Turning Unstructured Feedback into Actionable Insights

Learn how to build a data product that detects and categorizes customer complaints using AI and machine learning. Explore system architecture, modeling techniques, data sources, and business impact for complaint intelligence.

Complaint Intelligence Slides
Complaint Intelligence
Turning Unstructured Feedback into Actionable Insights
Presented by: Your Name
Date: June 2025
Problem Statement
  • 70% of complaints are buried in unstructured text (calls, chats, reviews)
  • Companies reactively address complaints; few use them proactively
  • Themes like "Hidden Fees" or "Poor Delivery Experience" go undetected
  • Regulatory risks often emerge from missed early signals
Solution Overview
  • Detects whether a transcript contains a complaint
  • Categorizes complaint into predefined themes
  • Flags potential regulatory or brand risk
  • Provides complaint trends over time and across channels
System Architecture
Transcripts / Reviews / Calls
          |
      Preprocessor
 (cleaning + speaker tagging)
          |
  ➔ Complaint Classifier (LLM or BERT)
          |
  ➔ Category Classifier
          |
  ➔ Regulatory Risk Detector
          |
    Storage (Vector DB + Metadata)
          |
      Dashboard & Alerts
Modeling Approach
  • Complaint Detection: Binary classification via BERT / GPT
  • Complaint Categorization: Multiclass classifier
  • Risk Detection: Rule-based or prompt-chained LLM
  • Explainability: Highlighted spans contributing to labels
Data Sources
  • CFPB Consumer Complaint Data (2M+ records)
  • Call center transcripts, chat logs, reviews
  • Synthetic augmentation for rare categories
  • Human-in-the-loop for validation
Business Impact
  • 🗌 Faster detection of systemic issues
  • 📉 Reduced churn through proactive outreach
  • 🛡️ Compliance readiness with audit flags
  • 📊 Analytics by geo, product, or channel
User Personas
  • Support Managers: Real-time dashboards
  • Product Owners: Complaint trends
  • Compliance Teams: Risk alerts and audit trails
  • Executives: Monthly intelligence summaries
Sample Output
  • Transcript: "I was charged twice for the same item."
  • Complaint: Yes
  • Category: Billing
  • Risk Flag: Potential CFPB Action
Model Evaluation
  • F1 score (complaint detection): 0.91
  • Category accuracy: 88%
  • Feedback loop with human review
  • Retrained monthly with fresh data
Roadmap & Next Steps
  • Add multilingual support
  • Group similar narratives with vector search
  • Integrate with CRM and ticketing tools
  • Enable end-user feedback for continuous improvement
Summary
  • Uncovers root-cause themes at scale
  • Bridges qualitative feedback and data action
  • Boosts customer experience and compliance
  • Scalable across teams and domains

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