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Advances in Data Analysis and Classification

Erschienen in:

01.06.2016 | Regular Article

Dynamic segmentation with growth mixture models

verfasst von: Francesca Bassi

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 2/2016

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Abstract

This paper proposes a new approach to dynamically segment markets. Dynamic segmentation i of key importance in many markets where it is unrealistic to assume stationary segments due to the dynamics in consumers’ needs and product choices. The main goal of the study is to analyse the dynamic process of financial product ownership under the assumption of heterogeneous growth in different segments taking into account significant determinants of growth trajectories. Using data from 2002 to 2010 collected by the Survey of Household Income and Wealth conducted by the Bank of Italy, this article shows that the Italian market of financial products is segmented and that this behavior’s trajectories over time are significantly influenced by the area of the country where the family lives and head of household’s education and gender.

Vorheriger Artikel Item selection by latent class-based methods: an application to nursing home evaluation

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With a time-varying covariate x, the model becomes \(y_{ijt} =\alpha _i +\gamma _t \beta _{1i} +x_{it} {\beta _{2i}} +\varepsilon _{ijt} \) and \(\beta _{2i} =\mu _{\beta _2 } +\xi _{\beta _{2i} } \).

Model estimation was performed with Latent Gold 5 Syntax Module Vermunt and Magidson (2013).

BIC = 93,474, df = 1720, npar = 114, Entropy-R\(^{2}\) = 0.9944. Due to the sparse and unbalanced nature of the contingency table, the classical statistics to evaluate model fit, X\(^{2 }\)and L\(^{2},\) do not have asymptotic \(\chi ^{2}\) distribution and therefore could not be used.

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Titel: Dynamic segmentation with growth mixture models
verfasst von: Francesca Bassi
Publikationsdatum: 01.06.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 2/2016
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-015-0230-x

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