OI Blog


Peter Iansek

CEO & Co-Founder

Peter Iansek


Oct 2023
OI Blog
Oct 2023

Contact Center Analytics: Everything You Need to Know [2023]

Contact Center Analytics: Everything You Need to Know [2023]
Peter Iansek


Peter Iansek

CEO & Co-Founder

Table of Contents:

  • Why is Contact Center Analytics Important? == Why is Contact Center Analytics Important?
  • Types of Contact Center Analytics == Types of Contact Center Analytics
  • How to Set Up your Contact Center Analytics == How to Set Up your Contact Center Analytics
  • Pros and Cons of Using Contact Center Analytics == Pros and Cons of Using Contact Center Analytics
  • The Best Contact Center Analytics Software == The Best Contact Center Analytics Software
  • Improve Your Contact Center Analytics with Operative Intelligence == Improve Your Contact Center Analytics with Operative Intelligence

Why is Contact Center Analytics Important?

With detailed and thorough analytics, contact centers can:

1. Increase customer retention

Customer loyalty takes time and effort. To keep customers happy with a product or service, organizations need to be able to quickly identify customer pain points and areas of improvement. 

This can lead to more personalized experiences, better strategies for improving customer retention and loyalty, and improved customer service metrics, like: 

  • First Response Time
  • Call Handle Time
  • First Call Resolution (FCR)
  • Quality of Recent Support
  • Average Wait Time


2. Understand customer feedback 

Most companies still utilize surveys to get feedback from customers. But surveys are subjective and reach limited customers.  

Another common method of receiving customer feedback is through "dispositions," which involves an agent selecting a "reason for contact" from a drop-down list in their CRM at the end of an interaction. These answers are subjective and the agent has to work within a limited list. 

Today, platforms can automatically analyze customer interactions through:  

  • Text analysis: Text analysis, also called text analytics, involves understanding natural language text and extracting useful information from it. Text analysis tools can consume text data from various sources, including emails, phone transcripts, surveys, customer reviews, and other documents. 
  • Speech recognition: Also known as automatic speech recognition (ASR), speech-to-text, or computer speech recognition, speech recognition is the ability of a computer to recognize and translate spoken language into text. 
  • Sentiment analysis: Sentiment analysis uses machine learning and AI to pull subjective elements from text to determine the feelings and opinions of the speaker. This can help organizations understand where and when customers are having a negative experience while using their product or service. 

Analytics platforms can then identify the root reason customers are contacting customer service. For example, Operative Intelligence analyzes 100% of inbound interactions and shares the root causes of overall inquiries using the customer’s actual words. Organizations no longer struggle with finding and justifying which interactions could be automated, constructing a business case, and monitoring ROI. 

With Operative Intelligence, this process is entirely automated – saving you months of time and effort – and the results are immediately available with a single click. You can be confident that the changes you’re making will give you the expected return through unique value scores for every driver. 

3. Improve agent performance

With contact center analytics, contact center managers can review automatically tracked KPIs (like hold times and first-call resolution rates or closing rates for sale) to identify top performers as well as agents that need to improve. 

By identifying metrics relevant to corporate objectives, contact center analytics platforms can also pull and share findings in easy-to-use dashboards and reports. 


4. Support objective decision-making

When contact centers have easy access to KPIs, customer satisfaction scores, and operational metrics, managers can easily identify areas of improvement, potential opportunities, and risks. With the right data, you can make faster, more effective decisions with more confidence. 

Types of Contact Center Analytics

1. Speech Analysis 

speech analyzer
Speech Analysis

What is it & why is it important?

Speech analysis uses artificial intelligence to comprehend, interpret, and analyze human speech. Contact centers use speech analytics for speech recognition, speech-to-text translations, and sentiment analysis. 

Speech analytic software can analyze 100% of customer conversations, allowing contact centers to be more proactive and have a precise understanding of what occurs during customer interactions. 

This analytics has recently gained popularity due to advances in artificial intelligence (AI) and machine learning. It’s now more accurate and efficient than ever before, making it a valuable tool for contact centers. 

Key metrics

Analyzing speech and converting speech to text can help with tracking and managing:  

  • Sentiment
  • CSAT (customer satisfaction) 
  • Agent effectiveness
  • Customer and agent effort
  • Quality assurance 

2. Text Analysis

Text Analysis graph
OI reports on customer inquiries, sub-inquiries, and root causes using the customers' own words. 

What is it & why is it important?

Text analysis, also called text analytics, involves understanding natural language text and extracting useful information from it. Text analysis tools can consume text data from various sources, including emails, phone transcripts, surveys, customer reviews, and other documents. 

