Unfolding large-scale marketing data

Ying HO, Yuho CHUNG, Kin-nam LAU

Research output: Journal PublicationsJournal Article (refereed)

7 Citations (Scopus)

Abstract

Marketers use multidimensional unfolding to understand the relationship between customer preferences and product positioning through a joint display of customers and brands on a map. In today's information age, unfolding marketing data is challenging, as marketing data can be large in size and high-dimensional in nature. Moreover, the unfolding model is always subject to the curse of the degeneracy problem. We propose a new approach to unfold customer-by-brand transaction data and customer-by-customer network data in a reduced space. The proposed approach can recover the true configuration with reasonable accuracy, is scalable in terms of the number of estimated parameters, and can produce non-degenerate solution. We compare its performance with existing approaches by simulation experiments and real data analyses with interesting results.

Original languageEnglish
Pages (from-to)119-132
Number of pages14
JournalInternational Journal of Research in Marketing
Volume27
Issue number2
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

Keywords

  • Degenerate solution
  • Information visualization
  • Multidimensional unfolding
  • Network data

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