Customer Experience

Updated On: May 29, 2024

Contact Center Analytics: Types, Importance, and Best Practices

Reading-Time 11 Min

call center analytics

Contact centers are not just hubs of complaints and inquiries but also troves of valuable information. Every interaction holds the key to customer satisfaction, loyalty, and business success. 

With the help of contact center analytics, organizations harness the data generated from customer interactions to uncover valuable insights. These insights help them make informed business decisions to drive growth and elevate the customer experience.

66% of call center businesses are looking to invest in advanced analytics to provide a better customer experience. Advanced analytics solutions are helping call centers improve FCR, CSAT, and AHT rates.

This blog explains the types of contact center analytics and their importance in improving call center efficiency, customer satisfaction, and retention rates. Plus we have also created a list of best practices to leverage the full potential of call center analytics in maximizing ROI.

What are Call Center Analytics?

Contact center analytics is the process of collecting, processing, and analyzing call center data to get valuable insights into customer experiences, call center performance, and business products. 

It includes monitoring key metrics like:

  • Customer satisfaction (CSAT)
  • First call response rate (FCR)
  • Average handle time (AHT)
  • Abandonment rate 
  • Average speed of answer (ASA)
  • Net promoter score (NPS)

.. and more.

These key metrics and insights help businesses understand their customer behavior, product potential, and agent performance

Therefore, it assists businesses in making strategic decisions to modify their products as per customer preferences and improve the customer experience

Plus, contact center analytics also point out areas where the agents fail to meet customer demands. You can use the insights to identify and enhance agents’ training modules. This helps businesses deliver quality customer service, resulting in high customer retention and satisfaction rates.


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Types Of Contact Center Analytics

There are a variety of contact center analytics that help call centers enhance their operational efficiency and productivity. Some of the notable call center analytics are:

Predictive Analytics

Predictive analytics uses historical data to predict the pattern of customer behavior, requirements, or outcomes ahead of time. The same type of analytics is usually used by adtech tools such as ad server or DSP.

Therefore, it gives you a competitive edge to prepare beforehand and avoid mismanagement. You can use the insights from predictive analytics to streamline your call center operations for peak times, product launches, promotions, product up-sells, and more.

Predictive analytics also help you create future-proof strategies to stay relevant in this dynamic business landscape. 

Interaction Analytics

Every customer interaction is an opportunity to understand their preferences, expectations, and pain points. 

Interaction analytics offers insights into understanding customer’s unique needs and brand experiences. This plays a crucial role when you plan to scale your product, launch new upgrades, or build a new product. 

Moreover, it also assists in monitoring agent performance by tracking call volume, response time, hold times, abandonment calls, and call transfers. 

Speech & Text Analytics

Speech and text analytics in a call center are similar to a detective reviewing a case through witness statements and documents. A detective actively listens to witness statements and analyses crucial documents to uncover hidden insights about the case. 

Through his analysis, he decodes the tone, emotion, and reaction of the witnesses. On the other hand, reviewing documents provides him with important clues about the victim’s personal and professional status. All this data helps him crack the case quickly.

Similarly, speech and text analytics in call centers help in the analysis of unstructured data from call recordings, email correspondence, and live chat. This analysis assists you in identifying the tone, sentiment, and other customer experience points. 

The valuable insights from speech and text analytics dig out areas for overall CX improvement. It also enables you to evaluate agent performance and modify training for improvement.

Customer Journey Analytics

Customer journey analytics is also known as cross-channel analytics, as it tracks the customer journey through interaction data across multiple channels.

This helps to minutely evaluate customer interaction at every stage and identify the friction areas. You can then implement strategies to reduce customer effort in those stages and ensure an overall seamless experience.

Self-Service Analytics

Most of the customers, before contacting the call center, try to find the solution themselves through self-service channels. These include chatbots, FAQs, and blogs. 

These are the places where you will find actual customer pain points. It could be questions that customers ask the chatbot, the most viewed blog topics, or the comments section in the blogs. 

Gathering and analyzing data from these self-service channels will highlight the raw customer issues. Therefore, it enables you to improve the self-service channels further and resolve customer queries without needing call assistance. Thus saving you both time and money.

Why is Contact Center Analytics so Important?

Contact center analytics key metrics

Analyzing data in a call center is like using a compass in a vast ocean. Just as a compass guides sailors through uncharted waters, call center analytics provides direction for businesses navigating through a sea of customer interactions.  

Much like a compass helps sailors make informed decisions, call center analytics empowers businesses to make strategic choices based on valuable insights gained from customer interactions.

By examining the data, businesses can:  

  • Identify customer pain points
  • Monitor and improve their service metrics
  • Base important strategic decisions on reliable data
  • Understand customer trends and patterns
  • Improve customer satisfaction
  • Achieve operational efficiency

As per a recent McKinsey report, 37% of companies use advanced analytics to identify missed opportunities and create valuable customer experiences. 

As a result, these companies have cut employee costs by $5 million and increased self-service containment by 5% to 20%. Additionally, they have successfully reduced the average handle time by 40%. Most importantly, businesses have witnessed an increase in the conversion rate on service-to-sales calls by nearly 50%.

Contact Center Analytics Best Practices

Contact center analytics play a crucial role in streamlining operations, creating business strategies, and improving the overall customer experience. 

If done in the right way, call center analytics have the power to transform not only your support services but also other areas of the business.

Here are some highly recommended contact center analytics best practices:

  1. Use the right tools: Without the right tools in hand, you won’t be able to break out of generalized insights. To dive deeper into high-value insights, you need powerful systems to handle and process large volumes of calls, texts, emails, and other data.
  1. Leverage the full potential of speech and text analytics: Often, businesses analyze call and text data (from chatbots and emails) separately. Moreover, call center analytics for sales, marketing, support, and other departments are used in isolation. This results in failing to identify vital points. Therefore, you need to have a common platform for speech and text data analytics across all departments for the best business outcomes.
  1. Data Quality and Integration: Ensure the quality of the data being collected. Integrate data from various sources within the contact center, including CRM systems, call recordings, chat logs, and customer feedback. Clean, accurate, and comprehensive data is essential for meaningful analytics.
  1. Security and Compliance: Ensure that analytics implementation complies with data protection regulations and industry standards. Protect sensitive customer information and adhere to privacy regulations to maintain trust and avoid legal issues.
  1. Continuous Improvement: Regularly review and update analytics models and strategies. The contact center environment is dynamic, and what works today may need adjustments tomorrow. Continuously strive for improvement based on data-driven insights.


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How Knowmax Improves Call Center Analytics?

Knowmax, a CX-focused knowledge management system, provides guided knowledge for both agents and customers across diverse channels. The AI-powered knowledge management tool enables faster service, keeps the knowledge base relevant and up-to-date, and enables faster agent training.

This approach significantly boosts issue resolution, positively impacting crucial metrics like Average Handle Time (AHT), First-Call Resolution (FCR), and Customer Satisfaction (CSAT). 

Knowmax also comes with in-built micro-segmented analytics, providing invaluable insights into agent performance. By meticulously analyzing engagement patterns with the information used to assist customers, our analytics offer actionable data. This empowers organizations to optimize their support processes and enhance overall customer satisfaction.

If your CX organization is seeking a dynamic knowledge base solution, connect with one of our KM experts today.

Nitin Saxena

Sr. Vice President

Nitin has 25 years of experience working at companies like HP and Mphasis. For more than 14 years, he has been a key figure at KocharTech (Knowmax's parent company), skillfully navigating operations, training, and quality management responsibilities across international and domestic sectors. Currently, he oversees Business Operations at Maxicus.

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