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Annual International Symposium on Information Management and Big Data

Annual International Symposium on Information Management and Big Data

SIMBig 2015, SIMBig 2016: Information Management and Big Data pp 120–132Cite as

Big Data Architecture for Predicting Churn Risk in Mobile Phone Companies

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 656))

Abstract

Nowadays in Peru, mobile phone companies have been affected by the problem of mobile number portability because since July 2014 customers can change their mobile operator in just 24 h. Companies look for solutions through the analysis of historical data of their customers in order to generate predictive models and to identify which customers would leave the company. However, the current way how this prediction is performed is too slow. In this paper, we show a Big Data architecture which solves the problems of the “classic architecture” using data from social networks in order to predict which customers may go to the competition company, according to their opinions. Data processing is performed by Hadoop, which implements MapReduce and can process large amounts of data in parallel way. After doing the tests and seeing the results, we got a high percentage of accuracy (90.03% of success).

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Author information

Authors and Affiliations

  1. Faculty of Systems Engineering, National University of San Marcos, Lima, Peru

    Alonso Raul Melgarejo Galvan & Katerine Rocio Clavo Navarro

Authors
  1. Alonso Raul Melgarejo Galvan
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  2. Katerine Rocio Clavo Navarro
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Corresponding author

Correspondence to Alonso Raul Melgarejo Galvan .

Editor information

Editors and Affiliations

  1. College of Medicine, University of Florida, Gainesville, Florida, USA

    Juan Antonio Lossio-Ventura

  2. Faculty of Engineering, Universidad del Pacífico, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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Melgarejo Galvan, A.R., Clavo Navarro, K.R. (2017). Big Data Architecture for Predicting Churn Risk in Mobile Phone Companies. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-55209-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55208-8

  • Online ISBN: 978-3-319-55209-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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