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

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.

Energy efficiency of consideration sets and choices: the impact of label format

“…More specifically, they made choices from product sets of refrigerators, TVs, washing machines and light bulbs (product sets were presented in random order). The product alternatives were described on 12–14 relevant attributes in a comparison matrix (Gilbride et al , 2016; Häubl and Trifts, 2000; see Figure 3). To have full control over the information that was presented to participants in the final choice task, we separated the two stages in different experiments with separate participant samples.…”

“…Only after the consumer clicks on a product or selects products for a more detailed comparison, more detailed product information is provided. At that stage, consumers are thus more likely to be exposed to relatively few products that are described on relatively many attributes (Gilbride et al , 2016).…”

Validation of the information processing theory of consumer choice: evidence from travel search engine clickstream data

“…We keep our observations brief and point the reader to articles cited herein for more detailed reviews, primarily Chen and Yao (2016), Goldstein et al (2020) and Gilbride et al (2016). These and other relevant studies are summarized in Table 1 in an effort to differentiate our study; the columns in the table highlight the context, research questions, dependent and independent variables, data, key results and whether the study explicitly includes any model to test the two-stage information processing theory of consumer choice.…”

“…They find that information accessed is more useful than information available to predict purchase, indicating that it is the actual processing of information that influences decisions. Gilbride et al (2016) also use a multinomial logit model to successfully model product-attribute item-by-item information accessed by consumers to predict their purchase.…”

“…The explosive growth in online shopping, projected to surpass $6tn worldwide by 2024 (Statista, 2021a), is accompanied by intense research interest in online shopping behavior, in general (Statista, 2021b), and purchase at product search engines, in particular (Kim and Kim, 2004; Choudhary et al , 2017; Hollebeek et al , 2018; Shen et al , 2018; Bhatnagar et al , 2019). An emerging relatively new area of research, within this larger literature, is examining theoretical and managerial aspects of consumers’ search and purchase decisions through the lens of the revered information processing theory of consumer choice (Payne, 1976; Bettman, 1979; Affonso et al , 2021), most recently in online point-of-purchase settings (Mintz et al , 2013; Currim et al , 2015; Gilbride et al , 2016).…”