To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles

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dc.contributor.authorRößler, Jannik
dc.contributor.authorTilly, Roman
dc.contributor.authorSchoder, Detlef
dc.date.accessioned2020-12-24T19:18:29Z
dc.date.available2020-12-24T19:18:29Z
dc.date.issued2021-01-05
dc.description.abstractWhen conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.193
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70805
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subjectcustomer targeting
dc.subjectensemble method
dc.subjectpredictive modeling
dc.subjectuplift modeling
dc.titleTo Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles
prism.startingpage1601

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