A demand learning data based approach to optimize revenues of a retail chain

Abstract

We consider the problem of selling a perishable product via a retail chain during a restricted time interval. Based on data describing buying behavior in the outlets of the retail chain in the first part of a specified selling season, we provide an optimal policy for reallocating the remaining inventory of the product and setting prices in the remaining part of the selling season in order to optimize total expected revenues.We use a Bayesian update approach in which the retail chain learns about customers' demand patterns in each outlet during earlier periods and decides how to allocate the remaining stock and adjust prices w.r.t customer arrival rates and willingness to pay. An illustrative example is used to describe our demand learning approach. © Springer-Verlag Berlin Heidelberg 2010.