Ancillary Services in Targeted Advertising: From Prediction to Prescription

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 to them, and ultimately prescribe the optimal personalized service recommendation that maximizes the expected long-term revenue. Methodology/results: We propose a novel method called cluster-while-classify (CWC), which jointly groups observations into clusters (segments) and learns a distinct classification model within each of these segments to predict the sign-up propensity of services based on customer, product, and session-level features. This method is competitive with the industry state of the art and can be represented in a simple decision tree. This makes CWC interpretable and easily actionable. We then use double machine learning (DML) and causal forests to estimate the NPV for each service and, finally, propose an iterative optimization strategy---that is, scalable and efficient---to solve the personalized ancillary service recommendation problem. CWC achieves a competitive 74% out-of-sample accuracy over four possible outcomes and seven different combinations of services for the propensity predictions. This, alongside the rest of the personalized holistic optimization framework, can potentially result in an estimated 2.5%-3.5% uplift in the revenue based on our numerical study. Managerial implications: The proposed solution allows online retailers in general and Wayfair in particular to curate their service offerings and optimize and personalize their service recommendations for the stakeholders. This results in a simplified, streamlined process and a significant long-term revenue uplift.