2011 DataDrivenMultiTouchAttribution

• (Shao & Li, 2011) ⇒ Xuhui Shao, and Lexin Li. (2011). “Data-driven Multi-touch Attribution Models.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020453

Subject Headings: Attribution Modeling.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Data-driven+Multi-touch+Attribution+Models
• http://dl.acm.org/citation.cfm?id=2020408.2020453&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; bagged logistic regression; digital advertising; modeling methodologies; multi-touch attribution model; performance; theory

Abstract

In digital advertising, attribution is the problem of assigning credit to one or more advertisements for driving the user to the desirable actions such as making a purchase. Rather than giving all the credit to the last ad a user sees, multi-touch attribution allows more than one ads to get the credit based on their corresponding contributions. Multi-touch attribution is one of the most important problems in digital advertising, especially when multiple media channels, such as search, display, social, mobile and video are involved. Due to the lack of statistical framework and a viable modeling approach, true data-driven methodology does not exist today in the industry. While predictive modeling has been thoroughly researched in recent years in the digital advertising domain, the attribution problem focuses more on accurate and stable interpretation of the influence of each user interaction to the final user decision rather than just user classification. Traditional classification models fail to achieve those goals.

In this paper, we first propose a bivariate metric, one measures the variability of the estimate, and the other measures the accuracy of classifying the positive and negative users. We then develop a bagged logistic regression model, which we show achieves a comparable classification accuracy as a usual logistic regression, but a much more stable estimate of individual advertising channel contributions. We also propose an intuitive and simple probabilistic model to directly quantify the attribution of different advertising channels. We then apply both the bagged logistic model and the probabilistic model to a real-world data set from a multi-channel advertising campaign for a well-known consumer software and services brand. The two models produce consistent general conclusions and thus offer useful cross-validation. The results of our attribution models also shed several important insights that have been validated by the advertising team.

We have implemented the probabilistic model in the production advertising platform of the first author's company, and plan to implement the bagged logistic regression in the next product release. We believe availability of such data-driven multi-touch attribution metric and models is a break-through in the digital advertising industry.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 DataDrivenMultiTouchAttribution	Xuhui Shao Lexin Li			Data-driven Multi-touch Attribution Models				10.1145/2020408.2020453		2011