One of the most intractable problems facing marketing managements today is to know when to buy marketing research to enhance their understanding of the probability of success. Conversely, when will the expenditure and resultant delay outweigh the benefits from a reduction in uncertainty? Nowhere is this problem more keenly felt than in new product development. Launching new products is often vastly expensive, and available evidence suggests that products fail more often than they succeed. However, well researched “pilot” marketing and sequential launches give competition time to retaliate or imitate, thereby constituting risks of a different ilk. None the less, marketing managements are tending to carry out more and more test operations to attempt to assess the likely outcomes of broadscale operations. This article provides an early report from part of a research programme carried out at the University of Bradford into methods of marketing experimentation. It offers an integration of Bayesian decision theory and network analysis which, in conjunction with DCF techniques, provide a powerful tool of cost/benefit analysis.
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