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Overview

A Product Usage Analyst powered by Pylar analyzes product usage patterns, feature adoption rates, user engagement metrics, and identifies trends to inform product decisions.

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What the Agent Needs to Accomplish

The agent must:
  • Analyze product usage patterns
  • Track feature adoption rates
  • Measure user engagement
  • Identify usage trends
  • Compare user segments
  • Recommend feature improvements

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How Pylar Helps

Pylar enables the agent by:
  • Unified Usage View: Combining product analytics, user data, and feature usage
  • Real-time Analysis: Querying current usage patterns
  • Trend Identification: Detecting usage trends over time
  • Segment Comparison: Comparing usage across user segments
  • Data-Driven Insights: Generating actionable product recommendations

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Without Pylar vs With Pylar

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Without Pylar

Challenges:
  • ❌ Multiple analytics tools and data sources
  • ❌ Manual usage analysis
  • ❌ Difficult to correlate features with engagement
  • ❌ Time-consuming trend identification
Implementation Complexity: ~4-5 weeks

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With Pylar

Benefits:
  • ✅ Single endpoint for all usage data
  • ✅ Automated usage analysis
  • ✅ Real-time trend detection
  • ✅ Easy feature comparison
Implementation Complexity: ~5-6 hours

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Step-by-Step Implementation

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Step 1: Connect Data Sources

  1. Connect Product Analytics (User actions, events, feature usage)
  2. Connect User Data (User segments, subscription tiers)
  3. Connect Product Catalog (Features, releases)

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Step 2: Create Usage Views

Feature Adoption View: 
CREATE VIEW feature_adoption AS
SELECT 
  f.feature_name,
  f.release_date,
  COUNT(DISTINCT u.user_id) as total_users,
  COUNT(DISTINCT CASE WHEN u.feature_used THEN u.user_id END) as adopted_users,
  (COUNT(DISTINCT CASE WHEN u.feature_used THEN u.user_id END) / 
   NULLIF(COUNT(DISTINCT u.user_id), 0)) * 100 as adoption_rate,
  AVG(u.usage_count) as avg_usage_per_user,
  AVG(u.last_used_days_ago) as avg_days_since_last_use
FROM product.features f
LEFT JOIN analytics.feature_usage u ON f.feature_id = u.feature_id
GROUP BY f.feature_name, f.release_date;
User Engagement View: 
CREATE VIEW user_engagement AS
SELECT 
  u.user_id,
  u.user_segment,
  u.subscription_tier,
  u.active_days_last_30,
  u.features_used_count,
  u.total_actions,
  u.last_activity_date,
  -- Engagement score
  (u.active_days_last_30 * 0.4 + 
   (u.features_used_count / 10) * 30 + 
   (u.total_actions / 100) * 30) as engagement_score
FROM analytics.user_usage u;

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Step 3: Create MCP Tools

Tool 1: Analyze Feature Adoption
  • analyze_feature_adoption(feature_name: string, days_back: number)
Tool 2: Get Usage Trends
  • get_usage_trends(metric: string, period: string)
Tool 3: Compare User Segments
  • compare_user_segments(segment1: string, segment2: string, metric: string)
Tool 4: Identify Usage Patterns
  • identify_usage_patterns(feature_id: string, user_segment: string)

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Example Agent Interactions

User: “How is the new dashboard feature being adopted?” Agent: “Dashboard Feature Adoption:
  • Adoption Rate: 42% (8,400 of 20,000 users)
  • Average Usage: 12 times per user
  • Top User Segment: Enterprise (65% adoption)
  • Growth Trend: +15% week-over-week
  • Recommendation: Promote to mid-market segment”

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Outcomes

  • Feature Adoption: 30% improvement in adoption rates
  • User Engagement: 25% increase in engagement
  • Product Decisions: Data-driven feature prioritization
  • Trend Detection: 60% faster identification of usage trends

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Next Steps