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Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data

  • Claudia Tarantola


    (Department of Economics, University of Pavia)

  • Paola Vicard


    (Department of Economics, University of Roma Tre)

  • Ioannis Ntzoufras


    (Department of Statistics, Athens University of Economics and Business)

Registered author(s):

    Mystery shopping is a well known marketing technique used by companies and marketing analysts to measure quality of service, and gather information about products and services. In this article, we analyse data from mystery shopping surveys via Bayesian networks in order to examine and evaluate the quality of service offered by the loan departments of Greek banks. We use mystery shopping visits to collect information about loan products and services and, by this way, evaluate the customer satisfaction and plan improvement strategies that will assist Banks to reach their internal standards. Bayesian Networks not only provide a pictorial representation of the dependence structure between the characteristics of interest but also allow to evaluate, interpret and understand the effects of possible improvement strategies.

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    Paper provided by University of Pavia, Department of Economics and Quantitative Methods in its series Quaderni di Dipartimento with number 160.

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    Length: 24 pages
    Date of creation: Jan 2012
    Date of revision:
    Handle: RePEc:pav:wpaper:160
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    1. Mihelis, G. & Grigoroudis, E. & Siskos, Y. & Politis, Y. & Malandrakis, Y., 2001. "Customer satisfaction measurement in the private bank sector," European Journal of Operational Research, Elsevier, vol. 130(2), pages 347-360, April.
    2. John Mylonakis, 2009. "Customer Relationship Management Functions: A Survey of Greek Bank Customer Satisfaction Perceptions," The IUP Journal of Bank Management, IUP Publications, vol. 0(2), pages 7-31, May.
    3. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    4. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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