Words are important in customer service, but not as important as feelings. Call duration or resolution are typical call metrics, but they don’t reflect how customers feel during or after a service interaction.
Progressive enterprises are no longer trying to focus only on the words being said, but on the how and the why they are being said. Conversational artificial intelligence (AI) sentiment analysis is helping to turn voice from an unstructured channel into an actionable well of emotional intelligence (EI).
Voice Calls Hold a Clue to Customer Satisfaction
The key is in every emphasis, hesitation, and cadence. AI-powered sentiment analysis can provide emotional context about each and every phone conversation in near real-time. Sentiment models are based on AI that has been trained on a massive corpus of text in a given language, and can therefore recognize a range of emotions such as anger, happiness, impatience, or bewilderment as a customer speaks.
This creates the opportunity to act on that insight, not only to see the conversation differently, but to intervene before an issue might arise. Customer support operations can trigger call escalation or routing to senior agents, or even churn prediction or retention strategies, before they become necessary.
AI listening can also provide insights into the agent’s communication skills, empathy, and the ability to explain things clearly during an emotionally charged interaction. In this manner, sentiment mapping can evaluate both sides of a conversation.
Sentiment AI into Tangible Business Impact
Sentiment analysis helps to close the loop between intelligence and insight. By incorporating AI sentiment listening into their enterprise-class voice systems, contact centers and business communications teams can start to use the technology as:
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Real-time coaching
Call supervisors can be alerted to step in or coach agents live during interactions as they become more emotionally charged.
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Automated alerts
Negative or “sentiment dropping” conversations can automatically ping supervisors or managers as they happen and feed into other escalation routes.
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Retention prediction
Sentiment trends can be layered over CRM and usage data to identify risk and predict which customers may be churning or at risk.
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Automated quality management
With AI listening, random call monitoring can be replaced by complete data collection and analysis to feed into improvement processes.
The best technology vendors should be focused on low-bias, contextual AI models to avoid privacy or fairness issues.
The most advanced solutions are using not only voice biometrics for speaker identification, but are also deploying natural language understanding and contextual emotional modeling for improved accuracy.
The Future of Customer Experience: Empathy at Scale
AI-powered sentiment analysis is quickly becoming a “must-have” and is an important step in taking the pulse of your customer experience (CX). Sentiment modeling allows organizations to become more empathetic and customer-focused at scale, giving a better idea of the customer, your employees, and how and why they behave.
Better decisions and loyalty come from putting sentiment intelligence to work. To learn more about AI-powered sentiment analysis and how it can transform your contact center into a real-time emotional intelligence enterprise, contact our solutions team.
