Dynamic Pricing and Price Discrimination on Online Platforms: An Exploratory Analysis
Many of us assume that our data shapes the price we pay. For this thesis, four market-leading companies in Swiss digital commerce explained what actually decides the number on your screen. All four companies vary their prices substantially, but all four stated that they do not price individually.
Haji, Aya;Kissmann, Sina, 2026
Type of Thesis Bachelor Thesis
Client Preisüberwachung (PUE)
Supervisor Binswanger, Mathias
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International research documents practices such as browsing-based pricing and geographic price variation. For Switzerland, however, empirical evidence remains limited. Consequently, the Swiss Price Supervisor commissioned this thesis to examine how prices are formed and adjusted in Swiss digital commerce. The thesis covers price variation between users and locations, the underlying pricing mechanisms, the transparency shown to consumers, and the implications for consumer welfare and market fairness. The findings reflect the views of the authors and not the position of the Price Supervisor.
The study adopted an exploratory approach. Semi-structured interviews were held with pricing experts from SBB, Digitec Galaxus, Lufthansa, and Ticketcorner, four market-leading companies in different industries. The interview guide followed the four research questions, and the transcripts were compared using Meuser and Nagel. A pricing workbook from Ticketcorner for a ski resort in the 2026/27 season was analysed with descriptive comparisons and a regression model, showing how the base price of a one-day ticket varied by customer category, booking lead time, and weather.
All four companies vary their prices substantially, but none reported differentiating between individual customers, contrary to what the literature predicts. All four said that two people searching for the same product at the same moment see the same price, regardless of device, login, or location. Prices move according to customer group and time of purchase. In the ski pricing data, the strongest influences on price are which age group a customer belongs to and when they book, not anything the company knows about them individually. The systems are also less automated than existing research suggests. Algorithms support the process, but staff set the pricing rules. The upper price limits vary considerably, ranging from fixed caps to more flexible structures ultimately shaped by customers’ willingness to pay. None of the four has an internal definition of a fair price, and disclosure differs from company to company. Where individual pricing is absent, where each company sets its upper price limits, and how it explains its prices, become central questions for consumers. Apart from the Ticketcorner pricing file, these findings rest on what the companies reported.
Studyprogram: Business Administration International Management (Bachelor)
Keywords dynamic pricing, digital platforms, personalized pricing, price transparency, pricing algorithms
Confidentiality: öffentlich