Dynamic Pricing using Machine Learning

<ul> <li>Have you ever looked up a plane ticket, and decided to buy later, only to come back and see the price has gone up a few hundred dollars?</li> <li>Have you ever experienced the same with concerts, hotels, or games?</li> <li>Have you ever thought about why Uber ride costs vary day to day?</li> </ul> All of the above is done using Dynamic Pricing techniques. Dynamic pricing is adjusting prices based on external elements such as demand, supply, market, and customer behavior. It involves setting flexible prices that can change frequently to optimize revenue, maximize profits, or achieve other business objectives. Dynamic pricing allows businesses to optimize their pricing strategies based on real-time market conditions and customer behavior, helping them to remain competitive and maximize their revenue potential. Generally speaking, dynamic pricing can be applied to most verticals and industries and is commonly used in e-commerce, retail, travel and hospitality, ride-sharing, sports, and entertainment industries. However, there are other sectors, such as highly regulated markets and commodities, that a single vendor couldn’t control the supply and demand. Therefore, it will not be as beneficial as highly scarce supply markets like airfare or concert tickets. In this article, we will review the use of machine learning in dynamic pricing. At first, we look at how traditionally dynamic pricing is done. Next, we review some of the strategic applications of machine learning methods in dynamic pricing, then we review some of the models used and a few papers published in each case. <a href="https://medium.com/@baabak/dynamic-pricing-using-machine-learning-5e882282effe">Website</a>