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Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts

Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts
Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts
The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn of customers. Some of them verify that customers’ social relationships influence the decision of changing the operator.
We propose a novel method to extract the dynamic relevance of each customer using social network analysis techniques with a binary classification method called similarity forests. The dynamic importance of each customer is determined by applying various centrality metrics over temporal graphs, to represent the relationships between customers and to extract behavioral patterns of churners and non-churners. These relationships are established in a temporal graph using the call detail records (CDR) of telco’s customers. In this paper, we compare the performance of different centrality metrics applied over two types of temporal graphs: Time-Order Graph and Aggregated Static Graph.
Centrality metrics, Churn prediction, Similarity forests, Social network analysis, Time series
0957-4174
1-11
Calzada-infante, Laura
20be73a7-b9e8-4c30-b3c4-954eb47bd1a6
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Calzada-infante, Laura
20be73a7-b9e8-4c30-b3c4-954eb47bd1a6
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Calzada-infante, Laura, Óskarsdóttir, María and Baesens, Bart (2020) Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts. Expert Systems with Applications, 160, 1-11, [113553]. (doi:10.1016/j.eswa.2020.113553).

Record type: Article

Abstract

The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn of customers. Some of them verify that customers’ social relationships influence the decision of changing the operator.
We propose a novel method to extract the dynamic relevance of each customer using social network analysis techniques with a binary classification method called similarity forests. The dynamic importance of each customer is determined by applying various centrality metrics over temporal graphs, to represent the relationships between customers and to extract behavioral patterns of churners and non-churners. These relationships are established in a temporal graph using the call detail records (CDR) of telco’s customers. In this paper, we compare the performance of different centrality metrics applied over two types of temporal graphs: Time-Order Graph and Aggregated Static Graph.

Text
ESWA Churnprediction SF R1- 20200320 - Accepted Manuscript
Restricted to Repository staff only until 10 June 2022.
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More information

Accepted/In Press date: 9 May 2020
e-pub ahead of print date: 10 June 2020
Published date: 1 December 2020
Keywords: Centrality metrics, Churn prediction, Similarity forests, Social network analysis, Time series

Identifiers

Local EPrints ID: 445241
URI: http://eprints.soton.ac.uk/id/eprint/445241
ISSN: 0957-4174
PURE UUID: 87176546-0f05-42f9-afc0-7165ee9c062e
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 26 Nov 2020 17:30
Last modified: 28 Apr 2022 01:53

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Contributors

Author: Laura Calzada-infante
Author: María Óskarsdóttir
Author: Bart Baesens ORCID iD

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