Which attribution model applies machine learning algorithms for credit distribution across touchpoints?

The data-driven attribution model uses machine learning algorithms to analyze the interactions users have with various touchpoints during their conversion journey. This model evaluates the unique paths different users take before completing a conversion to understand how much credit each touchpoint should receive. Unlike traditional models such as last-click or first-click, which apply fixed rules for credit distribution, the data-driven approach considers the effectiveness and contribution of each touchpoint based on the actual data from user interactions.

By leveraging machine learning, the data-driven model can adapt and refine its attribution based on changing user behavior and patterns over time, ensuring that credit assignments reflect the true role each channel plays in conversions. As a result, marketers can gain more accurate insights into their marketing performance and make informed decisions about resource allocation and strategy optimization.