Ancillary Services in Targeted Advertising: From Prediction to Prescription

Alison Borenstein (), Ankit Mangal (), Georgia Perakis (), Stefan Poninghaus (), Divya Singhvi (), Omar Skali Lami () and Jiong Wei Lua ()
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Alison Borenstein: Wayfair, Boston, Massachusetts 02116
Ankit Mangal: Wayfair, Boston, Massachusetts 02116
Georgia Perakis: Operations Management, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Stefan Poninghaus: Wayfair, Boston, Massachusetts 02116
Divya Singhvi: Stern School of Business, New York University, New York, New York 10012
Omar Skali Lami: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Jiong Wei Lua: Wayfair, Boston, Massachusetts 02116

Manufacturing & Service Operations Management, 2023, vol. 25, issue 4, 1285-1303

Abstract: 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.

Keywords: display optimization; NPV estimation; classification; causal inference; predictive analytics; prescriptive analytics; online retail (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:25:y:2023:i:4:p:1285-1303

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