Analytics for Call Center, Contact Center and Self Service
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Call Center, Contact Сenter and Self Service Analytics: Diving Into Your Performance

Successful companies usually have a clear outlook on the customer lifetime value so they optimize major KPIs such as customer satisfaction, average handle time, and contact resolution rates to gain customer loyalty. According to Statista research, 72% of contact centers’ marketing departments collect customer information through various data sources. Different kinds of customer support such as call centers, contact center and self service channels use analytics to measure agents’ performance and research actionable insights to improve customer experience by solving common issues with products or services. These insights and guidelines are essential in making long-term plans and introducing core KPIs to monitor operational decisions. 

They can also help agents make immediate decisions while interacting with the customer to provide better customer support and boost individual professional development. Speech analytics and predictive analytics are just some of the new technologies implemented in the work of different contact centers. Thus, learning about customer support analytics is the right place to start when searching for ways to improve customer experience, boost your business performance, and optimize call center operations. Read on to discover the peculiarities of call center, contact center and self service, why it is essential to track their performance and what KPIs you should consider.

customer support agent

What’s the difference between call center, contact center and self service 

A call center is a department that manages inbound and outbound customer interactions. Inbound calls handle inquiries about service and products, finances, and technical support, whereas outbound calls target fundraising, debt collection, and customer retention. Call center service can be located within a company or outsourced to a third-party provider, like WOW 24-7. Call centers rely only on phone calls to communicate with customers. Customer interactions happen in real-time, so they are often energy-consuming for the agent. Modern SaaS call center software uses automatic distributors to direct incoming calls to the correct department, asking the client about the problem first. It helps agents improve their efficiency by having a valuable way to track and analyze  performance and call volume. 

A contact center is a department where customer service is managed across multiple channels, such as live chat, phone, social media, and chatbot. Customer-facing agents use contact center software as a united platform to manage interactions received through different channels. 

Compared to having the phone as the only communication channel in a call center, using several channels allows companies to scale customer experience without recruiting more employees. Agents in the contact center can handle multiple chats and email interactions simultaneously. It enables them to provide quick problem resolutions and maintain a high level of customer service even during peak periods. Contact centers can develop more detailed profiles by combining customer data from various channels through which a client has contacted the company. These improved client profiles open the way to advances in predictive help, which contributes to a more efficient customer experience.

Self service channels are a set of tools that help customers research and resolve problems without having to reach a customer service representative. In contact centers, a chatbot can be integrated into customer engagement channels. Customers can immediately turn to a chatbot with their problems if they exhausted all the self-service options and cannot find an answer on their own. Self-service can be as simple as a web page containing FAQs or as complex as a conversational AI-based virtual assistant. A well-developed customer self-service strategy should contain prior research of customers’ needs and address them across multiple channels. 

Among the most popular self service channels are:

  • A web portal containing FAQs page, articles, video tutorials
  • Online customer forums to interact and exchange solutions for any issues clients may face (applicable for tech companies, automotive and home improvement retailers)
  • Intelligent Interactive Voice Response (IIVR) systems
  • Conversational AI-based Virtual Assistants with its own knowledge base
  • Self-checkout POS and self-order kiosks

Contact & call center analytics: how it helps solve customer service issues 

Firstly, call and contact center analytics help better understand call center agents’ performance and how empowered they are to serve the customers and identify areas that need more improvement and training. These actionable insights from detailed reports and interactive dashboards lead to improved customer experience and issue handling time. 

One of the most significant benefits of contact and call center analytics is the ability to boost your agent productivity in real time. When you receive ongoing data on your strengths and weaknesses, contact center managers can make adjustments as needed. Real-time analytics also empowers this ability for individual agents. Thus, they can view their performance, get feedback about call center operations and what aspects can be improved in each call. 

Customer data equips companies with valuable information to get more knowledge about the target audience, enhance customer journeys, and drive business revenue. This individualized and immediate feedback approach offers your team the chance to improve daily or weekly and from call to call.

Contact and call center analytics help solve the following challenges:

  • Establishing the core reason for agent performance rates 
  • Increasing inbound sales call volumes 
  • Improving overall customer experience 
  • Reducing average call handling time
  • Increasing the first time response rate
  • Understanding customers’ needs and tone of voice

Self service analytics: How it might boost customer care

Self service analytics spots self service issues to boost engagement rates by analyzing cases when customers leave the self service page abruptly or drop out of the company’s IVR flow. Thus, it helps make data-driven decisions on which paths to make changes to benefit both your customers’ and agents’ performance as self service channels optimize the work of contact center employees. Self service analytics increases customer satisfaction and reduces operational costs. The company can identify self-service issues by analyzing bottlenecks and abandonment rate in IVR software, customer satisfaction rate with FAQs and video tutorial, and the nature of interactions in existing client forums aiming for continuous fine-tuning. 

