Naufel J. Vilcassim

A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data

A random-coefficients logit model that allows for unobserved heterogeneity in brand preferences and in the responses to marketing variables is empirically investigated using household-level panel data. The unknown underlying distribution of unobserved heterogeneity is approximated by a discrete distribution. The results reveal that there is significant unobserved heterogeneity across households and that ignoring its effects results in a downward bias in the parameter estimates of the marketing variables. It is therefore important to account for heterogeneity in both preferences and responses in the absence of any a priori knowledge about the nature of heterogeneity across households.

Investigating retailer product category pricing from household scanner panel data

Abstract A growing body of industrial organization literature has directed attention to using the household level logit model, aggregated to the firm level, as the firms demand function in order to study markets with differentiated products. Such models, however, have ignored the role played by the channel intermediaries, i.e., the retailer, in setting retail prices. In this paper, we study optimal retailer product category pricing policies where the retailers objective is to maximize total category profits, and show how household scanner panel data can be used to determine these optimal prices. The empirical results obtained from analyzing the AC Nielsen yogurt data base reveals that the retailers category profit maximizing markups are brand specific and simplified pricing rules such as an across-the-board constant markup can be suboptimal. We also find that there are significant interactions between the optimal prices and the retailers decision to promote brands using feature advertisements. Additionally, we find that promoting different brands of yogurt are not equally attractive, and the retailer can be better off negotiating for different types of trade promotions (marginal cost reduction vs. payment of fixed sum) with the different manufacturers. Finally, by comparing these optimal retailer pricing rules to those obtained based on aggregate store-level sales data, we show that for purposes of normative analyses, using the household-level logit model aggregated to the brand level is theoretically and/or empirically superior to certain familiar aggregate specifications such as the constant-elasticity and linear demand models.

Structural Demand Estimation with Varying Product Availability

This paper develops a model that extends the traditional aggregate discrete-choice-based demand model (e.g. Berry et al. 1995) to account for varying levels of product availability. In cases where not all products are available at every consumer shopping trip, the observed market share is a convolution of two factors: consumer preferences and the availability of the product in stores. Failing to account for the varying degree of availability would produce incorrect estimates of the demand parameters. The proposed model uses information on aggregate availability to simulate the potential assortments that consumers may face in a given shopping trip. The model parameters are estimated by simulating potential product assortment vectors by drawing multivariate Bernoulli vectors consistent with the observed aggregate level of availability. The model is applied to the UK chocolate confectionery market, focusing on the convenience store channel. We compare the parameter estimates to those obtained from not accounting for varying availability and analyze some of the substantive implications.

Structural Modeling and Policy Simulation

A primary goal of research in marketing is to evaluate and recommend optimal policies for marketing actions, or “instruments” in the terminology of Franses (2005). In this respect, marketing is a very policy-oriented field, and it is ironic that so much published research skirts the issue of policy evaluation. Franses’s article draws much needed attention to the question of what sort of model is usable for policy simulation and evaluation. Our perspective on what constitutes a valid model for policy evaluation differs from Franses’s view, but we believe our view complements his in many important respects. We also strongly believe that marketing has much to contribute to the literature on structural modeling. We outline some of what we believe are the advantages for marketing scholars of using structural modeling for policy evaluations and some of the challenges presented by marketing problems. Franses focuses on a reduced-form sales response model in which the outcome variable (yt) is modeled conditional on marketing variables (xt). If customers anticipate future marketing actions and take these into account in responding to the environment at time t, an additional equation is appended to the system to describe the evolution of the xt variables. In Franses’s view, this system can be used for policy simulation if both the y and x equations have timeinvariant parameters. That is, the Lucas critique, which implies that parameters of reduced-form models change if the policy regime changes, does not apply. According to Franses, a model must pass standard diagnostics, possess good predictive properties, and exhibit parameter stability to be useful for policy simulation. We applaud the attention Franses is bringing to model diagnostics. We believe that structural work in both marketing and economics should pay close attention to the central features of the data. Increased use of model diagnostics will help ensure that structural models are capable of capturing these features. However, we do not believe that all the criteria proposed by Franses, such as out-of-sample validity and parameter stability, are either necessary or sufficient to render a model useful for policy simulation. Reduced-form models can pass all diagnostics, including out-of-sample validation, and still provide misleading predictions about the effects of policy changes. Reduced-form

When Talk is 'Free': The Effect of Tariff Structure on Usage under Two- and Three-Part Tariffs

