Artificial Intelligence (AI) Sentiment Analysis

AI sentiment analysis helps contact centers gain insights into customer feelings, opinions and attitudes, which can be valuable for improving customer service and overall customer experiences.

How AI Sentiment Analysis Works

• Text and Speech Analysis: AI sentiment analysis can analyze text-based interactions (like chat and email) and speech-based interactions (phone calls). It processes the content of these interactions to identify and classify sentiment.
• Sentiment Classification: AI algorithms classify sentiments into categories such as positive, negative, and neutral or specific emotions like happiness, frustration, anger, and satisfaction. Some systems provide a numerical sentiment score to quantify the sentiment intensity.
• Automated Feedback: By identifying positive or negative sentiments, contact centers can automatically route interactions to the appropriate teams or agents for follow-up. For instance, highly dissatisfied customers may be flagged for immediate attention.
• Performance Metrics: Sentiment analysis data can be used to track and improve customer service agent performance. Agents and supervisors can receive feedback based on the sentiment expressed in customer interactions.
• Root Cause Analysis: Sentiment analysis helps contact centers pinpoint the underlying reasons for customer dissatisfaction or satisfaction. This can inform process improvements and product enhancements.
• Real-Time Feedback: In some cases, sentiment analysis can provide real-time feedback during customer interactions. For example, a chatbot might detect customer frustration and escalate the conversation to a live agent.
• Trend Identification: By analyzing sentiments over time, contact centers can identify trends and patterns in customer feedback. This information can guide strategic decisions and improvements.
• Proactive Customer Engagement: Sentiment analysis can trigger proactive customer engagement based on detected sentiments. For example, if a customer expresses frustration in a chat, a supervisor or manager may be alerted to intervene and resolve the issue.