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Modeling large data sets in marketing

Author

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  • S. Balasubramanian
  • S. Gupta
  • W. Kamakura
  • M. Wedel

Abstract

In the last two decades, marketing databases have grown significantly in terms of size and richness of available information. The analysis of these databases raises several information‐related and statistical issues. We aim at providing an overview of a selection of issues related to the analysis of large data sets. We focus on the two important areas: single source databases and customer transaction databases. We discuss models that have been used to describe customer behavior in these fields. Among the issues discussed are the development of parsimonious models, estimation methods, aggregation of data, data‐fusion and the optimization of customer‐level profit functions. We conclude that problems related to the analysis of large databases are far from resolved, and will stimulate new research avenues in the near future.

Suggested Citation

  • S. Balasubramanian & S. Gupta & W. Kamakura & M. Wedel, 1998. "Modeling large data sets in marketing," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(3), pages 303-323, November.
    • Handle: RePEc:bla:stanee:v:52:y:1998:i:3:p:303-323
    DOI: 10.1111/1467-9574.00086
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    Cited by:

    1. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    2. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    3. Nkurunziza, Alphonse & Zuidgeest, Mark & Brussel, Mark & Van Maarseveen, Martin, 2012. "Examining the potential for modal change: Motivators and barriers for bicycle commuting in Dar-es-Salaam," Transport Policy, Elsevier, vol. 24(C), pages 249-259.
    4. Sonja Radas & Drazen Prelec, 2019. "Whose data can we trust: How meta-predictions can be used to uncover credible respondents in survey data," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    5. Naik, Prasad A. & Tsai, Chih-Ling, 2004. "Isotonic single-index model for high-dimensional database marketing," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 775-790, November.

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