For example, in the case of having many calls going through IVR, the analytical strategy should identify improvements for the customer journey and reduce call transfer to agents. To analyze self service IVR- driven applications, the company should track the performance of the most important and popular IVR journeys against key metrics. Then, when applying improvements to IVR, the team measures their impact on customer experience. Finally, the company can make self-service tools more user-friendly and accurate by using text and speech analytics tools.

What Contact and Call center data is needed to track agent performance

Contact and call centers have several data sources such as workforce management software and quality monitoring software for tracking agent performance at least once per quarter. Agents also use them to monitor and report daily progress, workflow, and arising issues. Thus, contact center managers identify key metrics to measure agent performance by analyzing existing data. Firstly, businesses inspect the average time an agent speaks with a customer. They determine whether the agent rushes to finish the call without resolving the issue or if they struggle to use desktop tools, spending too much time looking for the correct answer. In addition, specialists observe hold time, measuring how often customers are put on hold. If it happens often, the agents may not be equipped with the right tools for solving inquiries or may need access to customer data. 

Quality monitoring happens when specialists review customer chat, phone, or email interactions and evaluate agents on multiple factors, including customer interaction, problem resolution, and adherence to policy and procedure. Thus, the most effective contact center reporting can be conducted when a supervisor has access to audio recordings and agent’s screen recordings for a better overview of all the communication. The results identify the areas where supervisors may provide additional feedback or agents require training to reach proficiency. Other metrics include schedule adherence and dial transfer rate. For the first one, managers monitor how well agents adhere to their schedules, meaning whether they arrive and leave on time and take their appointed breaks. The dial transfer rate measures call center performance by investigating how often agents pass customer interactions to other specialists. This rate helps managers determine the reasons for dial transfer and whether primary agents have enough competencies to solve those issues independently.

Contact center

Call & Contact center analytics: How to Measure Customer Satisfaction 

Usually, companies rate customer experience after interaction with agents by sending email surveys or calling the person. Variables measure a scale ranging from “highly satisfied” to “highly unsatisfied.” Here’s a formula that can help you determine an average customer satisfaction rate:

Number of Satisfied Customers / Number of Survey Responses X 100

For instance, business communications company Nextiva uses the Customer Effort Score (CES) to measure the convenience of the customer journey with its services. The experience can relate to purchasing, searching for information, or resolving a problem. The reason for monitoring a company’s CES is that the easier it is for customers to communicate and integrate within the organization, the higher customer satisfaction will be. You can maintain customer loyalty by making it simple to communicate with the company. Next, the Net Promoter Score predicts customer behavior by asking how likely clients are to recommend the company to a friend or co-worker. This method measures overall satisfaction from the service or product but does not gather opinions about the organization at this very moment.

How do you ensure a high level of customer satisfaction in your company? The core strategies consistently improve your past customer feedback and empower call center agents with new skills and knowledge. Try to research the root of the customer satisfaction rate you’re receiving – ask customers, sales teams, and frontline staff – your team can bring up valuable insights on how to deal with high call volumes, for example. It is also crucial to understand the place of your business in the field by analyzing your competitors and their strategies for measuring customer loyalty. If the company is not meeting the industry’s benchmarks, a major review of all the processes will be efficient for improving customer satisfaction.

Core Key performance indicators you should consider

First Call Resolution

First Call Resolution indicates the percentage of customers’ issues agents resolve in the first interaction. A high first-call resolution (FCR) means your overall call center’s productivity is in good shape, positively impacting customer satisfaction. To determine your FCR, use the formula below:

(Total Resolved Cases / Total Number of Cases) x 100

The average standard rate is between 70% to 75%. Accordingly, anything below that indicates your FRC needs improvement.

Average Wait Time

This metric shows customers’ average time waiting for an agent to respond to their call. If users stay on hold for too long, they will hang up, which is unsuitable for a call center. Let’s say a customer waited through and finally got an answer. Even in this case, when call center managers pick up the phone, customers may grow impatient and upset. This obstacle makes it harder to communicate with them. Consequently, you must bring your average wait time to as low as possible.

Interaction Quality

How would your customers assess the overall quality of communication with your agents? This process involves monitoring and recording interactions evaluated by specialists and managers according to such requirements as capturing customer data, professional communication manner, and agent’s solution efficiency. The collected information provides you with essential knowledge about the quality of every customer service interaction. Based on that data, you should create an improvement plan for the agent performance. 

