First Degree Price Discrimination Using Big Data

Abstract

Second and 3rd degree price discrimination (PD) receive far more attention than 1st degree PD, i.e. person-specific pricing, because the latter requires previously unobtainable information on individuals’ willingness to pay. I show modern web behavior data reasonably predict Netflix subscription, far outperforming data available in the past. I then present a model to estimate demand and simulate outcomes had 1st degree PD been implemented. The model is structural, derived from canonical theory models, but resembles an ordered Probit, allowing methods for handling massive data-sets. Simulations show using demographics alone to tailor prices raises profits by 0.14%. Including web browsing data increases profits by much more, 1.4%, increasingly the appeal of tailored pricing, and resulting in some consumers paying twice as much as others do for the exact same product.