Churn Prepaid Client Profile in Romanian Postmodernism Telecommunications

Authors

  • Andreea Dumitrache PhD. Student, Academy of Economic Studies, Bucharest, Romania
  • Denisa Maria Melian PhD. Student, Academy of Economic Studies, Bucharest, Romania
  • Stelian Stancu 3 Prof. Univ. Dr., Academy of Economic Studies, Bucharest, Romania

DOI:

https://doi.org/10.18662/po/11.2Sup1/181

Keywords:

Churn, class imbalance, customer

Abstract

The telecommunications industry is one of the sectors in which customers play an essential role in maintaining stable incomes. Having an impact on all spheres of postmodern life, telecommunications helps bring about major changes in the world. This can be considered the reason why the world can grow and grow at such a rapid rate. The telecommunications industry offers not only a better social awareness, but also a better life in general. Customer profiling is a very important resource for telecommunications companies because it helps to form a portrait of their customers. The purpose of this paper is to identify the profile of the client who makes churn from a telecommunications company in Romania. The study is performed on the prepaid segment using an analytical method that is easy to view and interpret, Violin Plot. In our study, this technique identified the situation of the prepaid churn customer in telecommunications as being defined by inactivity, small recharge values and extra-options.

Author Biographies

Andreea Dumitrache, PhD. Student, Academy of Economic Studies, Bucharest, Romania

Student at the Doctoral School of Cybernetics and Statistics

Denisa Maria Melian, PhD. Student, Academy of Economic Studies, Bucharest, Romania

Student at the Doctoral School of Cybernetics and Statistics

Stelian Stancu, 3 Prof. Univ. Dr., Academy of Economic Studies, Bucharest, Romania

Stelian STANCU. University Professor Doctor at the Academy of Economic Studies in Bucharest.

References

Adebiyi, S. O., Oyatoye, E. O., & Kuye, O. L. (2015). An analytic hierarchy process analysis: Application to subscriber retention decisions in the Nigerian mobile telecommunications industry, International Journal of Management and Economics, 48(1), 63 – 83. https://doi.org/10.1515/IJME-2015-0035

Alberts, L. J. S. M. (2006). Churn prediction in the mobile telecommunications industry [Unpublished Masters Thesis]. Department of General Sciences, Maastricht University. https://pdfs.semanticscholar.org/c3a3/f1802e650eadc1400fd4ec79dfd99a80df35.pdf.

Archaux, C., Martin, A., & Khenchaf, A. (2004). An SVM based churn detector in prepaid mobile telephony. International Conference on Information & Communication Technologies (ICTTA) (pp. 19—23). https://doi.org/10.1109/ICTTA.2004.1307830

Băcilă, M. F., Rădulescu, A., & Mărar, I. L. (2013). Customer segmentation based on the value of consumption patterns in telecommunications. International Conference “Marketing – from information to decision” 6th Edition (pp. 1-51). https://www.researchgate.net/publication/268447379_Consumption-Based_Segmentation_an_analysis_of_a_telecom_company's_customers

Baran, R., Zerres, C., & Zerres, M. (2012). Customer relationship management (CRM). https://hvtc.edu.vn/Portals/0/files/635832614505799766customer-relationship-management.pdf

Baumer, B., Kaplan, D. T., & Horton, N. (2017). Modern data science with R. https://doi.org/10.1111/insr.12256

Bobbier, T. (2013). Keeping the customer satisfied: The dynamics of customer defection, and the changing role of the loss adjuster. https://www.iiste.org/Journals/index.php/IKM/article/download/41073/42230

Bose, I., & Xi, C. (2010). Exploring business opportunities from mobile services data of customers: An inter-cluster analysis approach. Electronic Commerce and Applications, 9(3), 197-208. https://doi.org/10.1016/j.elerap.2009.07.006

Dahiya, K., & Bhatia, S. (2018). Customer churn analysis in telecom industry. International Journal of innovative research explorer, 5. http://www.ijire.org/gallery/54-april-631.pdf

Geppert, C. (2002). Customer churn management: retaining high-margin customers with CRM techniques. https://www.amr.kpmg.com/microsite/kpmgme/downloads/CHURN_02_26final.pdf

Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. American Statistician, 52(2), 181-184. https://doi.org/10.2307/2685478

Hossain, M. M., & Suchy, N. J. (2013). Influence of customer satisfaction on loyalty: a study on mobile telecommunication industry. Journal of Social Sciences, 9(2), 73 – 80. https://doi.org/10.3844/jssp.2013.73.80

Hung, S., Yen, D. C., & Wang, H. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515-524. http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/telecomchurnanalysis.pdf

Lazarov, V., & Capota, M. (2007). Churn prediction. Journal, Business Analytics Course. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.462.7201

Lejeune, M. (2001). Measuring the impact of data mining on churn management. Internet Research, 11(5), 375-387. https://doi.org/10.1108/10662240110410183

Poel, D. V., & Lariviere, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157(1), 196-217. https://doi.org/10.1016/S0377-2217(03)00069-9

Richeldi, M., & Perrucci, A. (2002). Churn analysis case study. Enabling end-user datawarehouse. Mining Contract No.: IST-1999-11993. Deliverable No.D17.2. https://sfb876.tu-dortmund.de/PublicPublicationFiles/richeldi_perrucci_2002b.pdf

Storbacka, K. (1997). Segmentation based on customer profitability – retrospective analysis of retail bank customer bases. Journal of Marketing Management, 13(5), 479-492. https://www.tandfonline.com/doi/ref/10.1080/0267257X.1997.9964487?scroll=top

Tsiptsis, K., & Corianopoulos, A. (2009). Data mining tehniques in crm: inside customer segmentation. John Wiley & Sons Ltd.

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Published

2020-09-07

How to Cite

Dumitrache, A., Melian, D. M., & Stancu, S. (2020). Churn Prepaid Client Profile in Romanian Postmodernism Telecommunications. Postmodern Openings, 11(2 Supl 1), 93-106. https://doi.org/10.18662/po/11.2Sup1/181