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Revenue recovering with insolvency prevention on a Brazilian telecom operator

Authors:

Carlos André R. Pinheiro,

Alexandre G. Evsukoff,

Nelson F. F. EbeckenAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 8, Issue 1

Pages 65 - 70

https://doi.org/10.1145/1147234.1147244

Published: 01 June 2006 Publication History

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Abstract

This paper deals with a real application on a brazilian telephone company. Data mining analysis, based on neural networks, was performed on the customer base in order to understand and to prevent bad debt events. This paper describes the knowledge discovering process and focuses on two main products: the cluster analysis of the customer base and a bad debt event classification model. The segmentation of the customer base has provided a better understanding of several groups of customers' behavior. Distinct actions are taken, depending on the segment a given client was put in, according to strategic directions of the company. The classification of insolvent customers is used as a tool to help the company to take preventing actions in order to avoid main losses and taxes leakage. The results of the project's implantation show that investment on information technology infra-structure for data mining is highly profitable.

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Gajowniczek KOrłowski AZąbkowski T(2019)Insolvency modeling with generalized entropy cost function in neural networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2019.03.095Online publication date: Apr-2019
https://doi.org/10.1016/j.physa.2019.03.095
Zabkowski T(2016)RFM approach for telecom insolvency modelingKybernetes10.1108/K-04-2015-011345:5(815-827)Online publication date: 3-May-2016
https://doi.org/10.1108/K-04-2015-0113
Kim JKang P(2016)Late payment prediction models for fair allocation of customer contact lists to call center agentsDecision Support Systems10.1016/j.dss.2016.03.00285:C(84-101)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.dss.2016.03.002
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Revenue recovering with insolvency prevention on a Brazilian telecom operator

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Published In

ACM SIGKDD Explorations Newsletter Volume 8, Issue 1

June 2006

104 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1147234

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Association for Computing Machinery

New York, NY, United States

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Published: 01 June 2006

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Gajowniczek KOrłowski AZąbkowski T(2019)Insolvency modeling with generalized entropy cost function in neural networksPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2019.03.095Online publication date: Apr-2019
https://doi.org/10.1016/j.physa.2019.03.095
Zabkowski T(2016)RFM approach for telecom insolvency modelingKybernetes10.1108/K-04-2015-011345:5(815-827)Online publication date: 3-May-2016
https://doi.org/10.1108/K-04-2015-0113
Kim JKang P(2016)Late payment prediction models for fair allocation of customer contact lists to call center agentsDecision Support Systems10.1016/j.dss.2016.03.00285:C(84-101)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.dss.2016.03.002
(2015)BibliographyHeuristics in Analytics10.1002/9781118434260.biblio(209-216)Online publication date: 12-Sep-2015
https://doi.org/10.1002/9781118434260.biblio
Moudani WZaarour GMora-Camino F(2014)Fuzzy Prediction of Insolvent Customers in Mobile TelecommunicationInternational Journal of Strategic Information Technology and Applications10.4018/ijsita.20140701015:3(1-23)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.4018/ijsita.2014070101
Moudani WZaarour GMora-Camino F(2014)Fuzzy Classification of Customer Insolvency in Mobile TelecommunicationInternational Journal of Decision Support System Technology10.4018/ijdsst.20140701016:3(1-29)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.4018/ijdsst.2014070101
Zbkowski TSzczesny W(2012)Insolvency modeling in the cellular telecommunication industryExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.01.00839:8(6879-6886)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.01.008
Hsu KSrivastava J(2012)Improving bagging performance through multi-algorithm ensemblesFrontiers of Computer Science10.1007/s11704-012-1163-6Online publication date: 8-Jul-2012
https://doi.org/10.1007/s11704-012-1163-6
Hsu KSrivastava J(2011)Improving bagging performance through multi-algorithm ensemblesProceedings of the 15th international conference on New Frontiers in Applied Data Mining10.1007/978-3-642-28320-8_40(471-482)Online publication date: 24-May-2011
https://dl.acm.org/doi/10.1007/978-3-642-28320-8_40

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A Customer Management Dilemma: When Is It Profitable to Reward One's Own Customers?

Research Note---How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach

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