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Bayesian two-part multilevel model for longitudinal media use data

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  • Shelley A. Blozis

    (University of California, Davis)

Abstract

Multilevel models are effective marketing analytic tools that can test for consumer differences in longitudinal data. A two-part multilevel model is a special case of a multilevel model developed for semi-continuous data, such as data that include a combination of zeros and continuous values. For repeated measures of media use data, a two-part multilevel model informs market research about consumer-specific likeliness to use media, level of use across time, and variation in use over time. These models are typically estimated using maximum likelihood. There are, however, tremendous advantages to using a Bayesian framework, including the ease at which the analyst can take into account information learned from previous investigations. This paper develops a Bayesian approach to estimating a two-part multilevel model and illustrates its use by applying the model to daily diary measures of television use in a large US sample.

Suggested Citation

  • Shelley A. Blozis, 2022. "Bayesian two-part multilevel model for longitudinal media use data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 311-328, December.
    • Handle: RePEc:pal:jmarka:v:10:y:2022:i:4:d:10.1057_s41270-022-00172-9
    DOI: 10.1057/s41270-022-00172-9
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    References listed on IDEAS

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    Cited by:

    1. Maria Petrescu & Anjala S. Krishen, 2023. "Mapping 2022 in Journal of Marketing Analytics: what lies ahead?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(1), pages 1-4, March.
    2. Carlos Lamela-Orcasitas & Jesús García-Madariaga, 2023. "How to really quantify the economic value of customer information in corporate databases," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.

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