Social Network Analytics for Churn Prediction in Telco: Model Building,
Evaluation and Network Architecture
- URL: http://arxiv.org/abs/2001.06701v1
- Date: Sat, 18 Jan 2020 17:09:22 GMT
- Title: Social Network Analytics for Churn Prediction in Telco: Model Building,
Evaluation and Network Architecture
- Authors: Mar\'ia \'Oskarsd\'ottir, Cristi\'an Bravo, Wouter Verbeke, Carlos
Sarraute, Bart Baesens, Jan Vanthienen
- Abstract summary: Social network analytics are being used in the telecommunication industry to predict customer churn with great success.
We benchmark different strategies for constructing a relational learner by applying them to a total of eight call-detail record datasets.
We provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way.
- Score: 8.592714155264613
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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