A Model for Inferring Market Preferences from Online Retail Product Information Matrices

Timothy J. Gilbride*, Imran S. Currim, Ofer Mintz, S. Siddarth

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions.

Original languageEnglish
Pages (from-to)470-485
Number of pages16
JournalJournal of Retailing
Volume92
Issue number4
DOIs
StatePublished - 1 Dec 2016
Externally publishedYes

Keywords

  • Choice models
  • E-commerce
  • Expected utility
  • Information processing
  • Sequential search

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