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Supported Multi-Touch Attribution Models
Multi-Touch Attribution (MTA) offers several default models that analyze channel contribution. Select a model based on your specific business strategy.
| Model Name | Best For | How It Works | Example |
|---|---|---|---|
| First Touch | Analyzing top-of-funnel brand awareness drivers | Assigns 100% credit to the first interaction in the lookback window | Journey: Email → Google Ads → Conversion Credit: Email = 100%. |
| Last Touch | Optimizing bottom-of-funnel performance and immediate conversion drivers | Assigns 100% credit to the final interaction before conversion | Journey: Email → Google Ads → Conversion Credit: Google Ads = 100% |
| Linear Regression | Weighting every interaction in the customer lifecycle evenly | Distributes credit equally across every touchpoint in the journey | Journey: Email → Google Ads → Instagram → Conversion Credit: Each touchpoint ≈ 33% |
| Time Decay | Strategies that give more weight to recent interactions when attributing a sale | Assigns credit based on recency. Touchpoints closer to the conversion get more weight | Journey (Time-decay 7 days): Email (3 days before) → Google Ads (1 day before) → Conversion Credit: Google Ads receives more credit than the email, because it’s closer to the conversion |
| U-Shaped | Emphasizing both the initial discovery and the final close, while acknowledging mid-funnel nurturing | Allocates 40% to the first touch and 40% to the last touch. Splitting the remaining 20% equally among middle interactions | Journey: Email → Meta Ads → LinkedIn Ads → Google Ads → Conversion Credit: Email: 40%Meta Ads: 10%LinkedIn Ads: 10%Google Ads: 40% |
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