AI-Powered Sentiment Analysis – Client Experience Intelligence
Implemented enterprise-grade sentiment tracking from unstructured communication to help reduce churn and identify at-risk clients.
An enterprise services provider wanted to proactively detect customer dissatisfaction embedded in meeting notes, emails, and manager reports.
Business Challenges
- Manual review of transcripts and notes lacked accuracy
- No standardized sentiment classification across teams
- High effort to detect dissatisfaction early
Plenum’s Solution
- Used GPT-4 and Qwen 2.5 to classify sentiment in communication logs
- Built dashboards to visualize weekly and monthly trends
- Incorporated human feedback loop to fine-tune sentiment scoring
Key Capabilities Used
- Secure NLP Pipeline
- LLM-Based Sentiment Classifier
- Human-in-the-Loop Validation
- Dashboards & Alerts
Outcomes & Impact
Streamlined UAT feedback tracking and risk flagging
3-4
Reduced client churn detection lag by weeks
88%+
Achieved sentiment accuracy of across languages
Technologies
Implemented
GPT-4
Qwen 2.5
Inset BI
Momentum Connect
Secure APIs