In many service industries, firms introduce three-part tariffs to replace or complement existing two-part tariffs. In contrast with two-part tariffs, three-part tariffs offer allowances, or “free” units of the service. Behavioral research suggests that the attributes of a pricing plan may affect behavior beyond their direct cost implications. Evidence suggests that customers value free units above and beyond what might be expected from the change in their budget constraint. Nonlinear pricing research, however, has not considered such an effect. The authors examine a market in which three-part tariffs were introduced for the first time. They analyze tariff choice and usage behavior for customers who switch from two-part to three-part tariffs. The findings show that switchers significantly “overuse” in comparison with their prior two-part tariff usage. That is, they attain a level of consumption that cannot be explained by a shift in the budget constraint. The authors estimate a discrete/continuous model of tariff choice and usage that accounts for the valuation of free units. The results show that the majority of three-part-tariff users value minutes under a three-part tariff more than they do under a two-part tariff. The authors derive recommendations for how the provider can exploit these insights to further increase revenues.

Marketing investment decisions in a dynamic duopoly: A model and empirical analysis

Determining the levels of profit-maximizing investments in marketing mix activities such as advertising, detailing and sales promotion in a dynamic competitive environment is an important, but difficult managerial task. In this paper, we model the marketing rivalry over time between two competing firms as a differential game and derive expressions for the equilibrium investment decisions using closed-loop policies. We apply the analytical results derived from our model to examine empirically the dynamic advertising and detailing policies of two competing brands of a prescription drug. Our empirical results show that each firm takes into consideration the actions of its rival when determining its advertising and detailing expenditures. We also find that a firm could be misallocating resources between the different marketing mix instruments, even though the actual total expenditures may be close to the equilibrium levels. This suggests that when firms use multiple marketing mix investments as competitive tools, it could be misleading to model them as a single promotional variable.

Endogeneity and Simultaneity in Competitive Pricing and Advertising: A Logit Demand Analysis*

In this article, we use four data sets to provide a benchmark study of the effects of accounting for endogeneity and simultaneity in estimating marketing-mix effects in a logit demand framework. We compare the results obtained from accounting for endogeneity only to those from accounting for both endogeneity and simultaneity, and in the latter case we allow for more general models of firm behavior to examine the consequences of imposing assumptions about the behavior of firms. We find that accounting for both endogeneity and simultaneity not only affects the parameter estimates but also results in efficiency gains that affect the statistical significance of the estimates.

Do Promotions Increase Store Expenditures? A Descriptive Study of Household Shopping Behavior

An important question for retailers is whether promotions induce households to increase their in-store expenditures or merely reallocate a predetermined shopping budget. Should expenditures be fixed, retailers might decrease their profitability when running promotions by displacing expenditures from high margin to lower margin products. Using household level store receipts and an extended AIDS model, we provide evidence that while household expenditures do increase with promotions, there is also a significant reallocation of expenditures among the different categories. This implies that retailers have to choose carefully which products are promoted, if promotions are to increase profits.

Testing functional forms of market share models using the Box-Cox transformation and the Lagrange multiplier approach

Abstract The most commonly used functional forms in measuring market response functions are the linear and log-linear (double-log) specifications. Although the two models are mutually non-nested, they are both nested within the class of Box-Cox regression models. This enables one to test the statistical validity of these two models using nested tests, the power characteristics of which are better established relative to non-nested hypotheses tests, at least in large samples. In this paper, an application of the Lagrange multiplier (LM) test to determine the validity of linear, log-linear, and attraction-type formulations of market share models is illustrated using marketing data. The test is easy to compute and involves running only one extra linear regression. A Monte Carlo simulation is performed to study the properties of the test for samples of varying size and different levels of error variance. The simulation results indicate that the LM test should not be used with samples of less than 100 observations. We also compare the performance of the LM test to that of the PE test developed by MacKinnon, White, and Davidson for non-nested models. The results show that the PE test has a lower probability of a type 1 error for all sample sizes and different error levels. The power of the LM test, however, is greater when the error variance of the true model is high, given a fixed sample size.

Empirical implications of unobserved household heterogeneity for manufacturer and retailer pricing

This paper investigates manufacturer and retailer pricing decisions for brands within a given product category. We use household-level data and a nested logit model of purchase incidence and brand choice to specify and estimate the demand functions for brands. Our focus is on studying the effect of unobserved household heterogeneity on brand prices. The channel structure assumed is that of several competing manufacturers selling through a single retailer. Manufacturers set wholesale prices to maximize brand profits and the retailer sets selling prices for brands that maximize category profits. Empirical results indicate that accounting for heterogeneity directly influences the level of prices charged by the manufacturers and the retailer.