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Churn Prediction Using Dynamic RFM-Augmented Node2vec

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Personal Analytics and Privacy. An Individual and Collective Perspective (PAP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10708))

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Abstract

Current studies on churn prediction in telco apply network analytics to analyze and featurize call graphs. While the suggested approaches demonstrate a lot of creativity when it comes to deriving new features from the underlying networks, they also exhibit at least one of the following problems: they either do not account properly for dynamic aspects of call networks or they do not exploit the full potential of joint interaction and structural features and additionally, they usually address these in a non-systematic manner which involves hand-engineering of features. In this study, we propose a novel approach in which we address each of the identified issues. In a nutshell, first, we propose slicing a monthly call graph to capture dynamic changes in calling patterns. Second, we devise network designs which conjoin interaction and structural information. Third, we adapt and apply the node2vec method to learn node representations in a more automated way and to avoid the need for feature handcrafting.

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Notes

  1. 1.

    The terms dynamic, temporal, (time-)evolving, time-varying (networks) are also used interchangeably in the literature.

  2. 2.

    A churner is an individual who has stopped using mobile operator services.

  3. 3.

    The fourth week is considered to begin at \(22^{nd}\) day and lasts till the end of the month, hence longer than three previous weeks.

  4. 4.

    Exceptionally, due to the skewed distribution of R/F/M values, one can end up having less than five quintiles per R/F/M.

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Author information

Authors and Affiliations

  1. Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium

    Sandra Mitrović, Bart Baesens, Wilfried Lemahieu & Jochen De Weerdt

  2. School of Management, University of Southampton, Southampton, UK

    Bart Baesens

Authors
  1. Sandra Mitrović
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  2. Bart Baesens
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  3. Wilfried Lemahieu
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  4. Jochen De Weerdt
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Corresponding author

Correspondence to Sandra Mitrović .

Editor information

Editors and Affiliations

  1. KDDLab, ISTI-CNR, Pisa, Italy

    Riccardo Guidotti

  2. KDDLab, University of Pisa, Pisa, Italy

    Anna Monreale

  3. KDDLab, University of Pisa, Pisa, Italy

    Dino Pedreschi

  4. Inria, École Normale Supérieure, Paris, France

    Serge Abiteboul

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Mitrović, S., Baesens, B., Lemahieu, W., De Weerdt, J. (2017). Churn Prediction Using Dynamic RFM-Augmented Node2vec. In: Guidotti, R., Monreale, A., Pedreschi, D., Abiteboul, S. (eds) Personal Analytics and Privacy. An Individual and Collective Perspective. PAP 2017. Lecture Notes in Computer Science(), vol 10708. Springer, Cham. https://doi.org/10.1007/978-3-319-71970-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-71970-2_11

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  • Online ISBN: 978-3-319-71970-2

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