Sentiment Analysis
Sentiment Analysis is the technology that detects and classifies customer emotions—positive, negative, or neutral—from conversation text and tone. A customer saying "Your solution is amazing!" has positive sentiment; "I'm frustrated with the lack of support" has negative sentiment. Tracking sentiment over time reveals customer satisfaction trends.
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?
FAQs related to
Sentiment Analysis
How accurate is sentiment analysis?
Modern ML sentiment models are 85-95% accurate on clearly positive/negative text. Sarcasm and context-dependent language can trip them up. Always combine automated sentiment with human review for edge cases.
Can I improve sentiment with better support?
Absolutely. Sentiment often reflects support quality. Faster response times, empathetic language, and quick issue resolution all improve sentiment. Track sentiment improvement as your support team improves.
Should I respond to negative sentiment immediately?
Yes. Negative sentiment during a conversation is fixable. The agent can address concerns, clarify misunderstandings, and recover the relationship in real-time. Post-conversation negativity is harder to recover.
What causes negative sentiment?
Usually: unresolved problems, slow support response, feeling unheard, or misaligned expectations. Identify patterns and address root causes.
Can I use sentiment to predict churn?
Yes. Customers with declining sentiment are at high churn risk. Combine sentiment with other signals for better prediction.