The accuracy of an attribution model is limited by the assumptions of the model, and the quality and completeness of the data available to the model. Common digital attribution models on the market make a critical, yet hidden, assumption that ads only affect users by directly changing their propensity to convert. These models assume that ad exposure does not change user behavior in other ways, such as driving additional website visits, generating branded searches, or creating awareness and

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... est in the advertiser. In a previous paper [1], we described a Digital Advertising System Simulation (DASS) for modeling advertising and its impact on user behavior. In this paper, we use this simulation to demonstrate that current models fail to accurately capture the true number of incremen-tal conversions generated by ads that impact user behavior, and introduce an Upstream Data-Driven Attribution (UDDA) model to address this shortcoming. We also demonstrate that development beyond UDDA is still required to address a lack of data completeness, and situations that include highly targeting advertising.