The dynamic pricing competition
Pricing is one of the most challenging topics in the business world, with huge impacts when it comes to making profit or loss. In today’s world, prices are changed more frequently than ever before. Everyone involved tries to make the best use of data for their pricing decisions. However, designing data-driven price algorithms that perform well in competitive markets with uncertainty about customer behavior remains a big challenge.
To address this challenge, we invite people from academia and industry from various backgrounds(Operations Research, Pricing & Revenue Management, Machine Learning & AI – Reinforcement Learning) to let their ideas and algorithms compete in various basic market settings. By participating you will receive valuable insights on how your pricing strategy is performing: where it is good and outperforming others, and where is it lacking behind and gets beaten. Get the ultimate proof that your pricing algorithm is rocket science and unbeatable!
Contest details: We test and evaluate the performance of data-driven pricing algorithms in three different market scenarios: a duopoly with inventory constraints, a multi-product oligopoly, and a large market with horizontal product differentiation (more details on the three market scenarios are provided below). Each participating team is allowed to submit at most one algorithm for each market scenario. It is thus possible (but not necessary) to submit an algorithm for all three market scenarios; one can also participate in just one or two market scenarios. Algorithms have to be written in Python; more details on the required input and output variables and computation time restrictions are provided below.
From May 1st until July 31st, 2019 we run a training period, during which we simulate the performance of the algorithms each night. The simulations are based on undisclosed demand-generating mechanisms that are used to determine the average total revenue obtained by each algorithm. Based on these simulations we will provide information on the algorithm’s performance to the participants, which can be used to improve the algorithms. Thus, during the test period it is possible to replace every day one’s algorithm by a (hopefully) improved version.
By August 1st all participant needs to submit their final algorithms for the “real” contest simulation runs. Rankings are based on the obtained average revenue over all simulations. Based on these final rankings we will determine the winner of the contest, for each of the three market scenarios. For each market scenario, the best performing team will receive 500 euro. In case of unforeseen events, the organizing committee decides on the final ranking.
The submitted algorithm will always be your property, we will never publish the code, we only use it exclusively within the competition and for academic purposes (e.g. comparing different price algorithms), but never for commercial purposes. In case you would like to keep your algorithm private, it is possible to opt-out of an eventual future publication or other academic use by sending us an email to email@example.com before the competition starts. With the final submission – not the test phase – we only ask for a brief (e.g. 100 words) description of your approach. These descriptions are only used in the performance evaluation, similar to the below paper with the results of the 1st contest edition in 2017 at the Informs RM & Pricing Conference in Amsterdam.
Final definition of competition setup and deadlines
end of February 2019
Start of training period
1st of May 2019
Deadline for final algorithm submission
1st of August 2019
Announcement of winners and publishing of results
Mid August 2019
WHY YOU SHOULD JOIN?
... apart from winning and proving you are best ...
Similar to the 1st edition 2017, we may try to publish the results and findings of the 2019 competition again in a publication, depending on the academic value of the obtained results.
Paper on the 1st edition - Dynamic Pricing Challenge at the Informs RM & Pricing Conference in Amsterdam 2017
published in the Journal of Revenue and Pricing Management 2018
For more information please contact: firstname.lastname@example.org