Problem definition: Online retailers provide recommendations of ancillary services when a customer is making a purchase. Our goal is to predict the Net Present Value (NPV) of these services, estimate the probability of a customer subscribing to each of them depending on what services are offered...

Significant growth in data availability in revenue management, has made the use of machine learning tools core to forecasting and planning. In the case of e-commerce and retail, sophisticated machine learning models are key to accurate demand forecasting. This is often an essential input into...

Many firms regularly introduce new products. Before the launch of any new product, firms need to make various operational decisions, which are guided by the sales forecast. The new product sales forecasting problem is challenging when compared to forecasting sales of existing products. For...

We consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for a product. The retailer aims to maximize cumulative revenue collected over a finite time horizon by balancing two objectives: \textit{learning} demand and \textit{maximizing}...

Problem definition: We study personalized product recommendations on platforms when customers have unknown preferences. Importantly, customers may disengage when offered poor recommendations.Academic / Practical Relevance: Online platforms often personalize product recommendations using bandit...