Data-driven attribution works in which of the following ways?

Data-driven attribution (DDA) uses machine learning to analyze all conversion paths in your account. It assigns fractional credit to each touchpoint (ad clicks and impressions) based on its actual contribution to a conversion. This method leverages your account's historical data to identify which interactions are most impactful across various Google Ads channels like Search, YouTube, and Display. It moves beyond static rules, providing a more accurate understanding of your marketing efforts. The incorrect options describe other attribution models or misrepresent DDA: Prioritizing specific touchpoints and applying static logic describes rule-based models like Last Click or Linear. Using country- or region-specific data to credit consistent ad touchpoints across the Search network is too narrow and doesn't capture DDA's core mechanism. Crediting on an arbitrary basis is incorrect; DDA is data-driven and aims for accuracy, not arbitrariness.