Optimal Price Targeting

We study the profitability of personalized pricing policies in a setting with consumer-level panel data. To compare pricing policies, we propose an inverse probability-weighted estimator of profits, discuss how to handle nonrandom price variation, and show how to apply it in a typical consumer-packaged good market with supermarket scanner data. We generate pricing policies from Bayesian hierarchical choice models, regularized regressions, neural networks, and nonparametric classifiers using different sets of data inputs. We find that the performance of machine learning methods is highly varied, ranging from a 30.7% loss to a 14.9% gain relative to a blanket couponing strategy, whereas hierarchical models generate profit gains in the range of 13−16.7%. Across all models, information on consumers’ purchase histories leads to large improvements in profits, whereas demographic information has only a small impact. We find that out-of-sample fit statistics are uncorrelated with profit estimates and provide poor guidance toward model selection.