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Banking customer satisfaction evaluation: a three-way factor perspective


  • Caterina Liberati


  • Paolo Mariani



As management of a national bank wanted to analyze its retail service competition loss probably due to low customer satisfaction, we carried out an empirical study based on a sample of 27,000 retail customers. The survey aimed to analyze retail service weaknesses and to individuate possible recovery actions measuring their effectiveness across different waves (three time lags). We studied a definition of a new dissimilarity measure exploiting a dimension reduction obtained by a three-way factor analysis (TFA). We had previously focused our attention on the limits of this approach related to the geometrical properties of the TFA applied. We introduced a reassessment of the points to adjust the three-way solution according to the quality of representation of the points. This transformation only rescaled the factor scores producing a local adjustment of the point configuration. We then performed a trajectory analysis of the different waves. The results showed the effectiveness of our approach. Therefore, further study of the derivation of a synthetic measure of cluster routes seems appropriate. Copyright Springer-Verlag Berlin Heidelberg 2012

Suggested Citation

  • Caterina Liberati & Paolo Mariani, 2012. "Banking customer satisfaction evaluation: a three-way factor perspective," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 323-336, December.
  • Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:323-336
    DOI: 10.1007/s11634-012-0118-y

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    References listed on IDEAS

    1. Arbore, Alessandro & Busacca, Bruno, 2009. "Customer satisfaction and dissatisfaction in retail banking: Exploring the asymmetric impact of attribute performances," Journal of Retailing and Consumer Services, Elsevier, vol. 16(4), pages 271-280.
    2. 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.
    3. Escofier, B. & Pages, J., 1994. "Multiple factor analysis (AFMULT package)," Computational Statistics & Data Analysis, Elsevier, vol. 18(1), pages 121-140, August.
    4. Henk Kiers, 1991. "Hierarchical relations among three-way methods," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 449-470, September.
    5. Rizzi, Alfredo & Vichi, Maurizio, 1995. "Representation, synthesis, variability and data preprocessing of a three-way data set," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 203-222, February.
    6. Pierpaolo D’Urso, 2000. "Dissimilarity measures for time trajectories," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 9(1), pages 53-83, January.
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