Greg M. Allenby

Marketing models of consumer heterogeneity

The distribution of consumer preferences plays a central role in many marketing activities. Pricing and product design decisions, for example, are based on an understanding of the differences among consumers in price sensitivity and valuation of product attributes. In addition, marketing activities which target specific households require household level parameter estimates. Thus, the modeling of consumer heterogeneity is the central focus of many statistical marketing applications. In contrast, heterogeneity is often regarded as an ancillary nuisance problem in much of the applied econometrics literature which must be dealt with but is not the focus of the investigation. The focus is instead on estimating average effects of policy variables. In this paper, we discuss various approaches to modeling consumer heterogeneity and evaluate the utility of these approaches for marketing applications.

Modeling Interdependent Consumer Preferences

A consumers preference for an offering can be influenced by the preferences of others in many ways, ranging from social identification and inclusion to the benefits of network externalities. In this article, the authors introduce a Bayesian spatial autoregressive discrete-choice model to study the preference interdependence among individual consumers. The autoregressive specification can reflect patterns of heterogeneity in which influence propagates within and across networks. These patterns cannot be modeled with standard random-effect specifications and can be difficult to capture with covariates in a linear model. The authors illustrate their model of interdependent preferences with data on automobile purchases and show that preferences for Japanese-made cars are related to geographically and demographically defined networks.

Modeling Household Purchase Behavior with Logistic Normal Regression

The successful development of marketing strategies requires the accurate measurement of household preferences and their reaction to variables such as price and advertising. Manufacturers, for examp...

Overcoming Scale Usage Heterogeneity: A Bayesian Hierarchical Approach

Questions that use a discrete ratings scale are commonplace in survey research. Examples in marketing include customer satisfaction measurement and purchase intention. Survey research practitioners have long commented that respondents vary in their usage of the scale: Common patterns include using only the middle of the scale or using the upper or lower end. These differences in scale usage can impart biases to correlation and regression analyses. To capture scale usage differences, we developed a new model with individual scale and location effects and a discrete outcome variable. We model the joint distribution of all ratings scale responses rather than specific univariate conditional distributions as in the ordinal probit model. We apply our model to a customer satisfaction survey and show that the correlation inferences are much different once proper adjustments are made for the discreteness of the data and scale usage. We also show that our adjusted or latent ratings scale is more closely related to actual purchase behavior.

Multivariate Analysis of Multiple Response Data

Multiple response questions, also known as a pick any/J format, are frequently encountered in the analysis of survey data. The relationship among the responses is difficult to explore when the number of response options, J, is large. The authors propose a multivariate binomial probit model for analyzing multiple response data and use standard multivariate analysis techniques to conduct exploratory analysis on the latent multivariate normal distribution. A challenge of estimating the probit model is addressing identifying restrictions that lead to the covariance matrix specified with unit-diagonal elements (i.e., a correlation matrix). The authors propose a general approach to handling identifying restrictions and develop specific algorithms for the multivariate binomial probit model. The estimation algorithm is efficient and can easily accommodate many response options that are frequently encountered in the analysis of marketing data. The authors illustrate multivariate analysis of multiple response data in three applications.

A Dynamic Model of Purchase Timing with Application to Direct Marketing

Abstract Predicting changes in individual customer behavior is an important element for success in any direct marketing activity. In this article we develop a hierarchical Bayes model of customer interpurchase times based on the generalized gamma distribution. The model allows for both cross-sectional and temporal heterogeneity, with the latter introduced through the component mixture model dependent on lagged covariates. The model is applied to personal investment data to predict when and if a specific customer will likely increase time between purchases. This prediction can be used managerially as a signal for the firm to use some type of intervention to keep that customer.

Bayesian Analysis of Simultaneous Demand and Supply

In models of demand and supply, consumer price sensitivity affects both the sales of a good through price, and the price that is set by producers and retailers. The relationship between the dependent variables (e.g., demand and price) and the common parameters (e.g., price sensitivity) is typically non-linear, especially when heterogeneity is present. In this paper, we develop a Bayesian method to address the computational challenge of estimating simultaneous demand and supply models that can be applied to both the analysis of household panel data and aggregated demand data. The method is developed within the context of a heterogeneous discrete choice model coupled with pricing equations derived from either specific competitive structures, or linear equations of the kind used in instrumental variable estimation, and applied to a scanner panel dataset of light beer purchases. Our analysis indicates that incorporating heterogeneity into the demand model all but eliminates the bias in the price parameter due to the endogeneity of price. The analysis also supports the use of a full information analysis.

The effects of in-store displays and feature advertising on consideration sets

Abstract A heteroscedastic random utility model which allows for a flexible pattern of cross elasticities at the household level is explored. This flexibility enables the model to describe patterns of price sensitivity among competing brands which correspond to the competitive structure reflected in consideration sets. The effects of displays and features on these price sensitivities and the consideration sets are examined. The model is applied to scanner panel data of tuna purchases, where in-store displays and feature advertisements are found to increase product net utility and decrease price sensitivity for the promoted item.

Reassessing Brand Loyalty, Price Sensitivity, and Merchandising Effects on Consumer Brand Choice

This article reports the results of an empirical study of household brand choices over four scanner-panel data sets. The study uses a random-effects, autocorrelated, logistic regression model. The analysis presents evidence that the brand-choice process is not zero-order. In addition, the influence of in-store displays and feature advertisements on switching is shown to be about two to three times more effective than estimates obtained from previous studies. Finally, the analysis indicates systematic differences between frequent and infrequent buyers of products in terms of their sensitivity to price, displays, and feature advertising.

The Effectiveness of Demographic and Psychographic Variables for Explaining Brand and Product Category Use

The predictive relationship of a large and comprehensive set of personal descriptors to aspects of product and brand use is examined. The descriptors comprise demographic and general psychographic variables frequently used in segmentation studies and studies of consumer purchase behavior. The evidence is overwhelming that the covariates are related to brand use in an identical way for all brands, indicating that they are not useful for predicting relative brand preference. The covariates are shown to be predictive of product use. Discussion of the explanatory content of the variables is offered.