On the operational efficiency of different feature types for telco Churn prediction
On the operational efficiency of different feature types for telco Churn prediction
Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies generally agree that adding features typically increases predictive performance, they rarely discuss the accompanying issues such as data availability and computational cost. In this work, we bridge the gap between predictive performance and operational efficiency by devising a new feature type classification and a novel reusable method to determine optimal feature type combinations based on Pareto multi-criteria optimization. Our results provide several insights that can serve as a guideline for industry practitioners.
Churn prediction, Decision support systems, Feature type classification, Operational efficiency, Pareto optimal feature type combinations
1141-1155
Mitrović, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
16 June 2018
Mitrović, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
Mitrović, Sandra, Baesens, Bart, Lemahieu, Wilfried and De Weerdt, Jochen
(2018)
On the operational efficiency of different feature types for telco Churn prediction.
European Journal of Operational Research, 267 (3), .
(doi:10.1016/j.ejor.2017.12.015).
Abstract
Churn prediction in telco remains a very active research topic. Due to the uptake of social network analytics and the results of previous benchmarking studies showing a rather flat maximum performance effect of predictive modeling techniques, the focus has mainly shifted to expanding and exploring the relevant feature space. While previous studies generally agree that adding features typically increases predictive performance, they rarely discuss the accompanying issues such as data availability and computational cost. In this work, we bridge the gap between predictive performance and operational efficiency by devising a new feature type classification and a novel reusable method to determine optimal feature type combinations based on Pareto multi-criteria optimization. Our results provide several insights that can serve as a guideline for industry practitioners.
Text
On the Operational Efficiency of Different Feature Types for Telco Churn Prediction-
- Accepted Manuscript
More information
Accepted/In Press date: 7 December 2017
e-pub ahead of print date: 14 December 2017
Published date: 16 June 2018
Keywords:
Churn prediction, Decision support systems, Feature type classification, Operational efficiency, Pareto optimal feature type combinations
Identifiers
Local EPrints ID: 418897
URI: http://eprints.soton.ac.uk/id/eprint/418897
ISSN: 0377-2217
PURE UUID: a90d3772-0356-4254-8909-545e4edf51ab
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Date deposited: 23 Mar 2018 17:31
Last modified: 16 Mar 2024 06:14
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Contributors
Author:
Sandra Mitrović
Author:
Wilfried Lemahieu
Author:
Jochen De Weerdt
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