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Overview

A Product Feedback Analyzer powered by Pylar analyzes user feedback, feature requests, sentiment, and prioritizes product improvements based on data-driven insights.

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What the Agent Needs to Accomplish

The agent must:
  • Analyze user feedback and reviews
  • Track feature requests
  • Perform sentiment analysis
  • Prioritize improvements
  • Identify common pain points
  • Track feedback trends

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How Pylar Helps

Pylar enables the agent by:
  • Unified Feedback View: Combining feedback from multiple sources
  • Sentiment Analysis: Automated sentiment scoring
  • Priority Calculation: Data-driven prioritization
  • Trend Analysis: Identifying feedback patterns over time

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Without Pylar vs With Pylar

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Without Pylar

Challenges:
  • ❌ Feedback scattered across platforms
  • ❌ Manual sentiment analysis
  • ❌ Difficult to prioritize requests
  • ❌ Limited trend visibility
Implementation Complexity: ~4-5 weeks

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With Pylar

Benefits:
  • ✅ Single endpoint for all feedback
  • ✅ Automated sentiment analysis
  • ✅ Data-driven prioritization
  • ✅ Real-time trend tracking
Implementation Complexity: ~5-6 hours

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Step-by-Step Implementation

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Step 1: Connect Data Sources

  1. Connect Feedback Platform (UserVoice, Canny, Intercom)
  2. Connect Reviews (App Store, G2, Capterra)
  3. Connect Support (Support tickets, feature requests)

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Step 2: Create Feedback Views

Feedback Analysis View: 
CREATE VIEW feedback_analysis AS
SELECT 
  f.feedback_id,
  f.feedback_text,
  f.category,
  f.votes,
  f.sentiment_score,
  f.created_date,
  -- Priority score
  (f.votes * 0.4 + 
   ABS(f.sentiment_score) * 30 + 
   CASE WHEN f.category = 'Critical' THEN 30 ELSE 0 END) as priority_score
FROM feedback.feedback f;

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Step 3: Create MCP Tools

Tool 1: Analyze Feedback Sentiment
  • analyze_feedback_sentiment(category: string, days_back: number)
Tool 2: Get Top Feature Requests
  • get_top_feature_requests(limit: number, min_votes: number)
Tool 3: Identify Common Pain Points
  • identify_pain_points(category: string, min_mentions: number)
Tool 4: Prioritize Improvements
  • prioritize_improvements(criteria: string, limit: number)

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Example Agent Interactions

User: “What are users saying about our mobile app?” Agent: “Mobile App Feedback Analysis:
  • Overall Sentiment: 3.8/5 (Moderately Positive)
  • Top Issues: App crashes (45 mentions), Slow loading (32 mentions)
  • Top Requests: Dark mode (120 votes), Offline mode (95 votes)
  • Priority: Fix app crashes (High), Add dark mode (Medium)“

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Outcomes

  • Feedback Processing: 70% faster analysis
  • Prioritization: 40% improvement in feature prioritization
  • User Satisfaction: 25% increase in satisfaction scores
  • Product Roadmap: Data-driven roadmap decisions

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Next Steps