Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70805

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

File Size Format  
0158.pdf 552.52 kB Adobe PDF View/Open

Item Summary

Title:To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles
Authors:Rößler, Jannik
Tilly, Roman
Schoder, Detlef
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance, and Management
customer targeting
ensemble method
predictive modeling
uplift modeling
Date Issued:05 Jan 2021
Abstract:When 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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70805
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.193
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons