Personalized Pricing and the Value of Past Purchase Histories: An Empirical Perspective

Isis Durrmeyer, Jean-François Fournel and Mario Samano
Additional contact information
Isis Durrmeyer: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse, CEPR - Center for Economic Policy Research
Jean-François Fournel: Unknown
Mario Samano: Unknown

Working Papers from HAL

Abstract: Grocery loyalty programs record purchase histories that retailers do not use to personalize prices. We measure how much they forgo with a structural model of demand and pricing in which a Bayesian seller learns preferences from past purchases and personalizes prices. Profits rise by 7.1% with one-year histories, capturing two thirds of the gains under perfect knowledge of preferences. Most consumers (84.1%) gain, but the losses of the minority outweigh these gains, and aggregate consumer surplus falls. An optimal loyalty program charges a membership fee, yet most consumers enroll, because opting out signals price insensitivity.

Keywords: personalized pricing; behavior-based price discrimination; purchase histories; Bayesian learning; loyalty programs; value of information (search for similar items in EconPapers)
Date: 2026-10-21
Note: View the original document on HAL open archive server: https://hal.science/hal-05829059v1
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hal.science/hal-05829059v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-05829059

Access Statistics for this paper

More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().