What is Multi-Touch Attribution (MTA)?

Multi-Touch Attribution (MTA) is a measurement model that distributes credit for a conversion across multiple touchpoints in the user journey — not just the last one.

For example, if a user sees a TikTok video, then clicks a Google ad, and finally installs the app after seeing a Meta ad, MTA would assign a percentage of credit to each of those interactions based on their influence.

It aims to reflect how real users make decisions — influenced by several ads over time.

How does it work?

MTA requires stitching together all user interactions (impressions, clicks, visits) over time using identifiers like:

• Device ID
  
  

• IDFA / GAID (if available)
  
  

• Email or login
  
  

• First-party cookies
  
  

Common attribution models used in MTA include:

• Linear: equal credit to all touchpoints
  
  

• Time decay: more credit to recent interactions
  
  

• U-shaped: heavier weight to first and last
  
  

• Custom weighting based on business logic

To run effective MTA, you need rich, user-level data, which has become harder to access post-ATT and with growing privacy constraints. Some solutions rely on data clean rooms or modeled behavior to fill in the gaps.

Why it matters

MTA helps you understand the full user journey, not just the final click. It’s especially useful when:

• Users interact with your brand across multiple channels
  
  

• Campaigns are designed for awareness + performance
  
  

• You want to optimize budgets holistically, not just by last-click winners
  
  

While MTA is harder to implement under new privacy standards, it still offers critical insights for advanced teams — especially when paired with incrementality and cohort-based analysis.