امار چند متغیره درمدیریت بازار یابی صنعتی: جعبه ی ابزارمتخصص

Much published work over the years has pointed to the differences between business-to-consumer (B2C) and business-to-business (B2B) marketing. An undesirable by-product of this sometimes misdirected distinction is that managers working within B2B environments have generally not considered the use of what are seen as B2C techniques, such as multivariate statistical analysis. This article is structured in three parts. First, the argument for the similarities between B2B and B2C marketing is developed; second, three different multivariate statistical techniques are presented and combined to form a practical tool kit for use by B2B managers on strategic, operational, and tactical levels; and third, the results of an application of the techniques in the life science research chemicals industry is reported, demonstrating that the tool kit substantially enhanced managerial understanding of customer decision processes.

As any perusal of the appropriate journals indicates, the use of quantitative methodologies in business-to-consumer (B2C) marketing has been widespread for decades, while business-to-business (B2B) marketing has not embraced these techniques to the same extent. This is in part because of the assumption that B2B marketing is fundamentally different from B2C and the resultant reluctance to “borrow” B2C techniques. We argue that in many industries there is much to be gained by accepting the similarities in the two disciplines and thereby considering some of the multivariate techniques developed to enhance consumer understanding. This article shows how a tool kit of multivariate statistical techniques can be used together to give B2B marketers a competitive edge on three levels: strategically, operationally, and tactically. The tool kit discussed here consists of conjoint analysis, cluster analysis, and correspondence analysis. Conjoint analysis illuminates complex decision-making processes in multiproduct, multisupplier contexts and can thus be used to inform overall marketing strategy; cluster analysis, which segments buyers into groups with similar needs, enlightens operational resource allocation decisions; and correspondence analysis, which displays cluster information in two-dimensional space, can produce a visual aid useful for tactical sales training. We have structured this article as follows: firstly, we briefly review the debate on similarities and differences between B2B and B2C marketing; we then discuss the mechanics and applications of each of the three multivariate statistical techniques separately and together; finally, we demonstrate how the tool kit has been successfully applied strategically, operationally, and tactically in the life sciences industry.

is article began by arguing that the assumption of fundamental difference between B2B and B2C marketing may not always be of practical benefit to marketing managers. Specifically, it was argued that an unwillingness to see the relevance of B2C techniques to the analysis of B2B issues has resulted in little use by B2B marketers of the sophisticated multivariate statistical techniques used to understand the decision-making behaviour of individual consumers. It was argued that the differences between the two marketing contexts are constituted by degree rather than form and that where the focus of interest is individual decision making rather than the process of joint decision making or decision conflict; then, multivariate statistical techniques are likely to be useful. It was further suggested that this focus is likely to be most predominant in straight rebuy contexts. This article has gone on to show how three techniques can be used together in a tool kit to inform marketing strategy, operations, and tactics. Finally, a study in the life sciences industry has demonstrated on a practical level how these tools can be applied and what tangible outcomes can result. This study has added to, and opened up, several research streams. Firstly, it has made a contribution to the literature on the use of multivariate statistics in industrial marketing and has broadened the possibilities of such applications. Secondly, it has addressed a specific situation in which B2C concepts can work in a B2B context and opens the way to the development of a diagnostic framework to enable B2B managers to more easily identify contexts in which B2C tools can be used effectively. Thirdly, it has added to the growing literature that advocates seeking similarities rather than differences between B2B and B2C marketing.