PL EN


2012 | 13 | 3 | 65-78
Article title

Problemy modelowania rezygnacji klientów w telefonii komórkowej

Content
Title variants
EN
Problems of churn modelling at cellular telecommunication
Languages of publication
PL
Abstracts
PL
Przewidywanie i zarządzanie rezygnacjami klientów jest problemem wielu działalności gospodarczych, ale jest ono szczególnie dotkliwe w silnie konkurencyjnym sektorze telefonii komórkowej. Ze względu na wysokie koszty pozyskania nowych klientów i korzyści wynikające z utrzymania istniejących, istotną rolę w tego typu problemach odgrywają modele przewidujące rezygnację klientów. W tym kontekście autorzy artykułu, na podstawie danych empirycznych, zwracają uwagę na takie kwestie jak: (1) przygotowanie danych do analizy (2) problem doboru cech, (3) dobór odpowiednich technik modelowania wraz z oceną ich przydatności w kampaniach utrzymaniowych.
EN
Managing of customer churn is a serious problem at many businesses but is particularly important in the highly competitive and liberalized the cellular telecommunication sector. Due to the high costs of acquiring new customers and significant benefits of keeping existing ones, the predictive models for churn classification play an important role in this business. In this context, based on empirical data, the authors will tackle such issues as: (1) the problem of variable selection to determine the churn, (2) the problem of data preparation for the analysis; (3) selection of appropriate modeling techniques and the construction of models with their evaluation to support the retention campaigns.
Year
Volume
13
Issue
3
Pages
65-78
Physical description
Dates
published
2012
Contributors
  • Katedra Informatyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
  • Katedra Informatyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
References
  • Ahn J-H., Han S-P., Lee Y-S. (2006) Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry, Telecommunications policy 30, str. 552-568.
  • Brandt S. (1998) Analiza danych. Metody statystyczne i obliczeniowe, Wydawnictwo Naukowe PWN, Warszawa.
  • Burez J., Van den Poel D. (2007) CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services, Expert Systems with Applications 32(2), str. 277-288.
  • Burez J., Van den Poel D. (2008) Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department, Expert Systems with Applications 35 str. 497-514.
  • Coussement K., Van den Poel D. (2008) Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques, Expert Systems with Applications 34(1), str. 313-327.
  • Daskalaki S., Kopanas I., Avouris N. (2006) Evaluation of classifiers for an uneven class distribution problem, Applied Artificial Intelligence Vol. 20 (5), str. 381-417.
  • Drew S., Homem T. (2012) Some Large Deviations Results for Latin Hypercube Sampling, Methodology and Computing in Applied Probability 14 (2), str. 203-232.
  • Fortuna Z., Macukow B., Wąski J. (2002) Metody numeryczne, Wydawnictwo Naukowo- Techniczne, Warszawa.
  • Glady N., Baesens B., Croux C. (2009a) Modeling churn using customer lifetime value, European Journal of Operational Research 197, str. 402-411.
  • Glady N., Baesens B., Croux C. (2009b) A modified Pareto/NBD approach for predicting customer lifetime value, Expert Systems with Applications 36, str. 2062-2071.
  • Grupa Telekomunikacja Polska. (2011) Sprawozdanie Zarządu z działalności Grupy Kapitałowej Telekomunikacja Polska w pierwszym półroczu 2011 roku,. str. 19-21,
  • Pozyskano z : http://www.telix.pl/images/sprawozdania/TP_Grupa_2q2011.pdf.
  • Haas C. (1999) On Modeling Correlated Random Variables in Risk Assessment, Risk Analysis 19 (6), str. 1205-1214.
  • Huang B., Kechadi M., Buckley B. (2012) Customer churn prediction in telecommunications, Expert Systems with Applications 39, str. 1414-1425.
  • Hung S.Y., Yen D.C., Wang H.Y. (2006) Applying data mining to telecom churn management, Expert Systems with Applications 31, str. 515–524.
  • Hwang H., Jung T., Suh E. (2004) An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry, Expert System with Applications 26, str. 181-188.
  • Karus S., Dumas M. (2011) Predicting the maintainability of XSL transformations, Science of Computer Programming 76, str. 1161-1176.
  • Keramati A., Ardabili S. (2011) Churn analysis for an Iranian mobile operator, Telecommunications Policy 35, str. 344-356.
  • Kim H., Yoon C. (2004) Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market, Telecommunications Policy 28, str. 751–765.
  • Kohs G. (2006) Comparison of Churn Rates, Inside Market Research, June 2006, Pozyskano z : http://insidemr.blogspot.com/2006/06/comparison-of-churn-rates.html.
  • Larose D.T. (2006) Odkrywanie wiedzy z danych. Wprowadzenie do eksploracji danych, Wydawnictwo Naukowe PWN, Warszawa.
  • Lu J. (2002) Predicting customer churn in the telecommunications industry – An applications of survival analysis modeling using SAS, Proceedings of SUGI 27, Orlando, Florida, Paper 114.
  • Maddala G. S. (2006) Ekonometria, Wydawnictwo Naukowe PWN, Warszawa.
  • Madden G., Savage S., Coble-Neal G. (1999) Subscriber churn in the Australian ISP market, Information Economics and Policy 11, str. 195-207.
  • Napierała K., Stefanowski J. (2011) BRACID: a comprehensive approach to learning rules from imbalanced data, Journal of Intelligent Information Systems, Pozyskano z : http://www.springerlink.com/content/d484131415313k17/fulltext.pdf .
  • Neslin S. (2002) Cell2Cell: The churn game. Cell2Cell Case Notes. Hanover, NH: Tuck School of Business, Dartmoth College.
  • Neslin S., Gupta S., Kamakura W., Lu J., Mason C. (2006) Defection detection: Measuring and understanding the predictive accuracy of customer churn models, Journal of Marketing Research 43(2), str. 204-211.
  • Seo D., Ranganathan C., Babad Y. (2008) Two-level model of customer retention in the US mobile telecommunications service market, Telecommunications Policy, Volume 32, Issue 3-4, str. 182-196.
  • Sulikowski P. (2008) Mobile Operator Customer Classification in Churn Analysis, Proceedings of the SAS Global Forum Conference, San Antonio, Texas, paper 344.
  • Tsai C-F., Lu Y-H. (2009) Customer churn prediction by hybrid neural networks, Expert Systems with Applications 36, str. 12547-12553.
  • Waal de D., Toit du J. (2008) Gaining Insight into Customer Churn Prediction using Generalized Additive Neural Networks, Proceedings of SATNAC - South Africa Telecommunication Networks and Applications.
  • Wei C., Chiu I-T. (2002) Turning telecommunications call details to churn prediction: a data mining approach, Expert Systems with Applications 23, str.103-112.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-225837e7-e86a-4b7a-92d6-a9e98006833c
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