Modern e-commerce platforms pose both opportunities as well as hurdles for merchants. While merchants can analyze market situations at any given point in time and automatically reprice their products, they also have to compete simultaneously with dozens of competitors which might deploy automated pricing strategies on their own.
Currently, retailers lack the possibility to test, develop, and evaluate their algorithms appropriately before releasing them into the real world. At the same time, it is challenging for researchers to investigate how pricing strategies interact with and against each other under heavy competition.
We built an open simulation platform for dynamic pricing competition on online marketplaces. To be both flexible and scalable, the platform uses a micro service architecture and handles hundreds of concurrent merchants and consumers. It allows merchants to deploy the full width of pricing strategies, from simple rule-based strategies to more sophisticated data-driven strategies using machine learning. Our platform enables analyses of how a strategy's performance is affected by the customer behavior, price adjustment frequencies, the competitors' strategies, and the exit/entry of competitors. Moreover, our platform allows to analyze the long-term behavior of self-adapting strategies.