E-commerce is everywhere. Markets have become increasingly transparent and competitive. Products and competitors’ offers can be easily compared. It has become easy and almost costless to adjust prices. For sellers “dynamic pricing” offers many opportunities and is therefore more and more used. However, smart pricing strategies are hard to derive.
In this seminar, we learn how to compute dynamic pricing strategies. Based on data-driven approaches we will use simple regression models to quantify demand and to estimate sales probabilities (real-life data from the Amazon market place is available). We will also take a look at theoretical optimization models that can be solved using dynamic programming techniques.
After we have dived into the depths of demand learning and optimization, each student will compete against his fellow students on our pricing platform. This simulation platform has been build by last semester's master projects and allows us to evaluate the performance of different strategies. It has been built as a micro service architecture and allows to easily add a student's merchant implementation to the running marketplace. As part of this competition, we plan to let every student play against each other student as well as a simulation with all student competing concurrently.
