We model concept testing in new product development as a search for the mos
t profitable solution to a design problem. When allocating resources, devel
opers must balance the cost of testing multiple designs against the potenti
al profits that may result. We propose extreme-value theory as a mathematic
al abstraction of the concept-testing process. We investigate the trade-off
between the benefits and costs of parallel concept testing and derive clos
ed-form solutions for the case of profits that follow extreme-value distrib
utions. We analyze the roles of the scale and tail-shape parameters of the
profit distribution as well as the cost of testing in determining the optim
al number of tests and total budget for the concept phase of NPD. Using an
example, we illustrate how to estimate and interpret the scale and tail-sha
pe parameters. We find that the impact of declining concept-testing costs o
n expected profits, the number of concepts tested, and total spending depen
d on the scale/cost ratio and tail-shape parameter of the profit distributi
on.