We show how a retailer can estimate the optimal price of a new product using
observed transaction prices from online second-price auction experiments. For
this purpose we propose a Bayesian P\'olya tree approach which, given the
limited nature of the data, requires a specially tailored implementation.
Avoiding the need for a priori parametric assumptions, the P\'olya tree
approach allows for flexible inference of the valuation distribution, leading
to more robust estimation of optimal price than competing parametric
approaches. In collaboration with an online jewelry retailer, we illustrate how
our methodology can be combined with managerial prior knowledge to estimate the
profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
