Marketing Attribution Analyzer

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Overview

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What the Agent Needs to Accomplish

• Track customer journey across touchpoints
• Attribute conversions to marketing channels
• Measure channel effectiveness
• Analyze multi-touch attribution
• Compare attribution models
• Optimize channel mix

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How Pylar Helps

• Unified Journey View: Combining touchpoints, channels, and conversions
• Multi-Touch Analysis: Tracking all customer interactions
• Attribution Models: Multiple attribution model calculations
• Channel Comparison: Side-by-side channel effectiveness

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Without Pylar vs With Pylar

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Without Pylar

• ❌ Complex journey tracking across systems
• ❌ Manual attribution calculations
• ❌ Limited multi-touch visibility
• ❌ Time-consuming analysis

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With Pylar

• ✅ Automated journey tracking
• ✅ Multiple attribution models
• ✅ Real-time channel analysis
• ✅ Easy model comparison

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Step-by-Step Implementation

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Step 1: Connect Data Sources

1. Connect Marketing Channels (Ads, email, social)
2. Connect Analytics (Website touchpoints)
3. Connect CRM (Conversions, customer data)

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Step 2: Create Attribution Views

CREATE VIEW customer_journey AS
SELECT 
  c.customer_id,
  c.conversion_date,
  t.touchpoint_id,
  t.channel,
  t.touchpoint_date,
  t.touchpoint_type,
  ROW_NUMBER() OVER (PARTITION BY c.customer_id ORDER BY t.touchpoint_date) as touchpoint_order
FROM conversions c
JOIN touchpoints t ON c.customer_id = t.customer_id
WHERE t.touchpoint_date <= c.conversion_date;

CREATE VIEW attribution_analysis AS
SELECT 
  channel,
  COUNT(DISTINCT customer_id) as conversions,
  -- First touch
  SUM(CASE WHEN touchpoint_order = 1 THEN 1 ELSE 0 END) as first_touch,
  -- Last touch
  SUM(CASE WHEN touchpoint_order = max_touchpoint THEN 1 ELSE 0 END) as last_touch,
  -- Linear
  COUNT(DISTINCT customer_id) / max_touchpoint as linear_attribution
FROM customer_journey
GROUP BY channel;

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Step 3: Create MCP Tools

• track_customer_journey(customer_id: string)

• attribute_conversion(conversion_id: string, model: string)

• analyze_channel_effectiveness(model: string, period: string)

• compare_attribution_models(conversion_id: string)

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Example Agent Interactions

• Google Ads: 35% of conversions ($420K revenue)
• Email Marketing: 28% ($336K revenue)
• LinkedIn Ads: 22% ($264K revenue)
• Organic Search: 15% ($180K revenue)

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Outcomes

• Attribution Accuracy: 40% improvement
• Channel Optimization: 25% better budget allocation
• Journey Visibility: 100% touchpoint tracking
• ROI Improvement: 30% increase in marketing ROI

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Next Steps

• Marketing Campaign Optimizer Example
• Content Performance Analyzer Example