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

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
  • Handle: RePEc:pav:wpaper:160
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    File URL: http://economia.unipv.it/docs/dipeco/quad/ps/RePEc/pav/wpaper/q160.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application," Annals of Operations Research, Springer, vol. 145(1), pages 301-319, July.
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    Cited by:

    1. repec:eee:transa:v:106:y:2017:i:c:p:235-247 is not listed on IDEAS
    2. Flaminia Musella & Paola Vicard, 2015. "Object-oriented Bayesian networks for complex quality management problems," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 115-133, January.

    More about this item

    Keywords

    Bayesian networks; Customer satisfaction; Mystery shopping; Service quality improvement.;

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