Process Flow
Animated overview of the full workflow
TL;DR
Multi-touch attribution (MTA) distributes conversion credit across all marketing touchpoints in a customer's journey, rather than giving all credit to the first or last interaction. Models include linear (equal credit), time decay (more credit to recent touches), position-based (40/20/40), and data-driven (algorithmic). MTA helps you understand the true value of each channel and make better budget allocation decisions.
Most customers interact with multiple ads across multiple channels before converting. Last-click attribution credits only the final touchpoint, undervaluing awareness and consideration channels. MTA provides a more complete picture.
Step-by-Step Guide
Follow these 3 steps to complete this guide
- 1
Models
**Linear:** Equal credit to all touchpoints. **Time decay:** More credit to recent interactions. **Position-based (U-shaped):** 40% to first touch, 40% to last touch, 20% across middle. **Data-driven:** Machine learning assigns credit based on actual impact.
- 2
Platform Tools
Google Ads: Data-driven attribution (default for qualifying accounts). Meta: Attribution settings in ad set. GA4: Model comparison in the Attribution reports section.
- 3
Practical Approach
Perfect attribution is impossible. Focus on directional insights rather than exact numbers. Compare models to understand which channels are undervalued by last-click. Use incrementality testing to validate attribution findings.
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