What Is Sentiment Analysis?
Sentiment analysis uses natural language processing to extract emotion from customer language. It goes beyond words to understand context: "I can't live without this product" is positive; "I can't live without fixing this bug" is negative. Plura's conversation analysis automatically detects sentiment from voice calls, SMS, and chat, highlighting when customers are satisfied, frustrated, or considering leaving.
Sentiment vs. Satisfaction Score
Different but complementary:
Sentiment Analysis: Real-time emotional analysis during conversations (automated, immediate)
Satisfaction Score: Post-conversation survey asking "Rate your satisfaction 1-10" (manual, delayed)
Best practice: Use both. Sentiment shows real-time emotion; satisfaction score confirms perception and identifies surprises
Why Sentiment Matters for Retention
Negative sentiment is a leading churn indicator. A customer with negative sentiment in support conversations is likely to leave. Early detection enables intervention: when sentiment drops, reach out proactively, improve support, or clarify value. This prevents churn before it happens.
How Plura Enables Sentiment Analysis
Plura tracks sentiment across every interaction:
Real-Time Detection: Identify negative sentiment during conversations while there's time to respond
Coaching Alerts: Flag when agents are handling frustrated customers so supervisors can coach in real-time
Customer Health Tracking: Watch sentiment trends—declining sentiment signals at-risk customers
Conversation Insights: Identify what topics trigger negative sentiment (product issues, pricing, support delays)
Sentiment Patterns to Monitor
Track these:
Sentiment Trend: Is customer becoming more satisfied or frustrated over time?
Sentiment by Topic: Does sentiment drop when discussing specific features or departments?
Sentiment Recovery: When negative sentiment appears, does good support recover it?
Sentiment by Agent: Do certain agents maintain higher customer sentiment than others?