Multi-touch attribution (MTA) is a marketing measurement model. With multi-touch attribution, marketing teams can assign fractional credit to multiple touchpoints along the customer journey. 

Fractional credits represent a percentage assigned to each channel within the MTA model based on conversion data.

Why Multi-Touch Attribution Matters

People engage with a brand across many channels before they convert, so multi-touch attribution models enable marketers to measure which marketing processes, messaging, and channels have the greatest influence on final purchase decisions.

MTA helps marketers allocate budgets based on true ROI for specific channels and touchpoints and optimize the customer journey for improved performance.

Common Ways to Measure MTA

• Linear: A baseline MTA model providing a big-picture view of the customer journey, with equal value placed on every interaction.

• Time Decay: A model often used for campaigns that benefit from data about interactions occurring the closest to the time of conversion.

• U-Shaped: Also called position-based, this attribution model assigns 40% credit to the first and last touch interactions, with the remaining 20% to mid-funnel data.

• W-Shaped: A rule-based model commonly used in B2B marketing to focus equally on first touch, lead creation, and opportunity creation.

• Data-Driven: A model using some combination of AI, machine learning (ML), and statistical models that assign value based on the impact a touchpoint has on the outcome.