Universität zu Köln

Gattermann-Itschert, Theresa ORCID: 0000-0002-9202-1495 and Thonemann, Ulrich W. (2021). How training on multiple time slices improves performance in churn prediction. Eur. J. Oper. Res., 295 (2). S. 664 - 675. AMSTERDAM: ELSEVIER. ISSN 1872-6860

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Abstract

Customer churn prediction models using machine learning classification have been developed predominantly by training and testing on one time slice of data. We train models on multiple time slices of data and refer to this approach as multi-slicing. Our results show that given the same time frame of data, multi-slicing significantly improves churn prediction performance compared to training on the entire data set as one time slice. We demonstrate that besides an increased training set size, the improvement is driven by training on samples from different time slices. For data from a convenience wholesaler, we show that multi-slicing addresses the rarity of churn samples and the risk of overfitting to the distinctive situation in a single training time slice. Multi-slicing makes a model more generalizable, which is particularly relevant whenever conditions change or fluctuate over time. We also discuss how to choose the number of time slices. (c) 2021 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Gattermann-Itschert, Theresa UNSPECIFIED orcid.org/0000-0002-9202-1495 UNSPECIFIED Thonemann, Ulrich W. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-563839
DOI:	10.1016/j.ejor.2021.05.035
Journal or Publication Title:	Eur. J. Oper. Res.
Volume:	295
Number:	2
Page Range:	S. 664 - 675
Date:	2021
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1872-6860
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SUPPORT VECTOR MACHINE; CUSTOMER CHURN; REGRESSION; ATTRITION; SELECTION; MODELS; RETENTION; DEFECTION; INDUSTRY; CRM Multiple languages Management; Operations Research & Management Science Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/56383