Sentiment analysis is the process of classifying and scoring the emotional tone expressed during a customer conversation—whether it takes place via a voice call, email, SMS, or web chat—to determine whether the customer’s or contact center agent’s attitude is positive, negative, or neutral. 

Also referred to as opinion mining, it is commonly used to capture voice of the customer data to give businesses a deeper understanding of customers’ feelings about the service experience, agent performance, products, services, or the overall brand.

In contact centers, sentiment analysis can be used to monitor agent performance for emotional intelligence skills (e.g., empathy), service quality, and adherence to compliance standards. 

Sentiment analysis increases supervisor and quality team efficiency by allowing them to zero-in on specific interactions, such as those with negative customer sentiment. Low sentiment scores can help managers pinpoint agents that require additional coaching, training gaps within teams, or process breakdowns.

Contact center managers can also use customer sentiment analysis to identify high-performing agents for recognition and to incorporate their best practices into agent training programs.

Besides improving agent performance, customer sentiment analysis is a useful method for monitoring overall center performance. It allows contact center managers to quickly and easily identify negative sentiment patterns within call types or topics. This helps to alert managers to sources of friction that impact the customer experience, and effectively closes the gap between the identification and resolution of an issue.