With text analytics software, your contact center may identify trends, gauge the proportion of users experiencing a specific problem, and adjust your goods, services, and customer support based on this feedback. 

Text analysis also often includes sentiment analysis.   Sentiment analysis uses machine learning and AI to pull subjective elements from text to determine the feelings and opinions of the speaker. This can help organizations understand where and when customers are having a negative (or positive) experience. 

With text analysis, businesses are better equipped to understand and analyze customer sentiment, intelligently classify documents, and improve written content. Text analysis software may be used in conjunction with other analytics tools, including big data analytics and business intelligence platforms. 

Key metrics

Similar to speech analysis, text analysis can help track: 

  • Sentiment
  • CSAT 
  • Agent effectiveness
  • Interaction cost 

While Operative Intelligence isn’t solely a text analysis platform, it does conduct text analysis to provide actionable insights. OI analyzes 100% of inbound interactions and shares the root causes of overall inquiries using actual customer words. 

The platform also shows the volume, cost, and sentiment of every inquiry, so contact center leaders know which changes will drive the highest impact improvements in their customer experience, and operational and frontline performance. 

3. Predictive Analytics

Predictive Analytics tool

What is it & why is it important?

Predictive analytics uses data mining and mathematical modeling techniques to identify data patterns and predict future events, particularly client behavior. 

This technology can be used to identify potential problems and recommend solutions. By understanding past customer behavior, contact center agents can provide more accurate up-selling, improvements, or resolutions to common issues. Predictive analytics takes the guesswork out of these situations by providing data-backed predictions for what a customer is likely to do next.

Predictive analytics can also help with workforce management by providing predictions for the busiest times of day and year for contact centers. This allows managers to staff as needed. 

Key metrics 

Predictive analytics can uncover: 

  • Conversion rates
  • Churn rates
  • Employee and client engagement 
  • Workforce requirements 

4. Customer Analytics

contact center analytics platform
Operative Intelligence contact center analytics platform

What is it & why is it important?

Customer analytics, also known as customer data analytics, works to recognize, attract, and retain the most lucrative customers. Different tools identify high-value customers and suggest proactive ways to interact with them. These solutions integrate with widely used email clients, social media platforms, web content management systems, and consumer loyalty programs. 

Key metrics 

Customer data analytics looks at: 

  • Sentiment
  • Customer Effort Score
  • Net Promoter Score (NPS)
  • CSAT
  • First Contact Resolution (FCR)
  • Customer Retention 

5. Employee Satisfaction Analytics

employee satisfaction contact center analytics

What is it & why is it important?

Employee engagement analytics integrates employee engagement with other HR and non-HR data sources to identify the elements that affect retention and satisfaction. This modern standard practice in advanced staff management and retention is a subgenre of workforce analytics. 

Employee engagement analytics makes it possible for you to spot repeating trends in your workforce, pinpoint the underlying causes, and formulate a thoughtful response. With the correct data and data architecture in place, employee engagement analytics may generate reports and in-depth studies for each team, location, and business line. Effective employee engagement management requires teams to report on and monitor employee engagement. 

Key metric

Employee satisfaction analytics will commonly look at: 

  • Employee engagement
  • Employee happiness 
  • Retention rates
  • Attrition rates 

6. Agent Analytics

agent performance guide
Operative Intelligence uncovers the span of agent performance at the inquiry level.

What is it & why is it important?

Agent analytics is the process of monitoring agent performance to help drive effectiveness and productivity.  

Agent analytics dashboards allow managers to track the contact center's performance, identify areas that need improvement, and make data-driven decisions. It can also show how different campaigns are performing and help pinpoint where resources need to be allocated.

The right reporting and analytics dashboard can give agents insights into how they are performing, what type of calls they are receiving, and how they can improve their interactions with customers. This information can help contact centers improve customer satisfaction rates and reduce the number of callbacks.

Key metrics 

Agent analytics reviews: 

  • FCR
  • AHT
  • CSAT
  • ACW
  • Effectiveness
  • Average talk time (ATT) 

Operative Intelligence provides your contact center with an engaging, real-time dashboard that makes it simple for them to identify areas they can improve based on customer needs. You can also see where performance variability is driven at a team and agent level. Team leaders can coach with precision and drive measurable improvements. 

How to Set Up your Contact Center Analytics

contact center analytics team

1. Data Preparation

Data preparation is the process of getting your data ready for analysis. This may include cleaning up your data, formatting it in a way that makes it easy to analyze, and adding any additional information you need to make sure your analysis is accurate.

