Many advertisers rely on attribution to make a variety of tactical and strategic marketing decisions, and there is no shortage of attribution models for advertisers to consider. In the end, most advertisers choose an attribution model based on their preconceived notions about how attribution credit should be allocated. A misguided selection can lead an advertiser to use erroneous information in making marketing decisions. In this paper, we address this issue by identifying a well-defined objective for attribution modeling and proposing a systematic approach for evaluating and comparing attribution model performance using simulation. Following this process also leads to a better understanding of the conditions under which attribution models are able to provide useful and reliable information for advertisers.