Optimal Price Targeting

Adam N. Smith, Stephan Seiler and Ishant Aggarwal

No 9439, CESifo Working Paper Series from CESifo

Abstract: 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 non-random 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, while demographic information only has a small impact. We find that out-of-sample fit statistics are uncorrelated with profit estimates and provide poor guidance towards model selection.

Keywords: targeting; personalization; heterogeneity; choice models; machine learning (search for similar items in EconPapers)
JEL-codes: C11 C33 C45 C52 D12 L11 L81 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-com, nep-cwa, nep-dcm, nep-ind and nep-reg
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9439

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