Call center analytics: Everything you need to know
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 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.
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 improving agent performance, call centers can improve their service delivery and operational efficiency.
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.
Creovai can analyze customer interactions to understand their sentiment and emotions, which can help managers identify customers at risk of churning and take steps to prevent it.
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.
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.
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.