The PIMS (Profit Impact of Marketing Strategies) data entail sparse time-series observations for a large number of strategic business units (SBUs), In order to estimate disaggregate marketing mix elasticities of demand, a natural solution is to pool different SBUs. The traditional, a priori approach is to pool together those SBUs which one believes in advance to be very similar with respect to their marketing mix elasticities. We propose an alternative maximum likelihood, latent-pooling method for simultaneously pooling, estimating, and testing linear regression models . This method enables the determination of a “fuzzy” pooling scheme, while directly estimating a set of marketing mix elasticities and intertemporal covariances for each pool of SBUs. Our analyses reveal different magnitudes and patterns of marketing mix elasticities for the derived pools. Pool membership is influenced by demand characteristics, business scope, and order of market entry.econometric models, regression and other statistical techniques, marketing mix, competitive strategy

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Many businesses on the Internet in the late 1990s spent wildly, doing whatever it might take to attract customers to their sites. It soon became clear that the challenge was not simply to bring the customers in the door but also to retain these customers for future purchases. The quest was on to discover what tactics had the most appeal to Internet shoppers. This study reveals survey and behavioral data drawn from Internet customers that reflect what was most important to the Internet shoppers and compare the factors for attraction versus retention. Since many have viewed the Internet as creating more perfect information for the buyer, the question arises as to how important price will be in the purchase process. What becomes clear from the analysis is that what attracts customers to the site are not the same dimensions critical in retaining customers on a longer term basis.

This paper addresses the question of how the vertical structure of a product line relates to brand equity. Does the presence of “premium” or high-quality products in a product line enhance brand equity? Conversely, does the presence of “economy” or low-quality products in a product line diminish brand equity? Economists and marketing researchers refer to variation in quality levels of products within a category as “vertical” differentiation, whereas variation in the function or “category” of the products is referred to as “horizontal” differentiation. Much of the existing research on the relationship between product line structure and brand equity has focused on the horizontal structure of the product line and has been primarily concerned with —what happens when the product line of a brand is extended horizontally into new categories? Researchers have been concerned primarily with how the extension fares, but the effect of the extension on the products is also important. There is an analogous question of what happens when the product line of a brand is extended vertically, either “up market” or “down market.” This question of vertical extensions is part of the more general issue of how the vertical structure of a product line relates to brand equity. The specific research questions addressed in this paper are: (1) do “premium” or high-quality products enhance the brand equity associated with the other products in the line? (2) Conversely, do “economy” or low-quality products diminish the brand equity associated with the other products in the line? These research questions are relevant to three managerial issues in product-line strategy. First, what are the costs and benefits of including “down market” products within a brand? Second, what are the implications of including high-end models within a brand? Third, when should high-end and low-end products be offered under an existing brand umbrella and when should these products be offered under separate brands? We address these research questions empirically through an analysis of the models and brands within the U.S. mountain bicycle industry. We use price premium above that which can be explained by the physical characteristics of the bicycle as a metric for brand equity. We then test several hypotheses related to the relationship between extension of the product line upward and downward and the price premium commanded by the brand. We further support this analysis with a simple laboratory experiment. The analysis reveals that price premium, in the lower quality segments of the market, is significantly positively correlated with the quality of the lowest-quality model in the brand's product line; and, that for the upper quality segments of the market, price premium is also significantly positively correlated with the quality of the highest-quality model in the brand's product line. The results of the analysis are supported by the outcome of an experiment in which 63 percent of the subjects preferred a product offered by a high-end brand to the equivalent product o...

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