Which attribution model is recommended for use when implementing value-based bidding?

The data-driven attribution model is recommended for implementing value-based bidding because it assesses the contribution of each ad interaction in driving conversions using machine learning. This model assigns credit for conversions based on how each ad touchpoint affects the likelihood of the conversion occurring.

In a value-based bidding strategy, the focus is on achieving the best outcomes, such as maximizing conversion value or return on ad spend (ROAS). By utilizing the data-driven attribution model, advertisers can gain insights into which ads and channels are truly driving performance. It dynamically adjusts the attribution based on actual user behavior, allowing for more accurate assessments of ad effectiveness and enabling advertisers to allocate budget more efficiently to the most impactful strategies.

This approach stands in contrast to the other models, which may not provide as nuanced a view of performance. For instance, the last-click model attributes all conversion value to the final interaction before the conversion, potentially overlooking the influence of earlier interactions. The time decay model gives more credit to interactions that occur closer in time to the conversion but still lacks the sophistications of data-driven assessments. The first-click model attributes all credit to the first interaction, again neglecting the totality of influences throughout the customer journey. Thus, the data-driven approach is most aligned with the goal of