Statistical experimental methods have emerged as a powerful method for analyzing cause and effect relationships among factors over the past 75 years. Design of Experiments (DoE) methods are used in industry for process improvement and optimization purposes (Singh et al. 2006; Huang and Lin 2004. Taguchi (1986) introduced a simplified and modified DoE approach, which has been widely adopted in industry. More recently, the power of Taguchi's approach is that it is quite generally applicable to a broad range of experimental situations in which the components of variation, including those of interaction, are desired.
It has been used for such diverse applications as bearing deflections, diesel engine nozzle design, cloth quality evaluation, the design of clothing, bank and insurance contracting and electrical power consumption (Taguchi, 1988b) as well as engineering and science in general (Wright 2002). One limitation of the method is the actual process tends to cause disruption in the plant, and may be uneconomical (Sukthomya and Tannock 2005). In recent years, researchers have developed approaches in Neural Networks (Guh and Tannock 1999); and Evolutionary Operations (Box 1978)) to test process parameters, without production interruptions. However, in this study, classical experimental analysis and Taguchi Methods, without actual experimentation, are used to investigate process parameter effects.
While Taguchi methods have been used widely in all sorts of applications, their use in marketing is relatively limited. Their most common applications have been in advertising and sales, and direct marketing campaigns where success factors, thought to have major influence on sales, are tested to create optimal ads for increasing response rates. The techniques have been used to increase response to email, website and more recently pay per click advertising. In these cases orthogonal arrays were created to test which combination of features or success factors such as pricing, subject line, monthly fee, message text, sender, image, etc. generate optimum response. The methods have been touted as producing response increases of hundreds sometimes thousands of percent (Kowalick 2004; Roy and Bullock 2004).
More relevant to the current study, Taguchi methods have also been used in marketing in later stage product design where optimal values are determined for product features. For example, the size or weight of a SmartPhone, or its data transfer rate, or its storage capacity might be optimized as to cost of manufacture versus the market share to be garnered by the new product. As a matter of fact, as will be seen below in the Literature Review, the literature on the use of experimental method in Marketing, particularly Taguchi's method, is relatively sparse. This is perhaps due to marketing's growth out of and reliance on social rather than hard sciences. As will be demonstrated, the adoption of this method, more commonly used in engineering design and process management, can prove quite useful in marketing research. Rather than traditional one-factor-at-a-time experiments Taguchi technique "can be used to study effects of change of many factors at a time. Because the behavior of all kinds of things may usually be dependent on more than one factor, the areas of use of the technique are unlimited" (Roy and Bullock, p. 3), including testing many factors in combination in order to optimize market share. Thus, this paper provides a novel addition to the relatively sparse literature.
The SmartPhone was chosen for analysis as an extension of a research project originally given to an advanced marketing class taught by one of the authors. This project simply provided a convenient opportunity to demonstrate the use of Taguchi methods in marketing analysis.
The work of Sir Ronald A. Fisher of England (Fisher 1942) is credited with the immense contribution to experimentation over several decades ago (Kempthorne 1967). …