Sentiment analysis is the practice of leveraging natural language processing (NLP), machine learning, and computational linguistics to assess customer input and gain insights into their attitudes, emotions, and opinions regarding various aspects like brands, product lines, and marketing campaigns. By mining data sources such as social media comments, blogs, and product reviews, sentiment analysis classifies input into positive, neutral, or negative categories, offering valuable information through reporting tools.

Sentiment analysis is a valuable tool within contact centres as it helps assess the nature of customer comments within various communication channels, such as phone calls, text messages, emails, and chat sessions. This enables contact centres to route customer interactions based on sentiment, directing them to agents who excel at handling specific emotional states, like soothing angry customers. Moreover, sentiment scores can be attributed to individual agents, facilitating the identification of best practices and opportunities for agent coaching.

When integrated with other customer experience metrics like Net Promoter Scores, sentiment analysis outcomes provide a comprehensive view of customer sentiments and emotions, enhancing the centre’s ability to understand, respond to, and manage customer perceptions effectively.