Abandonment Rate

An abandoned call happens when the caller hangs up the phone while in the waiting line, using the IVR, or before leaving the voicemail. Here’s a simple formula to calculate the abandonment rate:

(Total of Inbound Calls – Total of Calls Handled) / (Total of Inbound Calls) x 100 

If your abandonment rate exceeds 5 percent, it is generally seen as an acceptable figure. However, if the number is above average or high, it signals that your agents miss a lot of customer calls.

Call center predictive analytics: Core Pros for businesses

Predictive analytics focuses on analyzing historical data of customer behavior. Combining metrics such as interaction quality, first call resolution, and customer satisfaction rates shows the core of predicted actions of users. Thus, predictive analytics are efficient at detecting customer intent. Using machine learning and artificial intelligence to analyze previous clients’ behavior and contact center communication, the company can predict how customers will respond to a potential follow-up message or call. Thus, predictive analytics can forecast the sales probability based on those metrics. Relying on call center analytics software data, business representatives have a better idea of what leads to use in their speeches to attract customers’ attention and improve productivity. In addition, it allows the company to plan staff schedules for seasonal spikes and forecast how successful product campaigns might be received.

Other technology tools helping to conduct predictive analytics include speech analytics, data mining and data science, and text analysis for communication through chatbot, email, or social media. What are the main benefits of call center predictive analytics?

  1. Better Customer Experience. With the help of predictive analytics, the company can define specific customer experience inclinations, such as customers repeating calls to solve minor issues like checking status updates. Thus, it helps the company forecast specific customer needs even before reaching out, improving customer satisfaction. 
  2. Increased Agent Performance. Artificial intelligence empowers agents by providing them with mock calls on real-world situations and training on guided workflows.
  3. Efficient Operations. With predictive analytics, call centers can anticipate the call volumes for the next hour, day and week, assigning enough agents to handle the calls for a smooth workload. For example, suppose analytics show a spike in call volume of inbound calls in advance. In that case, managers have time to cancel overtime hours and reschedule shifts to avoid collapse in the call center system.
call center agent productivity

Top analytics tools you might need to track your customer care performance


Mixpanel lets you understand how your customers interact with service or product and which features they use most often. You can analyze, measure, and improve customer experience and retention by studying call center analytics software, including usage statistics, events tracking, and sales predictions. The tool also allows you to try out various versions of a company’s website, app, or product and then decide which has the best performance. The contact center technology reports are simple to generate and create graphics and tables without any prior experience in coding. It is also possible to measure a product’s impact and the probability that certain groups of customers will upgrade it.

Google Analytics

One of the most popular customer analytics tools is Google Analytics. It helps assess the website and service performance. For instance, website managers review the products’ performance by creating goals in Google Analytics and see how effective their strategy has been after some time. The companies can analyze real-time data of customers on their website or app, agent performance, and the main customer communication channels. Thus, this data can be easily converted into interactive reports you can share with your team and make decisions accordingly. One of its core benefits is the ability to try out different A/B test layouts and assess the tool using a free plan.


InMoment provides valuable insights on your customers, product, or brand with a cutting-edge reporting studio. Their speech analytics tool contextualizes all the call centers’ conversations data into the bigger picture of customer and employee experience. It also gives personalized, actionable insights into improving all business operations, from customer relationships to agent performance. In addition, it contains a user-friendly interface, just like you’d expect from a tool dedicated to customer experience advancements.


Talkdesk is a perfect software for analyzing end-to-end customer experience and call center performance. It is a leader in using predictive analytics and artificial intelligence to ensure a seamless customer experience. The most helpful feature for companies is experiencing your team’s conversations with customers in real-time using easy-to-read dashboards. Talkdesk is the leader in conducting contact center reporting and analytics quickly and efficiently. Its interface includes all the essential measurement rates you expect from a top ranking analytics tool. 

Wrap Up

Different kinds of customer support such as call centers, contact centers and self service channels use analytics to measure agents’ performance and research actionable insights to improve customer experience. A call center is a department that manages inbound and outbound customer interactions via phone calls. Whereas a contact center is a department where customer service is managed across multiple channels, such as live chat, phone, social media, and chatbot. Self service channels are a set of tools that help customers research and resolve problems without having to reach a customer service representative. Contact and call centers need the average talk time, hold time, and quality monitoring data to assess agent performance. Core key performance indicators for service analytics are:

  • First call resolution
  • Average wait time
  • Interaction quality
  • Abandonment rate


In addition, predictive analytics efficiently detect customer intent using machine learning and artificial intelligence to analyze previous clients’ behavior and contact center communication. Thus, it allows the company to plan staff schedules for seasonal spikes and forecast how successful product campaigns might be received. The main benefits of call center predictive analytics include better customer experience, increased agent performance, and efficient operations. 

Outsource your call or contact center customer support to WOW24-7! Book a 30-minute conversation with our sales team to discover how to supercharge customer experience and overall business performance.

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