Challenges and opportunities in high-dimensional choice data analyses

Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables?×?Alternatives?×?Subjects?×?Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment.     

The interrelationships between brand and channel choice

We propose a framework for the joint study of the consumer’s decision of where to buy and what to buy. The framework is rooted in utility theory where the utility is for a particular channel/brand combination. The framework contains firm actions, the consumer search process, the choice process, and consumer learning. We develop research questions within each of these areas. We then discuss methodological issues pertaining to the use of experimentation and econometrics. Our framework suggests that brand and channel choices are closely intertwined, and therefore studying them jointly will reveal a deeper understanding of consumer decision making in the modern marketing environment.     

Multiple-Category Decision-Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior.     

Perspectives on Multiple Category Choice

Multiple category choice is a decision process in which an individualselects a number of goods, all of which are nonsubstitutable with respect toconsumption. Choices can be made either simultaneously or sequentially. Thekey feature of multiple category choice is the treatment of the choices asinterrelated because each item in the final collection of goods contributesto the achievement of a common behavioral goal. We discuss current andpotential applications of psychology, economics and consumer choice theoryin developing models of multiple category choice.