CRM development in practice, customer relationship management innovation

Authors

  • János Papp Szent István University
  • Ede Lázár Sapientia Hungarian University of Transylvania
  • Ágnes Brix Szent István University

DOI:

https://doi.org/10.18531/Studia.Mundi.2015.02.02.136-148

Keywords:

Costumer relationship, predicting model, CRM

Abstract

This research shows a customer relationship model, which does not only implement innovative elements into a specific supplier’s customer relationship management (CRM), but also reorganizes the customer service’s procedure. The primary aim of the model is to increase customer satisfaction, and to decrease the number of leaving customers, and furthermore to win new customers. The most important results of the model is the customer loyalty measure, which directly determines the customer relationship. Furthermore the efficiency of the different promotions, CRM elements and CRM channels, is shown by the analysis of the estimated explanatory variable’s parameters.

Author Biographies

  • János Papp, Szent István University

    associate professor

  • Ede Lázár, Sapientia Hungarian University of Transylvania

    associate professor and vice dean

  • Ágnes Brix, Szent István University

    PhD student

References

Amemiya T. (1985): Advanced econometrics. : Harvard University Press. 521. p., ISBN 0-674-00560-0

Borgulya I. (1998): Neurális hálók és fuzzy rendszerek. Dialog Campus.ISBN: 9789639123274

Fajszi B. – Cser L. (2004): Üzleti tudás az adatok mélyén. Budapesti Műszaki és Gazdaságtudományi Egyetem. 260. p., ISBN: 9634215580

Franses P.H. – Paap R. (2001): Quantitative models in marketing research. Cambridge: Cambridge University Press. 206. p., ISBN 0-521-80166-4

Greene W. (2003): Econometric analysis. Fifth Edition. New Jersey, Upper Saddle River: Prentice Hall. 1083. p., ISBN 9788177586848

Hajdu O. (2003): Többváltozós statisztikai számítások. Budapest: Aula Kiadó 457. p., ISBN 963-215-600-5

Hosmer D. W. ― Lemeshow S. (2000): Applied Logistic Regression, 2nd edition. New York: Wiley, 392p., ISBN 9780470582473

Kumar A. - VIthala R. R. - HARSH S. (1995) An Empirical Comparison of Neural Network and Logistic Regression Models, Marketing Letters, Vol. 6. No. 4. 251-264. p., ISSN: 0923-0645 DOI: http://dx.doi.org/10.1007/BF00996189

Long J. S. (1997): Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage. 297 p., ISBN 9780803973749 DOI: http://dx.doi.org/10.1086/231290

Oravecz B. (2007): Credit scoring modellek és teljesítményük értékelése, Hitelintézeti Szemle a Magyar Bankszövetség kiadványa, Vol. 2007. No. 6. 607-627. p., ISSN 1588- 6883

Sajtos L.―Mitev A. (2007): SPSS kutatási és adatelemzési kézikönyv. Budapest: Alinea Kiadó. 402. p., ISBN 978-963-9659-08-7

de SÁ J.P.M. (2007): Applied Statistics Using SPSS, STATISTICA, MATLAB and R.. Heidelberg: Springer. 520. p., ISBN 978-3-540-71971-7 DOI: http://dx.doi.org/10.1007/978- 3-540-71972-4

Schweidel D. A. , Fader P. S., Bradlow E. T. (2008): Understanding Service Retention Within and Across Cohorts Using Limited Information, Journal of Marketing Vol. 72. No. 1. 82-94. p., ISSN 0022-2429 DOI: http://dx.doi.org/10.1509/jmkg.72.1.82

Downloads

Published

2015-11-02

Similar Articles

21-30 of 44

You may also start an advanced similarity search for this article.