Visible to the public Multi-touch Attribution in Online Advertising with Survival Theory

TitleMulti-touch Attribution in Online Advertising with Survival Theory
Publication TypeConference Paper
Year of Publication2014
AuthorsYa Zhang, Yi Wei, Jianbiao Ren
Conference NameData Mining (ICDM), 2014 IEEE International Conference on
Date PublishedDec
Keywordsadvertising, advertising data processing, commercial advertising monitoring company, data handling, Data models, data-driven multitouch attribution models, digital advertising, Gold, Hazards, Hidden Markov models, Internet, Kernel, Multi-touch attribution, online advertising, Predictive models, probabilistic framework, probability, rule-based attribution models, survival theory, user conversion probability prediction, user interaction data

Multi-touch attribution, which allows distributing the credit to all related advertisements based on their corresponding contributions, has recently become an important research topic in digital advertising. Traditionally, rule-based attribution models have been used in practice. The drawback of such rule-based models lies in the fact that the rules are not derived form the data but only based on simple intuition. With the ever enhanced capability to tracking advertisement and users' interaction with the advertisement, data-driven multi-touch attribution models, which attempt to infer the contribution from user interaction data, become an important research direction. We here propose a new data-driven attribution model based on survival theory. By adopting a probabilistic framework, one key advantage of the proposed model is that it is able to remove the presentation biases inherit to most of the other attribution models. In addition to model the attribution, the proposed model is also able to predict user's 'conversion' probability. We validate the proposed method with a real-world data set obtained from a operational commercial advertising monitoring company. Experiment results have shown that the proposed method is quite promising in both conversion prediction and attribution.

Citation Key7023386