Prof. Dr. h.c. mult. Hasso Plattner

Data-Driven Demand Learning and Dynamic Pricing Strategies in Competitive Markets

General Information

  • Teaching staff: Dr. Matthias Uflacker, Dr. Rainer Schlosser, Martin Boissier
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Time: Tue 13.30 - 15.00
  • Room: D-E.9/10
  • First course: April Tue 17, 2018

Short Description

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.

Goals of the Course


  • opportunities and challenges of dynamic pricing
  • rule-based pricing strategies
  • demand learning under competition
  • data-driven response strategies

Do ...

  • work in small teams
  • implement algorithms, simulate sales processes, and analyze performance results
  • directly compete against your fellow students in a competitive setting

Improve ...

  • mathematical, analytical, and modelling skills
  • optimization/machine learning techniques


  • the participants should be interested in mathematical methods
  • the number of participants is restricted to 20

Teaching and Learning Process

The course is a combination of a lecture and a practical part:

  • teachers impart relevant knowledge
  • students work on a self-containing topic in a team of ca. 3 people
  • students regularly present their work
  • every team creates data-driven strategies
  • teams can play against each other


  • 30% Presentations
  • 20% Project Results
  • 40% Project Documentation
  • 10% Personal Engagement


Slides and Upcoming Topics

  • 1st MeetingIntroduction (Tue April 17)
  • 2nd Meeting: Customer Behavior (Tue April 24)
  • 3rd Meeting: Pricing Strategies(Mon April 30)
  • 4th Meeting: Demand Learning (Tue May 8)
  • 5th Meeting: Exercises & Examples (Tue May 15)
  • 6th Meeting: Introduction to Price Wars Platform (Tue May 22)
  • 7th Meeting: Dynamic Pricing Challenge (Tue May 29)
  • 8th Meeting: no meeting (Tue June 5)
  • 9th Meeting: Group Meeting I (Tue June 12)
  • 10th Meeting: Presentations of First Results (Tue June 19)
  • 11th Meeting: Group Meeting II (Tue June 26)
  • 12th Meeting: Group Meeting III (Tue July 3)
  • 13th Meeting: Group Meeting IV (on arrangement due to TuK)
  • 14th Meeting: Final Presentations (Tue July 17)
  • merchant deadline: ~Sep 1
  • documentation/paper deadline: ~Sep 15


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