Timothy J. Gilbride

Models for Heterogeneous Variable Selection

Preference heterogeneity is a major research stream in marketing aimed at quantifying and understanding the diversity of demand for product attributes and attribute levels. In experimental settings, in which consumers are presented with simple descriptions of product offerings, continuous distributions of heterogeneity, such as the multivariate normal, provide a useful representation of preference. However, in more complex cases in which respondents have value for only a few of the benefits associated with an offering or cognitive constraints that result in selective attention to a subset of the information available, continuous distributions of heterogeneity do not reflect the possibility that a subset of the variables has nonzero effect sizes for different respondents. Identifying which attributes are used in a brand choice decision is closely related to the statistical procedure of variable selection. This article extends variable selection methods to accommodate heterogeneity across consumers and data contexts, conditions frequently encountered in marketing studies. The authors apply the methods to a discrete-choice conjoint study in which data are collected in both full-profile and partial-profile formats.

The Role of Within-Trip Dynamics in Unplanned Versus Planned Purchase Behavior

The recent surge in the importance of shopper marketing has led to an increased need to understand the drivers of unplanned purchases. The authors address this issue by examining how elements of the current shopping trip (e.g., lagged unplanned purchase, cumulative purchases) and previous shopping trips (e.g., average historical price paid by the shopper) determine unplanned versus planned purchases on the current trip. Using a grocery field study and frequent-shopper-program data, the authors estimate competing models to test behavioral hypotheses using a hierarchical Bayesian probit model with state dependence and serially correlated errors. The results indicate that shoppers with smaller trip budgets tend to exhibit behavior consistent with a self-regulation model (i.e., an unplanned purchase decreases the probability of a subsequent unplanned vs. planned purchase), but this effect reverses later in the trip. In contrast, shoppers with medium-sized trip budgets tend to exhibit behavior consistent with a cuing theory model (i.e., an unplanned purchase increases the probability of a subsequent unplanned vs. planned purchase), and this effect increases as the trip continues. The article concludes with a discussion of implications for research and practice.

Posterior Predictive Model Checking: An Application to Multivariate Normal Heterogeneity

Analysts fitting a hierarchical Bayesian model must specify the distribution of heterogeneity. There are several distributions to choose from, including the multivariate normal, mixture of normals, Dirichlet processes priors, and so forth. Although significant progress has been made, estimating the models and obtaining measures for model selection remain ongoing areas of research for more flexible distributions of heterogeneity. As a result, the multivariate normal remains the default choice for many researchers and software packages. This article proposes model-checking statistics that signal the adequacy of the multivariate normal assumption for the distribution of heterogeneity; these methods do not require the analyst to fit alternative models. The authors use posterior predictive model checking to determine whether a discrepancy exists between the individual-level parameters and those implied by the assumed distribution of heterogeneity. In simulated and real data sets, the results show that these statistics are useful for identifying when the multivariate normal distribution is adequate, when there is a departure in the tails of the distribution, and when a multimodal distribution of heterogeneity may be more appropriate.

From Browsing to Buying and Beyond : The Needs-Adaptive Shopper Journey Model

We propose a theory-based model of the shopper journey, incorporating the rich literature in consumer and marketing research and taking into account the evolving retailing landscape characterized by significant knowledge, lifestyle, technological, and structural changes. With consumer well-being at its core and shopper needs and motivations as the focus, our needs-adaptive shopper journey model complements and contrasts with existing models. In addition, we identify 12 shopper journey archetypes representing the paths that consumers commonly follow—archetypes that illustrate the workings and applications of our model. We discuss the nature of these archetypes, their relationships with one another, and the psychological states that consumers may experience on these shopper journeys. We also present exploratory empirical studies assessing the component states in the archetypes and mapping the archetypes onto dimensions of shopping motivations. Finally, we lay out a research agenda to help increase understanding of shopper behavior in the evolving retailing landscape.

The Role of Within-Trip Dynamics and Shopper Purchase History on Unplanned Purchase Behavior

The recent surge in the importance of shopper marketing has led to an increased need to understand the drivers of unplanned purchases. This research addresses this issue by examining how elements of the current shopping trip (e.g., lagged unplanned purchase and cumulative purchases) and past purchases (e.g., average historical price paid by the shopper) determine unplanned versus planned purchases on the current trip. Using a grocery field study and frequent shopper data, we estimate competing models to test behavioral hypotheses using a hierarchical-Bayesian probit model with state dependence and serially correlated errors. Our results indicate that shoppers with smaller trip budgets tend to exhibit behavior consistent with a self-regulation model – an unplanned purchase decreases the probability of a subsequent unplanned purchase – but this effect reverses later in the trip. In contrast, shoppers with medium trip budgets tend to exhibit behavior consistent with a cueing theory model – an unplanned purchase increases the probability of a subsequent unplanned purchase – and this effect increases as the trip wears on. Further, factors from previous shopping trips predict unplanned purchases in the current trip, suggesting that retailers can use their frequent shopper program data to create customized shopping lists and improve the targeting of mobile app-based promotions.