Marketing Attribution Modeling

Marketing Attribution Modeling

Implementing marketing attribution models — from single-touch and multi-touch through algorithmic attribution, incrementality testing, and unified measurement.

When to Use

• Understanding which marketing channels drive conversions
• Allocating marketing budget based on actual impact
• Moving beyond last-click attribution (which overvalues bottom-of-funnel)
• Measuring incremental impact of marketing activities
• Building a unified marketing measurement framework

Attribution Models

ATTRIBUTION_MODELS = {
    'first_touch': '100% credit to first interaction (overvalues awareness)',
    'last_touch': '100% credit to last interaction before conversion (overvalues bottom)',
    'linear': 'Equal credit to all touchpoints in the journey',
    'time_decay': 'More credit to touchpoints closer to conversion',
    'position_based': '40% first touch, 40% last touch, 20% middle (U-shaped)',
    'algorithmic': 'ML-driven attribution based on actual channel influence',
    'incremental': 'Measures lift vs control group (true causal impact)',
}

class AttributionModel:
    """Calculate channel attribution."""
    def __init__(self, model_type: str = 'multi_touch'):
        self.model_type = model_type
    
    def attribute(self, journeys: List[Dict], conversions: List[int]) -> Dict:
        channel_credit = {}
        for journey, converted in zip(journeys, conversions):
            if not converted: continue
            channels = journey.get('touchpoints', [])
            if not channels: continue
            
            if self.model_type == 'first_touch':
                channel_credit[channels[0]] = channel_credit.get(channels[0], 0) + 1
            elif self.model_type == 'last_touch':
                channel_credit[channels[-1]] = channel_credit.get(channels[-1], 0) + 1
            elif self.model_type == 'linear':
                weight = 1 / len(channels)
                for ch in channels:
                    channel_credit[ch] = channel_credit.get(ch, 0) + weight
        
        return channel_credit

Common Pitfalls

1. Last-click dominance — underinvesting in awareness channels that drive top-of-funnel
2. Cross-device blind spots — attributing to wrong channel when user switches devices
3. Offline-online gap — online attribution misses offline purchases influenced by online
4. View-through vs click-through — view-through attribution is controversial; use with caution
5. Channel cannibalization — paid search capturing brand searches that would convert organically

Verification Checklist

• Attribution model selected (single, multi-touch, or algorithmic)
• Cross-device tracking enabled (or probabilistic)
• Offline conversion data integrated (if applicable)
• Model regularly validated against holdout/incrementality tests
• Channel overlap and cannibalization analyzed
• Budget allocation adjusted based on attribution insights
• Causal incrementality testing (geo holdout, time-series) in roadmap