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Multi-Touch Attribution

A model that credits multiple marketing touchpoints for a conversion.

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Definition

Multi-touch attribution (MTA) distributes conversion credit across all marketing touchpoints a customer interacted with before converting — not just the first or last click. It provides a more complete picture of how your marketing channels work together.

Common MTA Models

  • Linear: Equal credit to every touchpoint
  • Time Decay: More credit to touchpoints closer to conversion
  • U-Shaped: 40% first touch, 40% last touch, 20% middle
  • W-Shaped: 30% first, 30% lead creation, 30% last, 10% middle
  • Data-Driven: Algorithm determines credit (requires large data volume)

Why It Matters

Single-touch models like last-click over-credit bottom-funnel channels and under-credit awareness campaigns. MTA helps you allocate budget more accurately across the full funnel.

Examples

  • A customer sees a YouTube ad, clicks a Google Search ad, then converts via a retargeting ad — MTA credits all three
  • Switching from last-click to data-driven attribution reveals Facebook prospecting drives 30% more assisted conversions

Common Mistakes

  • Expecting perfect attribution — no model captures 100% of the customer journey
  • Using MTA without enough conversion volume to make the data statistically significant