Call Feedback System

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How It Works

• Performance Score (0-10): Overall call quality rating
• Written Feedback: Detailed analysis of call performance
• Improvement Suggestions: Specific recommendations for better results

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What Gets Analyzed

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Communication Quality

• Professionalism and tone
• Clear articulation and pacing
• Active listening skills
• Appropriate language use

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Effectiveness

• Goal achievement (sales conversion, issue resolution)
• Problem-solving approach
• Information gathering
• Call structure and flow

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Customer Experience

• Empathy and rapport building
• Responsiveness to customer needs
• Handling of objections or concerns
• Overall customer satisfaction indicators

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API Endpoints

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Get Call Feedback

const response = await fetch(`/api/call-feedback/${callId}`, {
  headers: {
    'x-user-token': 'your-token-here'
  }
});

const { data } = await response.json();

{
  "data": {
    "id": "feedback_123",
    "call_id": "call_456",
    "score": 8,
    "feedback": "The agent demonstrated excellent professionalism and successfully addressed the customer's concerns. The call flow was well-structured with clear explanations.",
    "improvement_suggestions": "Consider asking more qualifying questions early in the call to better understand customer needs and tailor the presentation accordingly.",
    "created_at": "2024-01-15T10:30:00Z"
  },
  "success": true
}

​

List Call Feedback

const response = await fetch('/api/call-feedback?limit=20&minScore=7', {
  headers: {
    'x-user-token': 'your-token-here'
  }
});

const { data } = await response.json();

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Query Parameters

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Get Feedback Statistics

const response = await fetch('/api/call-feedback/stats?days=30', {
  headers: {
    'x-user-token': 'your-token-here'
  }
});

const { data } = await response.json();

{
  "data": {
    "averageScore": 7.8,
    "totalFeedback": 150,
    "scoreDistribution": [
      {"score": 0, "count": 0},
      {"score": 1, "count": 1},
      {"score": 2, "count": 2},
      ...
      {"score": 10, "count": 15}
    ]
  },
  "success": true
}

​

Database Schema

CREATE TABLE call_feedback (
  id UUID PRIMARY KEY,
  call_id UUID REFERENCES calls(id),
  agent_id UUID REFERENCES agents(id),
  organization_id UUID REFERENCES organizations(id),

  -- Feedback data
  score INTEGER CHECK (score >= 0 AND score <= 10),
  feedback TEXT,
  improvement_suggestions TEXT,

  -- Timestamps
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
);

-- Performance indexes
CREATE INDEX idx_call_feedback_call_id ON call_feedback(call_id);
CREATE INDEX idx_call_feedback_organization_id ON call_feedback(organization_id);
CREATE INDEX idx_call_feedback_score ON call_feedback(score);

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Feedback Generation

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Error Handling

• No Impact on Calls: Feedback generation never affects call completion or webhook delivery
• Silent Failures: Failed feedback generation is logged but doesn’t create incomplete records
• Graceful Degradation: If feedback fails, the call record remains intact and accessible
• Error Logging: All errors are captured in Sentry for monitoring and debugging

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Best Practices

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Using Feedback Data

1. Regular Review: Check feedback regularly to identify patterns and trends
2. Agent Training: Use improvement suggestions for targeted coaching
3. Performance Tracking: Monitor average scores over time to measure improvement
4. Quality Assurance: Use low-scoring calls for additional review

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API Usage

1. Pagination: Use limit and offset for large datasets
2. Filtering: Apply score filters to focus on specific performance ranges
3. Caching: Cache feedback data appropriately as it doesn’t change once created

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Troubleshooting

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No Feedback Generated

1. Transcript Availability: Ensure the call has a valid transcript
2. Call Duration: Very short calls (< 30 seconds) may not generate feedback
3. System Status: Check if there are any ongoing system issues
4. Background Processing: Feedback generation can take 10-30 seconds after call completion

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Low Quality Feedback

1. Transcript Quality: Poor audio quality can affect transcript accuracy
2. Call Context: Ensure calls have sufficient context for AI analysis
3. Feedback Patterns: Look for patterns across multiple calls rather than focusing on individual instances