Statistics and Marketing

Statistical research in marketing is heavily influenced by the availability of different types of data. The last ten years have seen an explosion in the amount and variety of data available to market researchers. Demand data from scanning equipment has now become routinely available in the packaged goods industries. Data from e-commerce and direct marketing is growing at an exponential rate and provide coverage to a wide assortment of different products. Web-based technology has dramatically lowered the cost of survey research. Web-browsing data is an important new source of information about consumer tastes and preferences which is becoming available for a large fraction of the total consumer population. In this vignette, we explore some of the implications of this data explosion for the development of statistical methodology in marketing with primary emphasis on the explosion in demand data. Scanning equipment has provided the market researcher with a national panel of stores in addition to panels of households, altering the focus of marketing research. This data has stimulated a large literature on applied demand and discrete choice modeling. Demand models at the store level typically take the form of multivariate regression models in which demand for a vector of products is related to marketing variables such as prices, displays and various forms of advertising. At the household level, demand is discrete and a wide variety of multinomial logit and probit models have been applied to the data. Early experience with scanner data revealed that households have very different patterns of buying behavior that cannot be explained just by differences in the marketing environment. Some households, for example, exhibit strong brand loyalties while other households readily switch brands when prices are lowered. Even at the store level, large differences have been detected in price and local advertising sensitivity. Initial observations of store and consumer heterogeneity created considerable interest in models of observed and unobservable heterogeneity, primarily of the random effects form. The development and application of random effect models in marketing has been dictated in large degree by the available inference technology. The first paper in this area by Kamarkura and Russell (1989) used a finite mixture model of heterogeneity in a logit framework. Kamarkura and Russell postulate a discrete