Abstract
The most important company asset seems to be Customer Satisfaction (CS), which banks, in the recent years, have frequently analyzed. For reaching such target, a dynamic Factor Analysis offers an effective way of merging information about clients and their preferences evolution. In our work we performed a dynamic Customer Satisfaction study, by means of a three-way factorial analysis, and we also introduced a new index of shift and shape (SSI), to synthesize information about every customer, cluster or typology. We considered a national bank case, with spread network, evaluating results provided by a questionnaire framed according to the SERVQUAL model. The information employed was obtained via a Customer Satisfaction survey repeated three times (waves). We performed the dynamic factorial model and we illustrated the usage of SSI as a new measure of trajectories’ dissimilarity. Finally, we showed our results which highlight promising performances of our index.
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Notes
- 1.
As Kroonenberg (2007) highlighted, a three-way array can be seen as composed of two-mode sub-matrices called slices, and of one-mode sub-matrices (or vectors), called fibers. These two-way sub-matrices will be referred to as frontal slices, horizontal slices, and lateral slices. In our case we composed the X (tall matrix) using the horizontal juxtaposition of the slices. We analyzed such X via a simple PCA which is a no weighted version of the STATIS method (Escoufier 1980).
- 2.
Currently, in literature, two different ways to model a Likert scale are present: some refer to it as quantitative scale some as ordinal one. Based on Zani and Berzieri (2008) work we considered our data cardinal because it does not produce a substantial bias, therefore the 10-categories could be approximated to a 10 score
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Liberati, C., Mariani, P. (2014). Dynamic Customer Satisfaction and Measure of Trajectories: A Banking Case. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_20
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