Modern call centers have implemented artificial intelligence (AI) into their operations for several years, providing helpful features like auto-dialers, interactive voice response (IVR) systems, and real-time agent guidance. AI analytics has further assisted call centers with drilling down important metrics, reviewing conversations, and better understanding customers. 

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Keep reading to learn more about the role of AI analytics in call center performance, including: 

• How AI analytics has transformed the modern call center 
• 5 ways AI analytics improves call center performance 
• Agents get real-time feedback 
• Supervisors can readily identify strengths and areas needing improvement 
• Call centers learn customer behavior 
• AI analytics help reduce costs 
• Call centers may see reduced agent turnover 
• Analyze conversations to deliver better performance 
• Frequently asked questions 

How AI analytics has transformed the modern call center  

Earlier versions of call centers required agents to be a lot more hands-on with tasks like call routing, after-call notes and organization, customer categorization, and QA scoring. Today, call centers rely heavily on AI to complete these tasks while gaining more insightful information than they could gather manually, like customer sentiment and customer journey mapping. 

AI analytics specifically targets how a call center gathers and makes sense of data. AI technologies can analyze customer interactions across channels, customer satisfaction scores, and customer behaviors to help call centers make positive changes for customers. 

5 ways AI analytics improves call center performance  

Call centers can turn to AI analytics to enhance agent performance, better understand their customers, and improve efficiency to reduce costs. 

1. Agents get real-time feedback 

One type of AI analytics, conversation analytics, monitors and breaks down conversations between agents and customers into actionable insights. Conversation analytics determines if a customer needs more assistance than an agent is currently providing by analyzing sentiment, emotion, and language, and identifies when and how an agent can provide that help.  

As a result of conversation analysis, an AI-powered call center platform can provide feedback to agents in real time as they communicate with customers to steer the conversation in the right direction, increase sales, and improve customer satisfaction. This happens in the form of live suggestions straight to agent dashboards and insights delivered to supervisors as calls happen, allowing for speedy coaching opportunities.  

2. Supervisors can readily identify strengths and areas needing improvement 

AI analytics helps supervisors see the strengths and improvement areas of the call center as a whole and each agent individually to ensure the best customer experiences. For example, with CallMiner’s Eureka Analyze, supervisors can view customer scoring and agent quality scoring in an easy-to-use dashboard to learn more about what each agent does well and where they can focus on improvement. 

This drill-down allows supervisors to coach agents in key areas and identify ways for the team to enhance customer interactions. 

3. Call centers learn customer behavior 

By tracking customer interactions, AI analytics can determine what actions tend to drive certain customer behaviors, like deciding to buy a product or service or requesting a call escalation. These insights give call centers the information they need to meet customer needs and improve customer experiences. 

4. AI analytics help reduce costs 

AI analytics can increase efficiency by handling many tedious tasks that call center agents would typically be responsible for. By determining where calls need to be routed, how to organize customer calls, and when to offer additional help, like a chatbot, AI analytics can cut down on the time it would otherwise take to manage customer contacts. In turn, a call center could reduce labor costs and costs associated with customer churn. 

CallMiner helped Real Time Resolutions reduce costs & improve the #customerexperience. Check out their success here: https://t.co/2njHK1qAMN pic.twitter.com/JpTigms8EO

— CallMiner, Inc. (@CallMiner) May 26, 2017

5. Call centers may see reduced agent turnover 

AI analytics may help call center agents feel more prepared to handle customer needs. By analyzing when to provide feedback and suggestions and reducing the need for agents to complete repetitive tasks, agents could feel more confident in their abilities to help customers and, as a result, more satisfied with their jobs.  

Analyze conversations to deliver better performance 

Conversation intelligence software helps today’s call centers work smarter instead of harder. AI analytics constantly works in the background to learn more about your call center, your agents, and your customers, providing deep, accurate insights that allow your call center to deliver the performance customers expect.  

Request a demo to discover the CallMiner platform’s top benefits, like automated agent scoring, real-time agent coaching, automated scripts, and root-cause analysis to identify problem areas. 

Frequently asked questions 

How can AI analytics improve customer service? 

AI analytics scours customer conversation and behavior data to provide actionable insights to call center agents and teams, allowing them to learn more about what their customers want and how to address their needs. AI analytics can tell call centers information like what phrases tend to increase sales, at what point in the buying journey customers drop off most, and which agents are most successful at preventing customer churn. 

How can AI analytics improve call center agent performance? 

AI analytics provides customer and performance data that shapes how agents interact with customers. For example, AI can analyze a customer conversation, identify areas in the conversation that lean negative, and suggest ways for the agent to improve the conversation’s trajectory. Supervisors can also gather data about agent performance, like positive phrasing, call time, and overall helpfulness, to use for training and coaching. 

How do you analyze data in a call center? 

AI analytics software like the CallMiner platform can help call centers analyze large volumes of data from customer conversations. The software uses multiple forms of AI technology, including conversation analytics and speech recognition, to provide a holistic view of performance from individual agents to the call center as a whole.