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A significant portion of academic research on marketing strategy focuses on how national brands of repeat-purchase goods are managed or should be managed. Surprisingly little consideration is given in this tradition to the extended role of geography, i.e. distance and space. For instance, manufacturers of brands in non-durable product categories are well aware of the fact that their national brands perform very different across domestic U.S. markets. This holds even for product categories with limited product differentiation. In this chapter, we outline various processes through which the influence of geography on performance of national brands materializes. We discuss a number of alternative explanations for the emergence and sustenance of spatial concentration of market shares. Several of these explanations are modeled empirically using data from the United States packaged goods industry. This chapter closes with avenues for further academic research on spatial aspects of the growth of new products.

This paper includes an examination of two key issues on price decisions: (1) how should price decisions be made (the strategic and normative issue) within market contexts, and (2) how are price decisions actually made (the execution and implementation of price decisions). The paper closes with some observations useful for applied research and strategies for making effective pricing decisions. The propositions and literature review show that one pricing strategy does not fit a brand in all market contexts that brand executives experience annually in managing brands. Setting specific price points requires continuing deliberate management responses to dynamic market contexts. This paper provides useful sense-making conjunctive steps to accomplish such deliberate thinking effectively relevant for different market contexts.

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

Over the past 25 years as a marketing academic, I have been fortunate to have collaborated with various researchers and firms and have contributed to the advancement of the marketing field. This is a review article that tracks my progress through these years that has led me to explore different areas of marketing, thereby shaping me as a researcher and an academic. As I see now, all of my research work can be viewed from a decision-making point of view – decisions that marketers can make either at the market, brand/firm/store, or the customer level. These decisions have in turn been transformed into strategies or tactics leading up to successful implementations and improved bottom-line results. The development of strategies/tactics and successful implementations can be seen in nearly 10 areas of research that I have involved myself in. This article also highlights how my research studies have contributed and advanced the creation of knowledge in each of these research areas.

Market share attraction models are useful tools for analyzing competitive structures. The models can be used to infer cross-effects of marketing-mix variables, but also the own effects can be adequately estimated while conditioning on competitive reactions. Important features of attraction models are that they incorporate that market shares sum to unity and that the market shares of individual brands ake in between 0 and 1. Next to analyzing competitive structures, attraction models are also often considered for forecasting market shares.

The econometric analysis of the market share attraction model has not received much attention. Topics as specification, diagnostics, estimation and forecasting have not been thoroughly discussed in the academic marketing literature. In this chapter we go through a range of these topics, and, along the lines, we indicate that there are ample opportunities to improve upon present-day practice. We also discuss an alternative approach to the log-centering method of linearizing the attraction model. This approach leads to easier inference and interpretation of the model.

Recent research has examined the effect of the Financial Services Modernization Act of 1999, more commonly known as the Gramm–Leach–Bliley Act (GLB), on the market value of U.S. commercial banks, life insurers, property-liability insurers, thrifts, finance companies, and securities firms. This study fills a gap in our understanding of the Act by measuring the price and trading volume effects of the GLB on U.S.-listed foreign banks. A primary contribution of this study is to examine the role, if any, of two corporate governance perspectives, the stakeholder (code law), and shareholder (common law) models, in a cross-sectional analysis of foreign bank market reaction to the GLB.

Using a generalized least squares (GLS) portfolio approach, Corrado's rank statistic, and confirmed by the traditional market model approach, we find significant negative share price reactions to certain legislative announcements surrounding the passage of the GLB. Trading volume reactions corroborate the significant share price responses. In general, our results indicate that investors in foreign banks reacted negatively to key legislative action. In a cross-sectional analysis, younger, higher-risk foreign banks with less concentrated ownership and more subordinated debt from countries with higher quality accounting standards appear to have more positive (or less negative) share price reactions.

In this chapter we investigate whether the pricing of IPOs on Europe’s new stock market differs from that of IPOs on main market segments. We report a 22.3 percentage point difference in the average first-day return of new market IPOs (34.3%) and the average first-day return of main market IPOs (12%). We show that reduced incentives to control wealth losses and different firm and offer characteristics partially explain the higher average first-day return on new market segments. We also find that the bundling of IPO deals has been more important to control underpricing costs on new market than on main market segments.

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

Warranty policies for certain products, such as automobiles, often involve consideration of two attributes, for example, time and usage. Since consumers are not necessarily homogeneous in their use of the product, such policies provide protection to users of various categories. In this chapter, product usage at a certain time is linked to the product age through a variable defined as usage rate. This variable, usage rate, is assumed to be a random variable with a specified probability distribution, which permits modeling of a variety of customer categories. Another feature of the chapter is to model the propensity to execute the warranty, in the event of a failure within specified parameter values (say time or usage). In a competitive market, alternative product/warranty offerings may reduce the chances of exercising the warranty. This chapter investigates the impact of warranty policy parameters with the goal of maximizing market share, subject to certain constraints associated with expected warranty costs per unit not exceeding a desirable level.