Call center analytics: Everything you need to know
Call center leaders can’t afford to operate on hunches or gut feelings. They need to understand what is and isn’t working in customer interactions so they can avoid wasted spend, reduce customer and agent churn, and keep customer satisfaction high. That’s where call center analytics come into play.
Let’s take a look at everything you need to know about call center analytics, from the data sources to the common use cases to the technology that supports it.
Call center analytics give businesses valuable information about their customer base by analyzing and interpreting customer conversations in a call center. This includes incorporating both the call transcripts and other data about the calls, which can include metrics such as call volume, average handling time, first call resolution rate, customer satisfaction, and agent productivity. This data can be used to identify trends, patterns, and areas for improvement.
Armed with post-call analytics, leaders can gain insight to improve real-time interactions, the overall performance of the call center, and the customer experience.
Call center analytics can also give insight into the performance of individual agents and teams, which can help to identify areas where additional training or real-time guidance may be needed.
Additionally, call center analytics can help leaders understand customer behavior and preferences, which can help companies improve the customer experience, increase sales and revenue, and reduce operating costs.
Call center analytics can also provide insight that aids in evaluating the effectiveness of marketing campaigns, promotions, and other initiatives that are aimed at driving customer interactions.
Call center analytics technology uses machine learning and artificial intelligence (AI) to automatically analyze large sets of data and extract meaningful insights. For example, Creovai automatically transcribes and analyzes call center interactions to identify specific call reasons, agent behaviors, customer sentiment, discussion topics, and more.
Without call center analytics technology, call center leaders must rely on manual call evaluations to understand what’s happening in their interactions. (Many of our customers have talked about relying on manual QA scoring, limited information from call dispositions, or anecdotal evidence from call center agents before adopting Creovai.) This manual approach is time-consuming and difficult to scale, often leaving call centers with a very limited view of their interactions and causing them to miss opportunities for improvement.
By allowing businesses to understand their customer service and sales interactions at scale, call center analytics technology enables data-backed decision-making. It can help call center leaders lower operational costs, identify and reduce churn risk, improve agent performance, increase sales, and more.
Call center analytics can help leaders across the business make informed decisions about processes, products, services, and the overall customer experience. Here are a few of the major benefits our customers have seen when using common ways businesses apply their call center analytics:
Creovai analyzes customer interactions to identify common pain points and issues that cause customers to be dissatisfied, frustrated, or contact the company multiple times. By addressing these issues, companies can reduce customer effort and increase loyalty.
Real-world use case: Connexus Credit Union uses Creovai to analyze how successful their agents are at handling difficult member interactions—and how the language their agents use impacts the member experience. In their first month of analyzing their calls with Creovai, they discovered many of their agents were using language that made them sound unsure, which impacted member confidence. They began coaching agents to use advocacy-focused language and saw this behavior increase by over 40%. You can read more about their story here.
Call center leaders can use Creovai to analyze agent performance and identify what specific agents or teams or doing well and what they are struggling with. Managers or supervisors can use these insights to inform coaching sessions or improve their real-time workflows and scripts with Creovai Agent Workflow. By focusing on agent performance, call centers can improve their service delivery and operational efficiency.
Real-world use case: The British Columbia Lottery Corporation uses Creovai to identify high-effort interactions and calls in which customers express frustration. They analyze what agent behaviors and responses work best during these difficult interactions, then coach all agents to adopt these behaviors. Since overhauling their approach to QA and agent coaching with Creovai, they have seen a 220% increase in their Net Promoter Score. You can read more about their story here.
Creovai identifies common customer needs and call reasons so managers can improve their self-service options, such as chatbots or virtual assistants. This can reduce the effort required for customers to get their issues resolved or their questions answered, which leads to a better customer experience while also reducing overall call volume.
Real-world use case: TwinStar Credit Union uses Creovai to identify its best phone interactions, then trains its chatbot on the language agents use in those conversations. This has improved the chatbot’s responses to FAQs and reduced its unsure rate by 75%. You can read the full case study here.
Creovai can analyze customer interactions to understand their sentiment and emotions, which can help managers identify customers at risk of churning or spreading negative word-of-mouth and take steps to prevent these negative outcomes.
Real-world use case: Consumer goods company Thrasio uses CSATai, Creovai’s AI-powered predictive CSAT model, to detect when a customer is likely to rate themselves as dissatisfied with a contact center interaction. Customer service tickets with a predicted dissatisfied score are sent to a human agent to review and follow up with the customer, helping the company turn a potentially negative interaction around. This proactive approach has helped them achieve a best-in-class 97% CSAT score. You can find out more about Thrasio’s approach here.
Creovai can analyze customer interactions to identify opportunities for upselling and cross-selling, which can grow revenue and increase customer loyalty by providing them with products or services that better meet their needs. This technology can also identify specific agent behaviors that positively or negatively impact conversion rates, enabling contact centers to improve their coaching and real-time guidance and close more sales.
Real-world use case: A Fortune 50 telecom company used Creovai to determine that when agents directly asked for the sale, win rates increased significantly. They began prompting their agents to use this behavior on calls related to a specific product, and they saw their sales conversion rate for this product increase by 218%. You can find out more here.
