What is contact center analytics?

Contact center analytics is the systematic collection and analysis of data from customer interactions to find insights into the conversations. This process allows businesses to evolve from manual customer service reviews to in-depth information analysis, uncovering nuances such as intentions, sentiments, and emotions.

The transformation of analytics in contact centers intensifies with the integration of generative artificial intelligence (gen AI) technologies. AI-powered data analytics allows a deeper understanding of customer inquiries and automates tasks, ultimately enhancing call center performance. Research by Deloitte shows that 81% of contact center executives are also investing in AI to improve agent experience and operational efficiency.

Why AI-powered contact center analytics software drives performance

By 2025, 60% of service organizations will increasingly incorporate analytics technologies into their operations, including tasks such as analyzing voice and text interactions, according to Gartner. With AI-driven conversation intelligence software like Echo AI, your contact center uncovers hidden insights in every call, email, or social media conversation, effectively understanding what customers want.

Here's what AI-based contact center analytics tools can do for your operations:

• Identification of hidden patterns: AI instantly detects trends, recurrent problems, and preferences across all channels, providing an overview of your operations.

  
  

• Agent performance optimization: AI monitors individual and team performance and automatically reviews calls and text interactions. It also identifies coaching opportunities to improve average handle times and reduce call abandonment rates.

  
  

• Customer churn reduction: AI proactively classifies at-risk customers, segments them effectively, and personalizes action to address their concerns.

• Data-driven decision-making: AI processes real-time insights, helping you make better and quicker decisions. It prepares your contact center to constantly adapt, amplifying your competitive edge.

• Operational cost saving: AI helps you to reduce staffing levels, find areas for development in self-service channels, and make self-service channels more effective by learning from user behavior.

Types of contact center analytics solutions

Contact/call center analytics solutions include various types of data analysis that feed your business with valuable information about customers, agents, and workflows. 

• Interaction analytics: Reviews all conversations across phone calls, chats, emails, and social media, presenting a clear overview of the customer journey.
  
  

• Speech analytics: Scans the verbal content of spoken conversations. It recognizes keywords, phrases, and topics discussed.
  
  

• Voice analytics: It helps you to understand customer sentiment by identifying tone of voice, stress, speaking rate, and other non-verbal signs on a call. 
  
  

• Call analytics: Offers a broader picture of the entire call, combining data from various sources – call recordings, CRM systems, and agent performance metrics.

  
  

• Text analytics: Analyzes written communication channels, such as chats and emails, to extract trends and common topics.

  
  

• Agent analytics: Monitors and improves agent performance based on customer satisfaction and resolution rates, allowing training and development.

  
  

• Customer satisfaction analytics: Gauges and deeply reviews customer sentiments to raise satisfaction rates, such as net promoter score (NPS), customer effort score (CES), and customer satisfaction score (CSAT).

How to analyze contact center data

To improve your contact center performance, consider the following steps in your data analysis:

1. Map and review common customer pain point

Start by checking out the most important type of data: what your customers are sharing. Contact center teams surveyed by HubSpot claim that customers are more informed than ever – and have higher expectations when interacting with businesses than in the past.

By analyzing common customer concerns regarding the service, your team proactively responds to patterns of negative sentiment, reducing call volume and elevating the tone of conversations. Echo AI detects these trends in real time, generating answers to critical questions.

Metrics to follow:

• CSAT score: Monitor customer satisfaction

• NPS: Gauge customer loyalty

• CES: Measure customer effort 

• Churn rate: Analyze customer attrition

2. Look at the team's performance

Employee data is another significant source of information, helping you to assess call center agent productivity, quality, and attitude when dealing with customers. 

Call center analytics tools automate the review and scoring of calls and text messages, tracking metrics that lead to training opportunities. Echo AI further identifies areas that need coaching by analyzing patterns in agent performance and provides constructive gen AI feedback based on sentiment, resolution, and QA scores.

Metrics to follow:

• Average handle time (AHT): Manage call duration

• First call resolution (FCR): Verify agent efficiency

• QA score: Track quality

Adherence to schedule: Confirm team compliance

3. Identify opportunities to streamline your operations

Another crucial type of data to prioritize in your analysis is call center operation ratings. Consider, for example, that your contact center experienced an increase in phone call volume over the past year. 

By deeply analyzing metrics related to workflows, schedules, and other operative processes, you prepare your staff to work more effectively during peak times. It also saves time and costs associated with extra shifts and takes your CX to the next level.

Metrics to follow:

• Call volume: Shows customer demand

• Call abandonment rate: Tracks unhandled calls

• Peak-hour traffic: Identifies periods of high call volume

Cost per call: Indicates cost-effectiveness

Contact center analytics best practices

Here are five best practices to boost the effectiveness of contact center analytics:

1. Define clear goals: Remember, data analysis must serve a strategic purpose. Before implementing a new platform, set what objectives you aim to achieve with analytics. Define your key performance indicators (KPIs), then make sure your call center data analytics align with your business objectives to expand your CX strategy.
   
   

2. Benchmark competitors: What information do your competitors share about customer experience? Internal data insights are gold but don't forget to incorporate contact center benchmarks to elevate your performance. By constantly comparing key metrics such as CSAT and FCR, and evaluating service provided on contact center channels, you identify areas where you excel and find opportunities to stay ahead of the curve.

   
   

3. Choose a solution to scale: Predictions by McKinsey bring out the future of customer service as omnichannel, highly personalized, and AI-supported to deliver engaging experiences. Finding the right contact center solution involves analyzing factors like integrations with other platforms, the types of data the software can handle, its ease of use, and the support offered.

   
   

4. Empower your team: Contact centers with AI-driven solutions not only help customers on their journey but also improve agents' skills and provide opportunities for team development. Ensure that your team is prepared to use analytics tools and is aware of the benefits of AI and data analytics to take ownership of their performance actively.

   
   

5. Update processes constantly: Keep your analytics strategies updated and aligned with your business's evolution. Echo AI dashboards and reports help you closely monitor trends and adapt your approach based on ongoing data, refining goals and changing priorities when needed.

Analyze your data with the best AI-powered contact center solution

With Echo AI, you unify omnichannel contact center processes, covering all types of interactions in one accessible place.

Before choosing your contact center analytics software, ask yourself the following questions and discover how our AI-powered platform helps you to foster a customer-centric approach: