Back to Blog

Everything You Should Know About Call Center Analytics and Software

Acquiring a new customer costs 5 to 25 times more than retaining an existing one. However, keeping old customers is not an easy task either. In fact, 79% of consumers believe that a company's experience is as important as its product or services. 

Analyzing customer data through call center analytics can help improve customer engagement and experience. The call center is the first place a customer seeks information or assistance, and those interactions should always end on a positive note. Call centre agents can be educated to communicate more effectively and provide customized customer experiences.

In this article, we will discuss everything about call center analytics—understanding its importance, types and how you can choose the right call center analytics software.

What is Call Center Analytics?

Call center analytics is the process of collecting these customer data points, analyzing them and providing actionable insights. Based on these insights, call centers can understand the Dos and Don’ts while interacting with customers. Every day, call centers receive a huge number of inquiries. Each of them has various data points that can be used for personalized messaging. 

Types Of Call Center Analytics

According to a study, 76% of consumers prefer to use a variety of channels to interact with businesses, which results in a large variety of data being collected. Each type of data is evaluated following its own standards. For example, you may wish to determine how well AI chatbots solve customer problems. When gauging voice inquiries, you want to know the tone, words used, and pitch. 

Here are some of the most common call center analytics to consider:

  1. Voice analytics

  2. Customer self-service analytics 

  3. Predictive analytics 

  4. Omni-channel analytics 

Let's dive deep and look at how each one of these can help in improving the business.

Voice analytics: It does not limit itself to converting the voice to text but also helps understand the customer's tone and emotion. It can identify the common questions asked by analyzing the calls received. 

For example, a frustrated customer calls the contact center and shares a specific issue with your product. Voice analytics uses AI-enabled transcription to identify the tone and specific words to indicate the risk of customer churn.

Customer self-service analytics: 81% of all customers attempt to take care of matters themselves before reaching out to a live agent.

Self-service benefits both businesses and customers. Customers do not have to wait in long queues for the call center agents to attend the call. 

Creating guides, a knowledge base, and other resources to help customers find answers on their own is a one-time activity.

A much better solution is to use artificial intelligence (AI) enabled chatbots installed on the website. A chatbot can help provide the necessary information and solve people's most common questions. 

Customer self-service analytics helps you understand the effectiveness of these resources and chatbots. In fact, AI can take your customer experience game to the next level.

Predictive analytics:  Understanding your customer’s current behavior is one thing. However, predicting what they may need in the future is a whole other level of doing business. This is where predictive analytics comes into play. 

Predictive analytics helps increase conversions and reduce churn by activating the gaps in call center operations. It analyzes past customer interactions and forecasts future behavior. This way, it’s possible to address and prevent specific issues before they occur. 

Omi-channel analytics: With omnichannel customer service, your customers can communicate using voice, email, live chat, live calling, and social media. Every agent's client conversations are synced across all platforms in real time. Customers may begin a support interaction via email and continue on live chat. 

This shortens customer resolution times and eliminates the customer frustration of repeating themselves to multiple agents.

Omni-channel analytics helps with the global collection of call center data and analysis.

Why is call center analytics important

Understanding the target market's challenges and building a product that makes their lives easier is the first step of product development. 

Call center data can provide insights into consumer behavior (buying patterns, return rates, reasons for return, etc) throughout the customer journey. This can help your product team build the right products and services.

Every customer follows a unique customer journey. For example, a customer may begin by visiting your website, signing up for your mailing list, and then buying a product after a few months. Alternatively, someone might see an advert on Instagram and immediately buy it. It is critical for you to identify your most significant customer touchpoints and optimize them.

You can leverage different types of call center analytics to identify customer trends and improve customer experience. Here are a few major outcomes you can benefit from call center analytics.

  • Agent performance

  • First call resolution

  • Customer loyalty

Agent performance: Call center agents are often loaded with calls which takes a hit on their performance. Luckily, call center analytics can help track the agent's weekly performance by setting key performance indicators like CSAT (customer satisfaction score), handled calls, resolved issues, etc.

Agents also handle challenging calls which include frustrating customers or unusual problems. The agents can be trained effectively in Ender Turing's quality management system and personalized self-coaching. When such calls are handled right, you win a customer. You can explore more about Ender Turing here.

First call resolution:

Resolving the customer’s query in the first call without the need for any additional follow-up calls or emails is called First call resolution (FCR). It is a primary metric to assess the call center resolution process and agent performance.

Call center analytics identifies the number of calls resolved on the first call, the number of calls that needed follow-ups, and the reason for follow-ups. This detailed analysis helps in improving the process and agent performance.

Customer loyalty: 

The likelihood of a customer choosing to buy from a business repeatedly can be termed as customer loyalty. It is a never-ending relationship between the customer and the business. 

If a customer is willing to buy again from you, they will be more than happy to refer it to someone else as well. This makes customer loyalty important as it drives more business.

Call center analytics helps predict customer loyalty by analyzing the purchase history, resolution rates, and agent communication skills. Customer loyalty can also be improved by making efforts towards faster resolutions and effective agent communication skills. 

The power of call center analytics software

Call center analytics gives you information that can boost your performance. Some of the most important metrics to monitor include: 

These can become quite overwhelming, especially for medium or large businesses. Luckily, call center analytics software helps automate the process. You can get detailed reports with all the metrics you choose to monitor. 

The software also allows you to follow individual agents' performance and rate the quality of their work. With advanced tools like Ender Turing, you can follow agents’ progress and get insights on each address.


