Abstract
The telecommunications industry is confronted more and more to aggressive marketing campaigns from competitor carriers. Therefore, they need to improve the subscriber targeting to propose more attractive offers for gaining new subscribers. In the present effort, a five steps methodology to find new potential subscribers using supervised learning techniques over imbalanced datasets is proposed. The proposed technique applies community detection to infers consumption information of competitors carriers subscribers within the communities. Besides, it uses a sampling technique to reduce the effect of a dominant class for an imbalanced classification task. The proposal is evaluated with a real dataset from a Peruvian carrier. The dataset contains one-month data, which is about 200 millions of transaction. The results show that the proposed technique is able to identify between two to ten times more new potential clients, depending on the sampling technique, as shows using the top decile lift value.
Authors appear in alphabetical order, they contribute equally to the present paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amin, A., Shah, B., Khattak, A.M., Baker, T., Anwar, S., et al.: Just-in-time customer churn prediction: With and without data transformation. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6. IEEE (2018)
Columelli, L., Nunez-del Prado, M., Zarate-Gamarra, L.: Measuring churner influence on pre-paid subscribers using fuzzy logic. In: 2016 XLII Latin American Computing Conference (CLEI), pp. 1–10. IEEE (2016)
De Caigny, A., Coussement, K., De Bock, K.W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur. J. Oper. Res. 269(2), 760–772 (2018)
Dugué, N., Perez, A.: Directed Louvain: maximizing modularity in directed networks. Ph.D. thesis, Université d’Orléans (2015)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006)
Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)
Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017). http://jmlr.org/papers/v18/16-365.html
del Prado, M.N., Hendrikx, H.: Toward a route detection method base on detail call records. Working Papers, pp. 16–19. Centro de Investigación, Universidad del Pacífico, December 2016. https://ideas.repec.org/p/pai/wpaper/16-19.html
Saito, T., Rehmsmeier, M.: Precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10(3), 1–21 (2015)
Shobha, G., et al.: Social network classifier for churn prediction in telecom data. In: 2013 International Conference on Advanced Computing and Communication Systems, pp. 1–7. IEEE (2013)
Wu, X., Liu, Z.: How community structure influences epidemic spread in social networks. Physica A: Stat. Mech. Appl. 387(2–3), 623–630 (2008)
Zhu, M.: Recall, precision and average precision. University of Waterloo (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Alatrista-Salas, H., Nunez-del-Prado, M., Zevallos, V. (2020). Come with Me Now: New Potential Consumers Identification from Competitors. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-46140-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46139-3
Online ISBN: 978-3-030-46140-9
eBook Packages: Computer ScienceComputer Science (R0)