Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services

Yongjun Liao, Wei Du, Márton Karsai, Carlos Sarraute, Martin Minnoni, Eric Fleury

Research output: Contribution to Book/Report typesChapterpeer-review

Abstract (may include machine translation)

In this paper, we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations, we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of similar to 87%, which outperforms by similar to 7% supervised learning methods using only user attributes. In our second problem, we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-way indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing.
Original languageEnglish
Title of host publicationSocial Network Based Big Data Analysis and Applications
EditorsMY Day, S Khoury, J Kawash, M Kaya
Place of PublicationVienna
PublisherSpringer-Verlag Wien
Pages165-181
Number of pages17
ISBN (Electronic)9783319781969
ISBN (Print)9783319781952
DOIs
StatePublished - 2018

Publication series

NameLecture Notes in Social Networks, ISSN 2190-5428

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