Conference Agenda

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Session Overview
Session
MA8 - RM1: Dynamic pricing
Time:
Monday, 27/June/2022:
MA 8:30-10:00

Session Chair: Laura Niome Sprenkels
Location: Forum 12


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Presentations

Optimal dynamic pricing when customers develop a habit or satiation

Wen Chen1, Ying He2, Saurabh Bansal3

1Providence Business School; 2University of Southern Denmark; 3The Pennsylvania State University

We study a dynamic pricing problem over multiple periods when consumers develop a habit or satiation from their past consumption. We derive an inter-temporal demand function to capture these two effects. We establish that the profit maximization problem under our demand function is jointly concave and then characterize the trends in the optimal prices over the multi-period horizon. Finally, we provide several extensions including bounds on prices and optimal profit and non-stationary state dependence.



Pricing fast and slow: limitations of dynamic pricing mechanisms in ride-hailing

Daniel Freund1, Garrett J. van Ryzin2

1MIT, United States of America; 2Amazon, United States of America

Ride-hailing firms set prices dynamically to match supply and demand. But rapid price changes incentivize riders to wait for low prices. When prices drop, patient customers request en masse, causing a drop in supply and a price increase. We show how dynamic pricing inherently creates such oscillations in supply and prices, that these oscillations in supply levels are inherently inefficient, and that a service model that allows riders to wait in a formal queue overcomes this inefficiency.



Multi-product pricing: A customer choice model and a dynamic pricing approximation

Laura Niome Sprenkels, Zümbül Atan, Ivo Adan

TU/e, Netherlands, The

We study the pricing problem of an assortment of multiple, substitutable products. We propose two new methods that can support retailers with maximizing their revenues. The first method is a customer choice model based on the Markov Chain Choice model in combination with reservation prices. The second method relies on a linear approximation for the finite inventory, finite time horizon multi-product dynamic pricing problem.



 
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