Source: Ender Turing

Some of the benefits of using call center analytics software are:

  • resource optimization

  • higher reliability of results 

  • the ability to cross-compare results

  • easily accessible reports

  • actionable strategies for improvement 

It’s no wonder that approximately 66% of call centers plan to invest further in advanced analytics to improve customer experience. However, there are many options available in the market, and sometimes it can be hard to choose the best for your business. Let’s take a look at some of the criteria you need to consider in the process.

How to choose the best software

If you want to implement call center analytics in your business your first question would be how to choose the best software.

There is no universal choice. The best one for you depends on a few factors.

  • Type of software

  • Size of your business

  • Cost

  • Priority KPIs

Type of software: A major part of deciding on the call center analytics software that suits your business depends on your call volume at the contact center. If you have a huge volume of voice calls that need to be converted into text and analyzed, then speech or voice analytics could be the right fit for you. Check out more about speech analytics and the right tools here.

If you're having difficulty keeping up with customer demands, predictive analytics software that builds strong customer relationship management is your best bet. 

Predictive analytics software can help you build strong customer relationship management if your business struggles to keep up with customer demands. 

Size of your business: Choosing the right contact center analytics solution depends on two things: the number of agents and the volume of calls received. 

Big businesses with more than 50 agents and high call volume require platforms that quickly handle data loads and process information. Remember that you are investing in this software to reduce the time spent on ad-hoc reports. If the software cannot produce real-time dashboards, it may not be a good fit. 

Cost: Make sure to compare the pricing of different software providers and match it with your budget to strike the right deal.

Priority KPIs: KPI is a Key Performance Indicator with which you can measure the performance of the call center agents and the business. CSAT is the most used KPI to measure the customer's satisfaction after a call. Likewise, there are multiple metrics like customer effort score (CFS), customer retention rate (CRR), first call resolution rate (FCR), etc. You can match the metrics you are currently measuring in your call center and the metrics provided by the software.

Exploring different software platforms and using them for a while is essential to understand the solutions provided. You can explore Ender Turing by signing up here for free.

How to get the most out of call center analytics software

Investing in call center analytics software is not enough; you also must learn how to use it properly. Here are some tips to help you do that. 

  1. Make the most of the trial

  2. Start with sample data

  3. Train new agents

  4. Streamline workflows

  5. Take action on insights

Make the most of the trial: When you sign up for a free trial of the software, explore all the platform's features. Also, ask as many questions as possible during the trial to be hands-on with the call center analytics software.

Start with sample data: Once you start using the platform do not dump all the real-time customer data at once. It might confuse you. It is a best practice to start with sample data and understand the platform. You can move the complete data to the analytics software when everything seems smooth.

Train new agents: Ensure all agents are properly trained before they start using the software.  Don’t rush things - you want to give people enough time to ask questions and test all features before using the software in their daily work. Here is a list of call center agent skills that can help them become a top performer.

Streamline workflows: A call center does not work on picking up a customer call, answering the questions and disconnecting the call. A pre-defined process is to be followed by the whole team to provide good customer service. Otherwise, it would result in long waiting for the customers, poor customer service and frustrated customers.

For example, what will the agent do if multiple calls are routed to them simultaneously? There should be an option to reroute them to someone with bandwidth. 

Take action on insights: Call center analytics software provides insights on agent performance, at-risk customers, upsell and cross-sell opportunities. As a business, you are the one to take action.

If an agent performs poorly, you can arrange additional training sessions or use Ender Turing’s self-coaching option to coach them with live examples. 

Improve your business with the right tools

89% of consumers are more likely to make another purchase after a positive customer service experience. 

What if we told you that the right tools can make you the best customer service provider ever?

Ender Turing has advanced analytics features that can be easily customized. You can tag specific keywords as negative tone and the software will identify these from the conversations to analyze the tone of the complete conversation. This helps in empathizing with the customers on call. You can refer to the picture below on how to create different tags.

You can also automatically set triggers to send hourly, daily, and weekly reports. Configure this once and the report is in your inbox. All you need to do is forward it to the concerned stakeholders.


To sum it up, it is profitable to invest in call center analytics software but be aware to choose the right one for your business with proper research. We would love to show you what wonders Ender Turing can make for your business. You can start by signing up for a free trial of Ender Turing here.


Why is call center analytics important?

Making sense of the huge volumes of data produced by the call centers is important. The call center analytics can help understand the customer journey and improve customer service metrics. Good customer service can lead to customer retention and a reduction in overall costs.

What is the KPI for a call center?

While there are so many KPIs for a call center. The fundamental KPIs would be First Call Resolution (FCR), Average Handle Time (AHT) and Average time to answer.

  • First Call Resolution (FCR) measures the ability to resolve customer issues on first contact, with no callback or follow-up required.

  • Average handle time (AHT) refers to the length of time from when an agent answers until they disconnect from the call.

  • The average time to answer is the average amount of time from when a call is received until it is answered by an agent.

What is predictive analytics in the contact center?

Predictive analytics helps in understanding customer behavior trends and suggesting product or service improvements to drive better conversions.

Subscribe to keep an eye on the call center news!

Back to Blog
The cookie choice is yours.
View our Privacy Policy

Cookie Settings

We use cookies to improve user experience. Choose what cookie categories you allow us to use. You can read more about our Cookie Policy by clicking on Cookie Policy below.

These cookies enable strictly necessary cookies for security, language support and verification of identity. These cookies can’t be disabled.

These cookies collect data to remember choices users make to improve and give a better user experience. Disabling can cause some parts of the site to not work properly.

These cookies help us to understand how visitors interact with our website, help us measure and analyze traffic to improve our service.

These cookies help us to better deliver marketing content and customized ads.