Coalition Game Theory In Attribution Modeling

ABSTRACT

Attribution modeling (AM) has a crucial role in measuring the impact of advertising inputs in driving actions (clicks, conversions, purchases, homepage visits, etc.). A misattributing attribution model, such as last touch, allows publishers to ride freely on others' efforts. This, in turn, powers futile optimizations with no realized performance lift. Shapley value and logistic regression stand out as reliable attribution models with a reputation across-industry verticals. AM using coalitional game theory—Shapley values—can distribute fairly both gains and costs to inputs, with unequal contributions, working together. AM using Shapley values, however, faces a scalability challenge for most practical applications. Notwithstanding, existing scalable AM methods not only lack interpretability but also blur the contrast between efficiency and contribution. This study demonstrates a scalable way to approximate Shapley values, mainly with successive orders of probabilistic models, which also provide additional insights into the efficiency and contribution of interacting advertising inputs.