Attribution Model

An attribution model is the rule or algorithm used to assign credit for a conversion (lead, sale, or other goal) to the various marketing touchpoints that occurred in the customer journey before that conversion. Attribution models are fundamental to understanding which marketing channels and campaigns are driving revenue — and therefore how to allocate budget. Common attribution models: last-click (100% credit to the final touchpoint before conversion — most common, but systematically undervalues awareness channels), first-click (100% credit to the first touchpoint — overvalues acquisition channels, undervalues bottom-funnel), linear (equal credit to all touchpoints), time-decay (more credit to recent touchpoints), position-based / U-shaped (40% first, 40% last, 20% middle), and data-driven (machine learning model trained on actual conversion data — most accurate, requires sufficient data volume). Attribution model selection impacts budget allocation decisions: last-click attribution typically inflates the measured ROI of paid search and direct traffic while deflating the measured ROI of content marketing, organic social, and display advertising. For B2B with long, multi-touch sales cycles, data-driven or position-based attribution models generally provide the most actionable insights. GA4's data-driven attribution model is now the platform default for advertisers with sufficient conversion data.