Born, Alexander Kovachka, Nikoleta Lessmann, Stefan Seow, Hsin-Vonn
Year of Publication:
IRTG 1792 Discussion Paper No. 2018-065
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.
Automotive Industry Price Optimization Survival Analysis Dynamic Pricing