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Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

Predicting time-to-churn of prepaid mobile telephone customers using social network analysis
Predicting time-to-churn of prepaid mobile telephone customers using social network analysis
Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.
0160-5682
1135-1145
Backiel, Aimée
c5eae1ee-5a39-4d4b-b5f7-45dd03d61d69
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Claeskens, Gerda
81df6aa1-7480-456b-b298-82e9c29dde4a
Backiel, Aimée
c5eae1ee-5a39-4d4b-b5f7-45dd03d61d69
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Claeskens, Gerda
81df6aa1-7480-456b-b298-82e9c29dde4a

Backiel, Aimée, Baesens, Bart and Claeskens, Gerda (2016) Predicting time-to-churn of prepaid mobile telephone customers using social network analysis. Journal of the Operational Research Society, 67 (9), 1135-1145. (doi:10.1057/jors.2016.8).

Record type: Article

Abstract

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.

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More information

e-pub ahead of print date: 16 March 2016
Published date: 1 September 2016

Identifiers

Local EPrints ID: 425618
URI: https://eprints.soton.ac.uk/id/eprint/425618
ISSN: 0160-5682
PURE UUID: d34dec70-0486-43f2-be72-8a143723d891
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 26 Oct 2018 16:30
Last modified: 15 Aug 2019 00:44

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