Call centers use a variety of data sources to optimize performance. Types of data in call center analytics include:
This is data from customer calls, such as call volume, duration, transfers, holds, and call outcomes. These metrics can help call centers understand how efficiently they are operating and where there is room for improvement. Analyzing historical patterns in call data can also help managers forecast call volume and optimize agent staffing and scheduling.
This can include data captured in a CRM, such as records of previous interactions and account information, that can help call centers personalize service when assisting customers. Creovai lets you integrate customer data sources with your real-time guidance so agents can work efficiently and deliver great customer service without navigating between multiple screens.
Creovai uses speech analytics to analyze the words and phrases used in customer-agent interactions. This helps call center leaders understand customer needs and preferences, identify customer pain points, and evaluate agent performance.
Call centers that offer omnichannel customer service need to track interaction data across channels. Creovai can analyze data from multiple channels, including phone calls, chat sessions, and customer service tickets, to help call center leaders gain a complete understanding of customer interactions and behaviors.
It’s not unusual for customers to use multiple service channels. Some customers may gravitate towards self-service channels, such as chatbots, when they have a relatively straightforward question, and opt to speak to a live agent when dealing with a more complex issue. In some cases, customers may start on a self-service channel but escalate to a phone call when they’re unable to find the information they’re looking for.
Unfortunately, channel switching increases customer effort and can create a negative experience. According to research from the book The Effortless Experience: Conquering the New Battleground for Customer Loyalty, customers who pick up the phone after trying another service channel first are 10% more disloyal than those who resolve their issue in their first-choice channel.
Call centers should use conversation analytics to understand the omnichannel customer experience—especially the specific issues and factors that cause customers to switch channels. Omnichannel analytics can help call centers answer questions like:
Analyzing conversations across channels helps call centers identify and address the biggest sources of friction in the customer journey. They can use these insights to improve their self-service channels and make the escalation experience as low-effort as possible when customers do need to speak to a live agent.
You can't talk about call center analytics without mentioning predictive analytics and actionable insights.
Predictive analytics uses statistics, machine learning algorithms, and data mining to analyze historical data and make predictions about future events or behaviors. In the context of call center operations, predictive analytics can be used to forecast call volume, identify potential issues and opportunities, and optimize agent staffing and scheduling.
For example, predictive analytics can forecast call volume based on historical data, such as the time of day, day of the week, or season. This can help companies optimize their staffing levels and ensure they have enough agents available to handle their expected call volume.
Predictive analytics can also identify patterns and trends in customer interactions, such as common issues or pain points. For example, Creovai’s proprietary CSATai model predicts a customer satisfaction score for every interaction based on the phrases the customer and agent use. This can help companies proactively identify and address issues causing dissatisfaction—or identify factors causing satisfaction that they can build into their processes or agent training.
Actionable insights are information extracted from your data that can be used to drive specific actions or decisions. In the context of call center operations, actionable insights can be used to improve the performance of call center operations by identifying issues and opportunities--and providing specific recommendations for addressing them.
For example, actionable insights can include identifying which agents need additional training or coaching, which products or services are most in demand, or which customers are at risk of churning.
Overall, predictive analytics and actionable insights can improve the performance of call center operations by providing companies with valuable information into customer interactions, agent performance, and overall call center operations. These insights can be used to identify issues, optimize staffing and scheduling, and improve the overall customer experience.
To truly optimize performance, contact centers need more than raw data—they need a clear view of the metrics that matter. Conversation intelligence platforms like Creovai make it easier to track and understand a wide range of key performance indicators (KPIs), blending traditional call center metrics with deeper insights into agent performance and customer experience.
Below are a few of the types of KPIs you can track with call center analytics:
Standard KPIs such as average handle time (AHT), first call resolution (FCR), and call volume are foundational for call centers. A conversation intelligence platform can track these KPIs while providing additional information about the factors impacting them. Creovai uses advanced statistical models and regression analyses to identify the root causes of repeat contacts, long handle times, and more.
Call centers typically track QA criteria (such as using a proper greeting and adhering to compliance standards) in agent scorecards. A conversation intelligence platform like Creovai can automate objective QA criteria, letting call centers track agent performance across 100% of their interactions. Creovai can also help call centers track agent behaviors that are proven to positively impact the customer experience, such as using advocacy language and setting expectations.
Many businesses rely on customer survey scores (such as CSAT, NPS, or the Customer Effort Score) to measure and track their customer experience. However, surveys suffer from low response rates and sample bias, making it difficult for businesses to use them as a tool for meaningful CX improvement. Conversation intelligence platforms with predictive analytics, like Creovai, can fill in the survey gaps by predicting how a customer would have rated their satisfaction, effort, and sentiment based on what they said in their interaction.
By centralizing all these KPIs in one platform, contact center leaders can make faster, smarter decisions. Whether it’s recognizing top performers, addressing training gaps, or adjusting workflows, they can equip their teams with the insights needed to continuously improve both operational outcomes and customer experience.