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A comparative study of social network classifiers for predicting churn in the telecommunication industry

María Óskarsdóttir · Cristián Bravo · Wouter Verbeke · Carlos Sarraute · Bart Baesens · Jan Vanthienen

28 citations0/0 files storedIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Customer churn and segmentation Complex Network Analysis Techniques Data Mining and Machine Learning Applications

Abstract

Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the telecommunication industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.

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References

2001Birds of a Feather: Homophily in Social Networks 1999Building Data Mining Applications for CRM 2005Applying data mining to telecom churn management 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2008Customer churn prediction using improved balanced random forests 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2008Social ties and their relevance to churn in mobile telecom networks 2008Modeling churn using customer lifetime value

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