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Identifying Prime Customers Based on MobileUsage Patterns

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2016)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 1))

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Abstract

Increasing competition has led telecommunications service providers to develop marketing strategies to retain their customers and attract new customers. Big data analytics offers some promising solutions that can aid telecommunications service providers in raising their revenues as well as designing better business strategies. In this work, we illustrate how usage behaviors of mobile phone users can be used to identify customers that generate more revenue. Additionally, telecommunications companies can also find a number of people in a customer’s network and can potentially use this information for their benefits. The companies can identify the users with a large network and encourage them to serve as ambassadors to promote their services within their networks in exchange for certain incentives. For experimentation in this paper, we used the strath/nodobo dataset (v.2011-03-23) [1] comprising of customers’ phone usage records. Unsupervised classification technique (clustering) is employed to find both high and low mobile usage customers based on the frequency of their calls, messages and their average call duration. Furthermore, users with a large number of outgoing calls and who are connected to many other users are also identified.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, University of Victoria, Victoria, Canada

    Deepali Arora & Kin Fun Li

Authors
  1. Deepali Arora
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  2. Kin Fun Li
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Corresponding author

Correspondence to Kin Fun Li .

Editor information

Editors and Affiliations

  1. Campus Nord,Ed. Omega (Room 109), Technical University of Catalonia Campus Nord,Ed. Omega (Room 109), Barcelona, Spain

    Fatos Xhafa

  2. Fukuoka Institute of Technology , Fukuoka, Japan

    Leonard Barolli

  3. Federico II, UniversitĂ  degli Studi di Napoli Federico II, Napoli, Italy

    Flora Amato

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Arora, D., Li, K.F. (2017). Identifying Prime Customers Based on MobileUsage Patterns. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_82

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  • DOI: https://doi.org/10.1007/978-3-319-49109-7_82

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49108-0

  • Online ISBN: 978-3-319-49109-7

  • eBook Packages: EngineeringEngineering (R0)

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