Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230775
Authors: 
Born, Alexander
Kovachka, Nikoleta
Lessmann, Stefan
Seow, Hsin-Vonn
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-065
Abstract: 
Second-hand car markets contribute to billions of Euro turnover each year but hardly generate profit for used car dealers. The paper examines the potential of sophisticated data-driven pricing systems to enhance supplier-side decision making and escape the zero-profit-trap. Profit maximization requires an accurate understanding of demand. The paper identifies factors that characterize consumer demand and proposes a framework to estimate demand functions using survival analysis. Empirical analysis of a large data set of daily used car sales between 2008 to 2012 confirm the merit of the new factors. Observed results also show the value of survival analysis to explain and predict demand. Random survival forest emerges as the most suitable vehicle to develop price response functions as input for a dynamic pricing system.
Subjects: 
Automotive Industry
Price Optimization
Survival Analysis
Dynamic Pricing
JEL: 
C00
Document Type: 
Working Paper

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