the right amount of agents to staff over the holiday shopping season. Or plan for high call volumes when a new product rolls out. Or, you could use it to predict churn risk and identify any potential product or customer issues before they happen.


Voice analytics

Advances in customer service software have made it possible for voice to become a digital channel — with the same potential for insights as any writing-based channel such as email or chat. When voice is a digital channel, AI monitors conversations real-time and turns up insights that improve the customer experience.

Voice analytics shows you how many calls your contact center is getting, how quickly your agents are responding, and how long customers are waiting in a queue or being put on hold. It reports review average call handle time, as well as average handle time per agent. Transcripts of individual calls drafted by AI help you see if a particular agent needs more training on how to handle a particular issue, like an exception with a return. Reviewing multiple AI-powered transcripts for calls might reveal a that the entire team needs training on a particular issue.


Customer self-service analytics

Self-service channels like your help center, customer portal, or customer community empower customers to resolve simple issues on their own while deflecting more cases for your company.

You can use self-service analytics to see how well these channels are working for both your customers and employees. Review case deflection scores and see if there are any slowdowns or problems in the experience. Use self-service analytics to review common searches and identify any new trends in customer requests. Then, use this information to improve the customer experience, perhaps by creating new knowledge articles to address commonly asked concerns.


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