Skip to main content
SpringerLink
Log in

Dynamic Principal Component Analysis: A Banking Customer Satisfaction Evaluation

  • Conference paper
  • First Online:
Algorithms from and for Nature and Life

Abstract

An empirical study, based on a sample of 27.000 retail customers, has been curried out: the management of a national bank with a spread network across Italian regions wanted to analyze the loss in competition of its retail services, probably due to a loss in customer satisfaction. The survey has the aim to analyze weaknesses of retail services, individuate possible recovery actions and evaluate their effectiveness across different waves (3 time lags). Such issues head our study towards a definition of a new path measure which exploits a dimension reduction obtained with Dynamic Principal Component Analysis (DPCA). Results which shown customer satisfaction configurations are discussed in the light of the possible marketing actions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The significance of the correlation value between Weighted Multiway Factor Axes WMFA f1 and WMFAf2 has been computed via Pearson test \((\rho = -0.0163\ p -\mathit{value} = 0.9356)\) and Spearman test \((\rho = 0.0537\ p -\mathit{value} = 0.7898)\). It turned to be not zero but its value was not significative.

References

  • ABI: Dimensione Cliente (2009). Bancaria Editrice, Roma.

    Google Scholar 

  • Berry, L. L., Parasuraman, A., & Zeithaml, V. A. (1985). A conceptual model of service qualit and its implications for future research. The Journal of Marketing, 49, 41–50.

    Google Scholar 

  • Berry, L. L., Parasuraman, A., & Zeithaml, V. A. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–37.

    Google Scholar 

  • Berry, L. L., Parasuraman, A., & Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67, 420–450.

    Google Scholar 

  • Berry, L. L., Parasuraman, A., & Zeithaml, V.A. (1993). More on improving service quality measurement. Journal of Retailing, 69, 140–147.

    Article  Google Scholar 

  • Escofier, B., & Pagès, J. (1982). Comparaison de groupes des variables dèfiniers sue le mème ensenble d’individus (Rapport IRISA n.166). Rennes.

    Google Scholar 

  • Escofier, B., & Pag ès, J. (1994). Multiple factor analysis (AFMULT package). Computational Statistics & Data Analysis, 18, 121–140.

    Google Scholar 

  • Carlier, A. (1986). Factor analysis of evolution and cluster methods on trajectories. In F. de Antoni, N. Lauro, & A. Rizzi (Eds.), COMPSTAT86, Heidelberg, Germany; Wien: Physica-Verlag.

    Google Scholar 

  • Coppi, R., & D’Urso, P. (2001). The geometric approach to the comparison of multivariate time trajectories. In S. Borra, R. Rocci, M. Vichi, & M. Schader (Eds.), Advances in data science and classification. Heidelberg, Germany: Springer.

    Google Scholar 

  • D’Urso, P. (2000). Dissimilarity measures for time trajectories. Journal of the Italian Statistical Society, 9, 53–83.

    Article  Google Scholar 

  • D’Urso, P., & Vichi, M. (1998). Dissimilarities between trajectories of a three-way longitudinal data set. In A. Rizzi, M. Vichi, & H. H. Bock (Eds.), Advances in data science and classification. Heidelberg, Germany: Springer.

    Google Scholar 

  • Kiers, H. (1989). Three way method for the analysis of qualitative and quantitive two way data. Leiden: DSWO Press

    Google Scholar 

  • Kiers, H. (1991). Hierarchical relations among three way methods. Psychometrika, 56, 449–470.

    Article  MathSciNet  MATH  Google Scholar 

  • Kroonenberg, P.M. (1993). Principal component analysis of three-mode data. Leiden: DSWO Press.

    Google Scholar 

  • Kroonenberg, P.M., & De Leeuw, J. (1980). Principal component analysis of three-mode data by means of alternating least squares algorithm. Psychometrika, 65, 69–97.

    Article  Google Scholar 

  • Oliver, R.L. (1977). Effect of expectation and disconfirmation on post-exposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 4, 480–486.

    Article  Google Scholar 

  • Rizzi, A., & Vichi, M. (1995). Representation, synthesis, variability and data preprocessing of three-way data set. Computational Statistics & Data Analysis, 19, 203–222.

    Article  MATH  Google Scholar 

  • Tucker, R.L. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31, 279–311.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DEMS, University of Milano-Bicocca, P.zza Ateneo Nuovo n.1, 20126, Milan, Italy

    Caterina Liberati

  2. DEMS, University of Milano-Bicocca, via Bicocca degli Arcimboldi, n.8, 20126, Milan, Italy

    Paolo Mariani

Authors
  1. Caterina Liberati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Mariani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caterina Liberati .

Editor information

Editors and Affiliations

  1. University of Essex Department of Mathematical Sciences, Colchester, United Kingdom

    Berthold Lausen

  2. Ghent University Department of Marketing, Ghent, Belgium

    Dirk Van den Poel

  3. University of Marburg Databionics, FB 12, Marburg, Germany

    Alfred Ultsch

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Liberati, C., Mariani, P. (2013). Dynamic Principal Component Analysis: A Banking Customer Satisfaction Evaluation. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_40

Download citation

Publish with us

Policies and ethics

Search

Navigation