The data preparation pipeline includes: 

  1. Accessing the data: This could either be from an internal source like a database or external sources such as surveys and customer feedback.
  2. Ingesting: Once the data is accessed, you’ll need to fetch it into a database or other environment where it can be used for analysis.
  3. Cleansing: This process includes eliminating missing, duplicate, or incorrect values, and ensuring that the data is consistent and in the correct format.

2. Automate Data

Automating the data-gathering process will allow you to collect and analyze all of your customer interactions in one place. Automation eliminates manual data entry, saves time, and ensures accuracy. 

3. Create Dashboards

Reporting and analytics dashboard provides a visual display of contact center metrics and KPIs. A great reporting and analytics dashboard provides insights into call volume, average wait time, abandoned calls, customer satisfaction, contact center agent performance, and more. 

4. Utilize Integrations

Utilizing integrations with other software or web-based applications is a great way to streamline your contact center analytics setup. By integrating the data from different systems, you can easily analyze customer interactions from multiple perspectives. This will create an insightful overview of customer experience and provide valuable insights into how you can improve your service. 

By integrating data between systems, information can be seamlessly shared between departments. This eliminates mistakes and ensures that everyone in your business has the data they need to perform optimally. 

5. Configure & Maintain Data

You’ll also need to ensure that the data is properly maintained. This includes regularly updating your data to reflect any changes in customer behavior or contact center operations. It’s also important to filter out inaccurate or irrelevant data.

By taking these measures, you can ensure that your contact center analytics provides valuable insights that can improve your customer service operations.

6. Pull Key Insights 

There are a few key things you need to pull key insights: 

  • Tracking numbers for each agent and each campaign
  • Recording of all customer interactions, including the agent's notes
  •  A way to analyze the data

Once you have these things in place, you can start pulling key insights from your data. For example, you can track how many contacts an agent is handling in a day, what kind of wait times are being reported, and what percentage of customers are being contacted more than once.

This information can help you identify areas where your agents need improvement and where you need to make changes to your campaigns or customer service strategy.

However, not every contact center analytics platform requires this amount of work and preparation. For example, Operative Intelligence automatically transforms an organization's existing call center data into actionable insights–without any data preparation required on the user end.  

Once Operative Intelligence can access an organization’s data, the platform provides insights in two weeks without any manual input. Each of these seven steps are automated–saving organizations and contact center leaders time and reducing overall costs for customer service. 

Pros and Cons of Using Contact Center Analytics

contact center agent at work


  • Boost Agent Efficiency: Optimizing agent operations is one of the most essential benefits of analyzing your contact center data. Agents can evaluate their performance and receive feedback about what is working well and what they can improve upon in each contact. 
  • Maximizing Revenue: Better customer interactions can improve customer retention, boost upsells, and increase customer loyalty. 
  • Cross-Channel Insights: Insights across all customer interaction provides visibility across the customer journey. 


Data preparation is incredibly important: without clean and configured data, analysis platforms can waste time reviewing irrelevant or inaccurate data, clouding outcomes and slowing down procedures. 

And while several analytics solutions can gather data, they also need to provide real-world insights about customer interactions – and how they can improve. 

To effect real change, organizations must understand the underlying reason why customers are contacting customer service. 

The Best Contact Center Analytics Software

While contact  center analytics can present challenges, the best contact center analytics software addresses those issues – and more. 

Operative Intelligence is the next-gen analytics platform for enterprise call and contact centers. Unlike traditional contact center analytics tools, Operative Intelligence automatically transforms an organization's existing contact center data into real-world, actionable insights. It uses artificial intelligence (AI) technology to analyze and quantify chances for performance improvement at the contact center. 

Operative Intelligence identifies:

  • The reasons customers contact support at scale
  • Insights by inquiry driver: volume, cost, sentiment, satisfaction, resolution
  • The underlying reasons for consumer complaints and the financial costs to the business
  • The best potential for contact center automation and each opportunity's ROI
  • What’s driving increases in handle time and how to fix it
  • What’s driving negative customer sentiment and satisfaction
  • Effectiveness of the agent and team (resolution + satisfaction) for all queries

And remember that time-consuming data preparation? Operative Intelligence integrates with your current contact center systems without requiring data preparation from your end. It provides a clear picture of how your contact center is functioning as well as the journeys customers take using your service or product. 

Improve Your Contact Center Analytics with Operative Intelligence

With the help of contact center analytics, organizations can improve customer satisfaction while enabling agents to provide a better customer experience. 

Operative Intelligence provides advanced analytics that empower contact center leaders to have data-based conversations with leadership about creating better products, better services, and happier customers. 

Ready to drive changes at every level of your business with irrefutable data?  

Book a demo with Operative Intelligence today.

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