Which attribution model spreads credit for a conversion across different touchpoints through the use of machine learning algorithms?

The data-driven attribution model uses machine learning to analyze all conversion paths and determine how different touchpoints influence conversion outcomes. It assigns fractional credit to each touchpoint based on its actual contribution, providing a more accurate and nuanced understanding of marketing effectiveness. Last click attribution gives 100% of the credit to the final touchpoint before conversion, ignoring all prior interactions. First click attribution assigns all credit to the initial touchpoint, overlooking subsequent influences. Time decay attribution gives more credit to touchpoints that occurred closer in time to the conversion, but it doesn't use machine learning to understand actual impact. These models are rule-based and don't adapt to your specific data like the data-driven model.