Product-development and marketing managers know that customers make purchasing decisions on the basis of many criteria, including service quality, delivery speed and price. But since no company can excel in all aspects of service delivery simultaneously, companies must make trade-offs on the basis of what they do best, what criteria matter most to their customers, and what their competitors are offering.
An August 2002 white paper, “Understanding Customer Value Drivers: A Key to Successful Service Management,” offers managers a way to assess customers' choices in order to optimize return on their product-development and customer-service investments. Authors Rohit Verma, associate professor of operations management at the University of Utah's Eccles School of Business, and Gerhard Plaschka, associate professor of management at DePaul University's Kellstadt Graduate School of Business, base their framework upon choice modeling (CM) — an approach first introduced in the 1970s by Daniel L. McFadden, Nobel Prize-winning professor of economics at the University of California, Berkeley.
McFadden's research focused on both the economic reasons for individual choices and the ways researchers could measure and predict these choices. He began with the macroeconomic theory that says people will act to maximize their self-interest and applied it to the most complex microeconomic choices, such as why people take certain jobs, whether they marry, and how they travel to work. McFadden's work subsequently led to applications in various fields, including marketing, natural resource economics and transportation, as well as service management.
Verma and Plaschka believe that most companies lack a clear understanding of their relevant customer and market drivers and, as a result, their product or marketing managers tend to take a scattershot approach, hoping that at least one product-service offering will succeed. Using McFadden's theories and subsequent work on experimental choice analysis (introduced by Jordan Louviere of the University of Technology, Sydney, and other researchers), the authors developed a three-step process to assess and challenge management preconceptions of how customer offerings will be received.
CM links theoretical behavior —observed in experiments, surveys and other forms of stated preferences — with behavior observed in real-life situations. Unlike conjoint analysis (in which study respondents rank their preferences using experimental profiles), CM requires that respondents make choices in simulated situations derived from realistic variations of expected market offerings. The process comprises three steps.