Few studies have considered the relative role of the integrated marketing mix (advertising, price promotion, product, and place) on the long-term performance of mature brands, instead emphasizing advertising and price promotion. Thus, little guidance is available to firms regarding the relative efficacy of their various marketing expenditures over the long run. To investigate this issue, the authors apply a multivariate dynamic linear transfer function model to five years of advertising and scanner data for 25 product categories and 70 brands in France. The findings indicate that the total (short-term plus long-term) sales elasticity is 1.37 for product and .74 for distribution. Conversely, the total elasticities for advertising and discounting are only .13 and .04, respectively. This result stands in marked contrast to the previous emphasis in the literature on price promotions and advertising. The authors further find that the long-term effects of discounting are one-third the magnitude of the short-term effects. The ratio is reversed from other aspects of the mix (in which long-term effects exceed four times the short-term effects), underscoring the strategic role of these tools in brand sales.

Which marketing strategies are most effective for introducing new brands? This paper sheds light on this question by ascribing growth performance to firms' postlaunch marketing choices. We decompose the success of a new brand into its ultimate market potential and the rate at which it achieves this potential. To achieve this aim we formulate a Bayesian dynamic linear model (DLM) of repeat purchase diffusion wherein growth and market potential are directly linked to the new brand's long-term advertising, promotion, distribution, and product strategy. We perform the analysis on 225 new-brand introductions across 22 repeat-purchase product categories over five years to develop generalized findings about the correlates of new-brand success. We find that access to distribution breadth plays the greatest role in the success of a new brand, and that investments in distribution and product innovation lead to greater marginal increases in sales for new brands than either discounting, feature/display, or advertising. Moreover, distribution interacts with other strategies to enhance their effectiveness. These findings underscore the utility of extending marketing mix models of new-brand performance to include product and distribution decisions.diffusion, new products, marketing mix, dynamic linear model, empirical generalization

The purpose of this paper is to investigate the relationship between the sales volume of a firm and its brand image. Consumers' self-perception and perception of brand image, with respect to congruency models, have a strong influence on their behavior in the marketplace. Therefore it is expected that the fluctuations (the authors use fluctuation and variation interchangeably) in image attributes will explain the fluctuations in sales figures. In order to test this hypothesis, consecutive surveys were carried out, on a monthly basis to collect image data. Factor analysis was performed on the image attributes over time and three main image factors were attained. To determine the net effect of image attributes on sales, multiple regression analysis was performed, using the time series data, and all three image factors were found to be significant in the model.Brand image, which usually includes the product's name, its main physical features and appearance (including the packaging and logo), and its main function(s), is the key to answer the question of how the consumer chooses

Edirne, housing, satisfaction, measurement invariance, multi-group analysis,

Using data from 50 U.S. markets, Bronnenberg, Dhar, and Dubé (2007) observe that geographic variation is the predominant source of variation in national brand market shares. The authors of this comment extend this surprising and previously undocumented result in several respects. First, they replicate this basic finding in France and find that it is robust to time and region aggregation and data duration. Second, because of regional variation in chain locations and the limited research on the relative effect of chains on market shares, they link chain effects to market shares in France. The authors find that (1) chain effects explain more variation in market share than either time or region effects and (2) the addition of chain effects attenuates region-specific effects. Thus, chain structure is both another source of regional variation in demand and an important consideration in its own right. Finally, by coupling brand-time effects with longer data, the authors note that brand-time effects are larger than brand-region effects in France. This result suggests the importance of long-term effects in marketing and the need to collect longer durations of data to explain variation in market shares.