Which attribution model uses machine learning algorithms to assign credit for conversions?

The data-driven attribution model leverages machine learning algorithms to assign conversion credit across touchpoints in a user’s journey. This model analyzes various different ways users interact with marketing initiatives and determines the value of each interaction based on how likely it is to contribute to a conversion.

By utilizing historical data, the machine learning algorithms can identify patterns and discern which interactions matter most in driving conversions. This allows for a more nuanced view of user behavior, as it can dynamically adjust and optimize the attribution based on the data collected over time. This means that it performs better in accurately reflecting the influence of multiple interactions that may have contributed to the eventual conversion.

In contrast, the other models—like last-click, first-click, and linear—use fixed rules for assigning credit that don't take into account the complexities and nuances of the customer journey. This can lead to less accurate representations of how different touchpoints contribute to conversions.