Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63931

Towards More Robust Uplift Modeling for Churn Prevention in the Presence of Negatively Correlated Estimation Errors

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dc.contributor.author Oechsle, Frank
dc.contributor.author Schönleber, David
dc.date.accessioned 2020-01-04T07:28:13Z
dc.date.available 2020-01-04T07:28:13Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63931
dc.description.abstract The subscription economy is rapidly growing, boosting the importance of churn prevention. However, current true lift models often lead to poor outcomes in churn prevention campaigns. A vital problem seems to lie in instable estimations due to dynamic surrounding parameters such as price increases, product migrations, tariff launches of a competitor, or other events with uncertain consequences. The crucial challenge therefore is to make churn prevention measures more reliable in the presence of game-changing events. In this paper, we assume such events to be spatially finite in feature space, an assumption which leads to particularly bad churn prevention results if the selected customers lump in an affected region of the feature space. We then introduce novel methods which trade off uplift for reduced similarity in feature space when selecting customers for churn prevention campaigns and show that these methods can improve the robustness of uplift modeling.
dc.format.extent 8 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject churn
dc.subject decision trees
dc.subject estimation errors
dc.subject monte carlo
dc.subject uplift modeling
dc.title Towards More Robust Uplift Modeling for Churn Prevention in the Presence of Negatively Correlated Estimation Errors
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.192
Appears in Collections: Service Analytics


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