Machine Learning Techniques to Power Dynamic Pricing
Wayfair has over 10 million products spanning home furnishings, housewares and home improvement goods and more, making it a “one stop shop” for everything home. With nearly 1,600 engineers and data scientists, its team is constantly finding innovative ways to solve the hardest problems in its highly competitive industry. Among those many challenges, is that a variety of internal and external conditions can trigger pricing changes, from suppler inventory to surges in demand and more. Wayfair employs dynamic pricing to address that phenomenon of pricing fluidity in ecommerce. To guide decisions around price changes in response to these situations and triggers, Wayfair uses several Machine Learning techniques. Attendees will learn about several initiatives for which Wayfair uses feature engineering and supervised learning techniques to achieve forecasting precision. These projects will illustrate Wayfair’s ability to react quickly and decisively amid changing market conditions, while maintaining accuracy amid increasing complexity at scale. Each of the real-world examples explored during this talk will showcase the intersection of business, data science and engineering.
Presenter: Jomar Delores, Wayfair