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Pricing in a Digital World

English title Pricing in a Digital World
Applicant Bühler Stefan
Number 178836
Funding scheme Project funding (Div. I-III)
Research institution Forschungsgemeinschaft für Nationalökonomie (FGN-HSG) Universität St.Gallen
Institution of higher education University of St.Gallen - SG
Main discipline Economics
Start/End 01.06.2018 - 31.05.2022
Approved amount 460'000.00
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All Disciplines (3)

Legal sciences
Science of management

Keywords (6)

Escalating Fines; Online Privacy; Behavior-Based Pricing; Digitization; Customer Backlash; Payment Evasion

Lay Summary (German)

Cookies, Kundenkarten, oder digitale Fingerabdrücke - Unternehmen nutzen die Möglichkeiten der Digitalisierung, um das Kaufverhalten von Kunden systematisch zu analysieren und ihre eigenen Preise dynamisch anzupassen. Die Auswirkungen der verhaltensbasierten Preisdiskriminierung (BBPD) auf die Preisstruktur und die Markteilnehmer sind bisher nur in Teilen untersucht worden. Dieses Projekt leistet einen Beitrag zur Analyse der Auswirkungen von BBPD.
Lay summary
Inhalt und Ziel des Forschungsprojekts

Das übergeordnete Ziel dieses Projektes ist es, zu einem besseren Verständnis von BBPD beizutragen. Die Analyse fokussiert auf folgende Aspekte: (i) das Preissetzungsverhalten, wenn Kunden nur unvollständig identifiziert werden können; (ii) die Auswirkungen auf das Kaufverhalten strategischer Kunden, die bei variierenden Preisen zu möglichst tiefen Preisen einkaufen wollen; (iii) die strategische Rolle von Kundenprotesten und Boykotten; sowie (iv) die Parallelen von BBPD zur dynamischen Anpassung von Bussen für eine Umgehung der Zahlungspflicht (z.B. Online-Piraterie, Schwarzfahren).

Wissenschaftlicher und gesellschaftlicher Kontext des Forschungsprojekts

Dieses Projekt wird neue Erkenntnisse über die Auswirkungen der verhaltensbasierten Preisdiskriminierung generieren, die in der digitalen Welt zunehmend an Bedeutung gewinnt. Die Ergebnisse werden eine fundierte Diskussion über die gesellschaftlichen Auswirkungen von BBPD ermöglichen und dazu beitragen, Lösungen für die Vermeidung möglicher schädlicher oder unerwünschter Konsequenzen zu finden.

Direct link to Lay Summary Last update: 24.05.2018

Responsible applicant and co-applicants


Project partner


In the digital world, tracking individual customers is commonplace. This research project provides new insights into optimal pricing when sellers can observe their customers' purchase histories. We will pursue the following three related sub-projects:(1) Behavior-based price discrimination with imperfect customer recognition.The first sub-project analyzes optimal pricing if the seller can observe its customers' purchase histories and may engage in behavior-based price discrimination. This practice has received a lot of attention recently, since new technologies have improved the firms' abilities to track individual customers. Examples include loyalty programs for supermarkets, and the use of online tracking tools such as cookies, beacons, and fingerprinting (Acquisti, Taylor, and Wagman, 2016). We add to the price discrimination literature by introducing (i) imperfect customer recognition, and (ii) the possibility that the seller does not focus on pure profit only. Our generalized setting naturally nests monopoly pricing with imperfect customer recognition and the canonical model of optimal law enforcement with uncertain detection (Becker, 1968; Polinsky and Shavell, 2007). We contribute to the literature on optimal law enforcement by providing a novel explanation for "escalating penalty schemes".(2) Dynamic payment evasion.Fraudulent consumption by nonpaying consumers (e.g., digital piracy, shoplifting, or fare dodging) is pervasive in many markets. This sub-project builds on Buehler, Halbheer, and Lechner (2017) and studies how a profit-maximizing firm sets prices for regular customers and fines for caught payment evaders over time. Work by Board and Pycia (2014) suggests that the possibility to acquire a product without payment provides low-value customers with an outside option, such that the Coase conjecture should be expected to fail for prices (but not necessarily for fines). Using the tools developed in the first sub-project, we will characterize the profit-maximizing prices and fines. In addition, we will exploit a data set on fare dodging in the greater Zurich area that we already posses to provide novel evidence on the dynamic behavior of detected payment evaders. We also work on obtaining data on "Coop supercard" holders who use self-scanning in traditional supermarkets and face possible inspection by on-site personnel to gain further insight into dynamic payment evasion.(3) Online privacy and customer backlash. Firms are often tempted to price discriminate customers based on their individual purchase histories. However, they may refrain from such price discrimination if they fear to antagonize their customers: If a firm's customers realize that they are being discriminated, they may produce a backlash that renders price discrimination unprofitable. This sub-project studies behavior-based pricing when customers do not know ex-ante whether they are being discriminated against, but may discover differential pricing over time. Using a repeated game framework, we will examine whether the threat of a backlash can protect customers from unwanted price discrimination. In addition, we will perform a laboratory experiment to test the predictions of the theoretical analysis.