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We show how a retailer can estimate the optimal price of a new product usingobserved transaction prices from online second-price auction experiments. Forthis purpose we propose a Bayesian P\'olya tree approach which, given thelimited nature of the data, requires a specially tailored implementation.Avoiding the need for a priori parametric assumptions, the P\'olya treeapproach allows for flexible inference of the valuation distribution, leadingto more robust estimation of optimal price than competing parametricapproaches. In collaboration with an online jewelry retailer, we illustrate howour methodology can be combined with managerial prior knowledge to estimate theprofit maximizing price of a new jewelry product.

Optimal pricing using online auction experiments: A P\'olya tree approach