Guides selection and honest application of a marketing attribution model, surfacing common measurement traps. Use when evaluating channel ROI, auditing tracking, or choosing between attribution models.
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name: Marketing Attribution
description: Guides selection and honest application of a marketing attribution model, surfacing common measurement traps. Use when evaluating channel ROI, auditing tracking, or choosing between attribution models.
---
# Marketing Attribution
Attribution does not reveal truth - it reveals a model's assumptions about truth. Choosing a model is choosing a set of tradeoffs. The goal is to pick the model that creates the least-distorted decisions for the business, not the one that flatters the most channels.
## Understand What Each Model Assumes
First-touch attribution credits the first interaction entirely - useful for understanding discovery channels, but it ignores everything that closed the sale. Last-touch does the opposite and over-credits closers like brand search. Linear attribution spreads credit evenly - neutral but often meaningless. Time-decay weights recent touches more heavily - reasonable for short sales cycles. Data-driven attribution uses conversion path modeling - best in theory but requires high conversion volume (typically 1,000+ monthly conversions) and complete tracking. Position-based (U-shaped) splits 40/20/40 between first touch, middle, and last touch - a practical default for teams that lack volume for data-driven models.
## Choose Based on Sales Cycle and Data Volume
For sales cycles under 7 days with fewer than 500 monthly conversions: use last-touch with branded/direct excluded. For cycles 7-30 days: use position-based. For cycles over 30 days or complex B2B: use time-decay or a CRM-based opportunity model. For teams with 1,000+ conversions and clean tracking: test data-driven and compare decisions it produces versus current model. Never use first-touch as a primary model for budget allocation - it systematically over-invests in awareness and starves conversion channels.
## Avoid the Three Most Common Traps
Trap 1 - View-through inflation: Meta and display platforms count view-through conversions by default, crediting an impression even when the user converted organically. Set view-through window to 1 day maximum or disable it, and compare platform-reported conversions to actual revenue. Trap 2 - Cross-device gaps: a user who sees an ad on mobile and converts on desktop appears as two separate paths. Use a unified identity layer or accept that mobile spend will be systematically undercredited. Trap 3 - Sampling and consent loss: iOS privacy changes and cookie deprecation mean 20-40% of conversions may be untracked. Use aggregated event measurement, first-party data enrichment, and model-based estimation - do not treat reported numbers as complete.
## Triangulate, Do Not Depend on One Signal
No single attribution model is sufficient. Use platform-reported data alongside three additional signals: (1) geo holdout tests - pause a channel in a test market and measure revenue impact; (2) incrementality tests - meta-analysis tools or platform lift studies; (3) MTA modeled data from a third-party tool. When all three signals agree, act with confidence. When they conflict, investigate before making budget decisions.