IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v10y2016i2d10.1007_s11634-015-0230-x.html
   My bibliography  Save this article

Dynamic segmentation with growth mixture models

Author

Listed:
  • Francesca Bassi

    (University of Padua)

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.

Suggested Citation

  • Francesca Bassi, 2016. "Dynamic segmentation with growth mixture models," 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. 10(2), pages 263-279, June.
    • Handle: RePEc:spr:advdac:v:10:y:2016:i:2:d:10.1007_s11634-015-0230-x
    DOI: 10.1007/s11634-015-0230-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-015-0230-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-015-0230-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Francesca Bassi, 2013. "Analysing markets within the latent class approach: an application to the pharma sector," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 29(3), pages 199-207, May.
    3. Francesco Bartolucci & Silvia Bacci & Fulvia Pennoni, 2014. "Longitudinal analysis of self-reported health status by mixture latent auto-regressive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 267-288, February.
    4. Green, Paul E & Carmone, Frank J & Wachspress, David P, 1976. "Consumer Segmentation via Latent Class Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 3(3), pages 170-174, December.
    5. M. Salgueiro & Peter Smith & Marcel Vieira, 2013. "A multi-process second-order latent growth curve model for subjective well-being," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 735-752, February.
    6. Konuş, Umut & Verhoef, Peter C. & Neslin, Scott A., 2008. "Multichannel Shopper Segments and Their Covariates," Journal of Retailing, Elsevier, vol. 84(4), pages 398-413.
    7. Francesca Bassi, 2007. "Latent class factor models for market segmentation: an application to pharmaceuticals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(2), pages 279-287, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Lucas Lopes Ferreira de Souza & Francesca Bassi & Ana Augusta Ferreira de Freitas, 2021. "Longitudinal analysis of microfinance borrowers in Brazil: A dynamic market segmentation," Journal of International Development, John Wiley & Sons, Ltd., vol. 33(6), pages 1063-1083, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesca Bassi & Fulvia Pennoni & Luca Rossetto, 2020. "The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 542-567, October.
    2. Pallant, Jason I. & Pallant, Jessica L. & Sands, Sean J. & Ferraro, Carla R. & Afifi, Eslam, 2022. "When and how consumers are willing to exchange data with retailers: An exploratory segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    3. Kondo, Fumiyo N. & Okubo, Taishi, 2022. "Understanding multi-channel consumer behavior: A comparison between segmentations of multi-channel purchases by product category and overall products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    4. Pallant, Jason I. & Danaher, Peter J. & Sands, Sean J. & Danaher, Tracey S., 2017. "An empirical analysis of factors that influence retail website visit types," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 62-70.
    5. Ken Deal, 2014. "Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 7(1), pages 5-21, March.
    6. Blömker, Jan & Albrecht, Carmen-Maria, 2024. "Psychographic segmentation of multichannel customers: investigating the influence of individual differences on channel choice and switching behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    7. Tueanrat, Yanika & Papagiannidis, Savvas & Alamanos, Eleftherios, 2021. "A conceptual framework of the antecedents of customer journey satisfaction in omnichannel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    8. Nakano, Satoshi & Kondo, Fumiyo N., 2018. "Customer segmentation with purchase channels and media touchpoints using single source panel data," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 142-152.
    9. Frasquet, Marta & Ieva, Marco & Ziliani, Cristina, 2019. "Understanding complaint channel usage in multichannel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 94-103.
    10. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," 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. 16(2), pages 273-300, June.
    11. Neslin, Scott A., 2022. "The omnichannel continuum: Integrating online and offline channels along the customer journey," Journal of Retailing, Elsevier, vol. 98(1), pages 111-132.
    12. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," 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. 10(2), pages 209-224, June.
    13. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    14. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," 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. 12(2), pages 235-260, June.
    15. William H. Greene & Mark N. Harris & Rachel J. Knott & Nigel Rice, 2021. "Specification and testing of hierarchical ordered response models with anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 31-64, January.
    16. Salmani, Yasamin & Partovi, Fariborz Y., 2021. "Channel-level resource allocation decision in multichannel retailing: A U.S. multichannel company application," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    17. Silvia Bianconcini, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 466-468, September.
    18. Thøgersen, John, 2017. "Housing-related lifestyle and energy saving: A multi-level approach," Energy Policy, Elsevier, vol. 102(C), pages 73-87.
    19. Gil, J.M. & Diaz-Montenegro, J. & Varela, E., 2018. "A Bias-Adjusted Three-Step approach for analysing the livelihood strategies and the asset mix of cacao producers in Ecuador," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277215, International Association of Agricultural Economists.
    20. Marcin Gąsior, 2021. "Environmental Attitudes and Willingness to Purchase Online—Classification Approach," Sustainability, MDPI, vol. 13(15), pages 1-17, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:10:y:2016:i:2:d:10.1007_s11634-015-0230-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.