The University of Southampton
University of Southampton Institutional Repository

Social network analytics for churn prediction in Telco: model building, evaluation and network architecture

Social network analytics for churn prediction in Telco: model building, evaluation and network architecture
Social network analytics for churn prediction in Telco: model building, evaluation and network architecture
Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.
Social Networks Analytics, Churn Prediction, Relational Learning, Collective Inference, Telecommunication Industry, Network Construction
0957-4174
204-220
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Sarraute, Carlos
00c589d2-3b06-4172-88fb-df61068d7106
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanathien, Jan
6f422b84-1d59-4d0d-89cc-d0cc25540022
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Verbeke, Wouter
57c0d98a-130a-4202-b6dd-cdc6914f4732
Sarraute, Carlos
00c589d2-3b06-4172-88fb-df61068d7106
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanathien, Jan
6f422b84-1d59-4d0d-89cc-d0cc25540022

Óskarsdóttir, María, Bravo, Cristian, Verbeke, Wouter, Sarraute, Carlos, Baesens, Bart and Vanathien, Jan (2017) Social network analytics for churn prediction in Telco: model building, evaluation and network architecture. Expert Systems with Applications, 85 (1), 204-220. (doi:10.1016/j.eswa.2017.05.028).

Record type: Article

Abstract

Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.

Text
Oskarsdottir et al. Social Network Analytics for Churn Prediction in Telco - Accepted Manuscript
Download (504kB)
Text
Social Networks for Churn Prediction - Accepted Manuscript
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 7 May 2017
e-pub ahead of print date: 16 May 2017
Published date: 1 November 2017
Keywords: Social Networks Analytics, Churn Prediction, Relational Learning, Collective Inference, Telecommunication Industry, Network Construction
Organisations: Decision Analytics & Risk

Identifiers

Local EPrints ID: 408281
URI: https://eprints.soton.ac.uk/id/eprint/408281
ISSN: 0957-4174
PURE UUID: 52e8e730-c4ac-41ae-af85-f696af029ba8
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 19 May 2017 04:02
Last modified: 17 Sep 2019 04:52

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×