Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2012

Abstract

In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our CC approach using real data provided by an established mobile social networking site, with a primary focus on prediction of churn in chat activities. Our results demonstrate that using CC and social features derived from interaction records and network structure yields substantially improved prediction in comparison to using conventional classification and user profile features only.

Keywords

Chat activity, Collective churn prediction method, Collective classification, Interaction record, Network structure, Service provider profitability, Service-based industry, Social factor, Social network, User community, User profile factor

Discipline

Databases and Information Systems

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: 26-29 August 2012, Istanbul, Turkey

First Page

210

Last Page

214

ISBN

9781467324977

Identifier

10.1109/ASONAM.2012.44

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

LARC

Additional URL

http://doi.org/10.1109/ASONAM.2012.44

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