A customized market response model: development, estimation, and empirical testing

Article Abstract:

A customized, stepwise, log-linear, distributed lag, restricted market response model is proposed to estimate the effects of various elements of promotion expenditures on sales in the presence of potentially significant effects due to trend and-or seasonality when using time-series data. As distinct from standardized software packages, the customization offers management several benefits: (a) an (optional) imposition of prior restrictions on the directions of the coefficient variables; (b) an empirical determination of the lag structure for selected variables; (c) the detrending of the data to allow for the assessment of incremental marketing mix effects above trend; and (d) a simplified sensitivity analysis. The model is empirically tested and validated using sales data for a brand where the impact of several marketing mix variables is estimated and investigated via policy simulations. A comparison of these results with those obtained from a corresponding unrestricted model illustrates the advantages of this approach. Finally, the limitations of this procedure and directions for future research are discussed. (Reprinted by permission of the publisher.)

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Estimating individual cross-section coefficients from the random coefficient regression model

Article Abstract:

Marketing researchers frequently encounter cross-sectional, time-series data when developing sales response models. One approach to analyzing such data is to estimate a separate OLS equation for each cross-section. Alternatively, one could pool the data from all cross-sections to estimate a single set of response coefficients for all cross-sections. However, when data are pooled, the responsiveness of individual cross-sections cannot be evaluated. In this note, we introduce a version of the random coefficient model that can be used to estimate separate sets of response coefficients for each cross-section, thereby circumventing the assumption that coefficients are homogenous in all cross-sections. We demonstrate this approach with an empirical model that relates brand level sales to price and advertising. (Reprinted by permission of the publisher.)

Regression analysis

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Incorporating context effects in the multidimensional scaling of 'pick any/N' choice data

Article Abstract:

Customers' choice of brand products may be affected by their choice of shopping establishments, as well as their familiarity with the particular establishment. This finding was shown in a multidimensional scaling model for determining consumer behavior that incorporates context effects. The model discounted the expected substitution effects through the inclusion of an hierarchical brand structure. The findings have potential applications for formulating market strategies.

Supermarkets and Other Grocery (except Convenience) Stores, Marketing, Grocery stores, Supermarkets, Consumer Behavior, Research, Brand choice, Multidimensional scaling

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