10 AI Use Cases: Call Centre Performance & Effectiveness

Artificial intelligence (AI) has entered virtually every industry to assist with tasks. Effective call centres leverage AI daily to improve their operations, from translating customer conversations in real time to minimizing excess tasks for agents with the goal of boosting productivity.

In this post, CallMiner explores 10 AI use cases for call centre performance and effectiveness, including:

• Real-time agent feedback and coaching
• Redacting sensitive information
• Translation
• Multichannel customer journey mapping
• Sentiment and emotion analysis
• Transcription
• Customer conversation organization
• Learning customer behavior trends
• Improving workflows
• Offering self-serve tools to customers
• Frequently asked questions

AI Use Case #1: Real-Time Agent Feedback and Coaching

AI helps call centres identify areas of opportunity for agents to upsell, provide personalized offers, or assist customers better by listening in on conversations using conversation analytics technology.

Conversation analytics captures specific words and phrases that trigger real-time coaching and feedback for agents, including positive reinforcement, agent empathy scoring, and compliance scripts.

AI Use Case #2: Redacting Sensitive Information

Customers shouldn’t need to worry about their sensitive information provided over the phone or email getting into the wrong hands.

AI can listen for and redact sensitive information when it’s provided across multiple customer contact channels, allowing agents to get the information they need without having access to sensitive data.

AI Use Case #3: Translation

AI-driven call centre platforms with language integrations can help agents understand customers from all over the world. AI tools listen to or read customer conversations as they happen, translating them in real time for agents to serve customers as efficiently as possible.

AI Use Case #4: Multichannel Customer Journey Mapping

Customer journey mapping helps businesses understand how customers find and interact with them and their services or products.

Using AI-powered tools, organizations can analyze the customer journey along every touchpoint, from email to website to social media and beyond, and use that data to improve customer experiences.

AI Use Case #5: Sentiment and Emotion Analysis

Sentiment analysis and emotion analysis use AI-driven algorithms to determine customer opinions and feelings during their interactions with a call centre.

Call centres can use this information to understand how a conversation is going in real time, learn how well products or services are serving customers, and improve brand experience.

AI Use Case #6: Transcription

AI tools can transcribe phone conversations between agents and customers to text, providing a full transcription to share with other agents when escalating calls.

Then, AI processes can analyze the transcription to uncover insights about the conversation, such as customer sentiment, emotion, and customer experience.

AI Use Case #7: Customer Conversation Organization

AI automates the process of organizing customer conversations in call centres. For example, AI tools can detect specific words and key phrases to categorize conversations by type, such as refunds, product complaints, fraud, or praise.

Or, AI-driven technology can organize conversations by sentiment, contact method, length, or whatever metrics make the most sense for your call centre.

AI Use Case #8: Learning Customer Behavior Trends

By analyzing customer data, like where customers call from, what times they’re likeliest to call, or what words and phrases make them feel most appreciated, AI can help call centres identify patterns in customer behaviors.

As a result, call centres can staff more efficiently, offer support where it’s needed most, and give customers more of what they want and expect from the support team.

AI Use Case #9: Improving Workflows

AI can connect multiple tools together, keeping the entire call centre’s workflows streamlined and productive.

From speech recognition software to CRM integrations, connecting all tools to one another through one AI-powered system allows each member of the team to access relevant data and insights and provide a more cohesive customer experience.

AI Use Case #10: Offering Self-Serve Tools to Customers

Thanks to automation processes, AI allows customers to serve themselves when it makes sense, like when they need a quick answer from a chatbot or want to request their account information over the phone.

For example, interactive voice response (IVR) systems have become a critical part of the call centre. IVR systems move customers through an automated phone system that listens for customer responses and provides helpful information based on those responses.

Frequently Asked Questions

How Can AI Help My Contact Centre?

AI digs into customer conversation data to provide contact centres with insights that improve conversations and customer experiences.

AI-powered tools can redact sensitive information, provide real-time agent coaching, detect customer sentiment, and understand customer behaviors, resulting in a cohesive, effective support experience.

What Are the Key Things Contact Centre AI Can Do?

Contact centre AI tools identify trends in customer behaviors and help agents and managers find areas of opportunity to create and enhance customer experiences.

AI can also offer self-service tools to customers, translate and transcribe conversations, hide sensitive information, improve workflow, and map customer journeys across multiple channels.

Will Call Centres Be Replaced by AI?

AI is meant to assist call centres rather than replace them. Many customers prefer a human touch when interacting with businesses, which AI can’t replace.

Instead, call centres can welcome AI-driven tools into their workflows to help them understand their customers’ needs and wants, leading to better customer service practices.

This blog post has been re-published by kind permission of CallMiner – View the Original Article

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: CallMiner