Multi-Touch Attribution (MTA) is a marketing analytics method used to assign proportional credit to each touchpoint or interaction a consumer has with a brand before completing a desired action—such as making a purchase or signing up for a service. Unlike single-touch attribution models that credit just the first or last interaction, multi-touch attribution recognizes that the customer journey is often nonlinear and influenced by multiple channels, campaigns, and messages.

This approach aims to answer a key question in marketing: Which touchpoints actually contribute to conversions, and how much influence does each one have? For example, a customer might first encounter a product through a social media ad, click on a Google search result later, receive a promotional email, and finally make a purchase after clicking on a retargeting ad. Multi-touch attribution allows marketers to evaluate the effectiveness of each of these interactions rather than attributing the entire success to just one.

There are various multi-touch attribution models, each distributing credit differently:

- Linear attribution gives equal weight to all touchpoints.

- Time decay gives more credit to touchpoints closer to the conversion.

- U-shaped (or position-based) emphasizes the first and last interactions.

- Custom or algorithmic models use data-driven techniques to assign credit based on real behavior patterns.

Multi-touch attribution offers a more nuanced and accurate view of marketing performance, which can help businesses optimize budget allocation, improve campaign strategies, and ultimately drive better ROI. However, implementing it requires robust data collection, cross-channel integration, and often advanced analytics tools or machine learning models to process and interpret the data effectively.

Despite its benefits, MTA can be challenging due to issues like data silos, privacy regulations (e.g., GDPR, CCPA), cookie deprecation, and user tracking limitations. As a result, marketers often blend MTA with other methods like marketing mix modeling (MMM) for a more holistic understanding of channel impact.