AI Performance Evaluation: Call Center Sentiment Analysis
Customer sentiment is a simple measure of how your customers feel about your brand. It’s sometimes referred to as “user sentiment” and is all about understanding the emotions and intent behind every business-customer interaction.
To analyze customer sentiment, you must gather feedback data from a range of touchpoints, such as survey responses, emails, live chats, and call transcripts.
You can then sift through all this data by hand to identify patterns and rank emotional tone, but this is incredibly time-consuming. Alternatively, use a customer sentiment analysis tool that uses AI to automate these processes. This software will provide live call insights and generate accurate reports to help you track and improve customer sentiment metrics over time.
In a call center, these metrics might include:
- Average response time
- Average handling time
- Call abandonment rate
- Customer reviews
- Customer satisfaction score
- Total resolution time
Customer sentiment analysis will determine whether an interaction is considered negative, positive, or neutral. However, modern software takes this one step further by classifying conversations based on three key categories:
Fine-grained analysis ranks interactions on a scale of very negative to very positive. It’s often associated with aspect-based analysis, which focuses on specific topics.
Emotion detection analyzes language to detect the emotions of the agent and caller throughout their dialogue.
Intent analysis identifies the motivations behind customer feedback to help sales teams take positive action.
