IDEAS home Printed from https://ideas.repec.org/a/ora/journl/v1y2012i1p1112-1118.html
   My bibliography  Save this article

Prepaid Telecom Customers Segmentation Using The K-Mean Algorithm

Author

Listed:
  • Bacila Mihai-Florin

    (Universitatea Babes-Bolyai din Cluj-Napoca, Facultatea de Stiinte Economice si Gestiunea Afacerilor)

  • Radulescu Adrian

    (Business Logic Systems Ltd,)

  • Marar Liviu Ioan

    (Business Logic Systems Ltd,)

Abstract

The scope of relationship marketing is to retain customers and win their loyalty. This can be achieved if the companiesâ€(tm) products and services are developed and sold considering customersâ€(tm) demands. Fulfilling customersâ€(tm) demands, taken as the starting point of relationship marketing, can be obtained by acknowledging that the customersâ€(tm) needs and wishes are heterogeneous. The segmentation of the customersâ€(tm) base allows operators to overcome this because it illustrates the whole heterogeneous market as the sum of smaller homogeneous markets. The concept of segmentation relies on the high probability of persons grouped into segments based on common demands and behaviours to have a similar response to marketing strategies. This article focuses on the segmentation of a telecom customer base according to specific and noticeable criteria of a certain service. Although the segmentation concept is widely approached in professional literature, articles on the segmentation of a telecom customer base are very scarce, due to the strategic nature of this information. Market segmentation is carried out based on how customers spent their money on credit recharging, on making calls, on sending SMS and on Internet navigation. The method used for customer segmentation is the K-mean cluster analysis. To assess the internal cohesion of the clusters we employed the average sum of squares error indicator, and to determine the differences among the clusters we used the ANOVA and the post-hoc Tukey tests. The analyses revealed seven customer segments with different features and behaviours. The results enable the telecom company to conceive marketing strategies and planning which lead to better understanding of its customersâ€(tm) needs and ultimately to a more efficient relationship with the subscribers and enhanced customer satisfaction. At the same time, the results enable the description and characterization of expenditure patterns for services that are continuously growing. Also, the study demonstrates this analysis model is efficient for a large customer base.

Suggested Citation

  • Bacila Mihai-Florin & Radulescu Adrian & Marar Liviu Ioan, 2012. "Prepaid Telecom Customers Segmentation Using The K-Mean Algorithm," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 1112-1118, July.
    • Handle: RePEc:ora:journl:v:1:y:2012:i:1:p:1112-1118
    as

    Download full text from publisher

    File URL: http://anale.steconomiceuoradea.ro/volume/2012/n1/164.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    2. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    3. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
    4. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    5. Udoinyang G. Inyang & Okure O. Obot & Moses E. Ekpenyong & Aliu M. Bolanle, 2017. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification," Modern Applied Science, Canadian Center of Science and Education, vol. 11(9), pages 151-151, September.
    6. Yingqiu Zhu & Qiong Deng & Danyang Huang & Bingyi Jing & Bo Zhang, 2021. "Clustering based on Kolmogorov–Smirnov statistic with application to bank card transaction data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 558-578, June.
    7. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    8. Philippe Baecke & Dirk Van Den Poel, 2010. "Improving Purchasing Behavior Predictions By Data Augmentation With Situational Variables," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(06), pages 853-872.
    9. Albarrán Lozano, Irene & Marín Díazaraque, Juan Miguel & Alonso, Pablo J., 2011. "Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view," DES - Working Papers. Statistics and Econometrics. WS ws113729, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
    11. Cinar, E. Mine & Hienkel, Tyler & Horwitz, William, 2019. "Comparative entrepreneurship factors between North Mediterranean and North African Countries: A regression tree analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 88-94.
    12. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
    13. Legohérel, Patrick & Hsu, Cathy H.C. & Daucé, Bruno, 2015. "Variety-seeking: Using the CHAID segmentation approach in analyzing the international traveler market," Tourism Management, Elsevier, vol. 46(C), pages 359-366.
    14. Pagn, Jos A. & Pratt, William R. & Sun, Jun, 2009. "Which physicians have access to electronic prescribing and which ones end up using it?," Health Policy, Elsevier, vol. 89(3), pages 288-294, March.
    15. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    16. Farías, Pablo, 2019. "Determinants of knowledge of personal loans' total costs: How price consciousness, financial literacy, purchase recency and frequency work together," Journal of Business Research, Elsevier, vol. 102(C), pages 212-219.
    17. Hayk Manucharyan, 2020. "How do managers actually choose suppliers? Evidence from revealed preference data," Working Papers 2020-12, Faculty of Economic Sciences, University of Warsaw.
    18. David Olson & Qing Cao & Ching Gu & Donhee Lee, 2009. "Comparison of customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 3(2), pages 117-130, June.
    19. Azarnoush Ansari & Arash Riasi, 2016. "Taxonomy of Marketing Strategies Using Bank Customers’ Clustering," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(7), pages 106-106, June.
    20. Tien-Hsiang Chang & Kuei-Ying Hsu & Hsin-Pin Fu & Ying-Hua Teng & Yi-Jhen Li, 2022. "Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis," Sustainability, MDPI, vol. 14(3), pages 1-15, February.

    More about this item

    Keywords

    market segmentation; profiling segments; telecommunication services; k-mean cluster; relationship marketing;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ora:journl:v:1:y:2012:i:1:p:1112-1118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catalin ZMOLE (email available below). General contact details of provider: https://edirc.repec.org/data/feoraro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.