An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace Le Chen Alan Mislove Christo Wilson Northeastern University Northeastern University Northeastern University Boston, MA USA Boston, MA USA Boston, MA USA
[email protected] [email protected] [email protected] ABSTRACT tings due to lack of data (e.g., competitors’ prices) and physical The rise of e-commerce has unlocked practical applications for al- constraints (e.g., manually relabeling prices on products). In con- gorithmic pricing (also called dynamic pricing algorithms), where trast, e-commerce is unconstrained by physical limitations, and col- sellers set prices using computer algorithms. Travel websites and lecting real-time data on customers and competitors is straightfor- large, well known e-retailers have already adopted algorithmic pric- ward. Travel websites are known to use personalized pricing [25], ing strategies, but the tools and techniques are now available to while some e-retailers are known to automatically match competi- small-scale sellers as well. tors prices [40, 17]. While algorithmic pricing can make merchants more competi- While algorithmic pricing can make merchants more compet- tive, it also creates new challenges. Examples have emerged of itive and potentially increase revenue, it also creates new chal- cases where competing pieces of algorithmic pricing software inter- lenges. First, poorly implemented pricing algorithms can inter- acted in unexpected ways and produced unpredictable prices [37], act in unexpected ways and even produce unexpected results, es- as well as cases where algorithms were intentionally designed to pecially in complex environments populated by other algorithms. implement price fixing [5]. Unfortunately, the public currently lack For example, two competing dynamic pricing algorithms inadver- comprehensive knowledge about the prevalence and behavior of al- tently raised the price of a used textbook to $23M on Amazon [37]; gorithmic pricing algorithms in-the-wild.