Jen Wang received her Ph.D. in Biophysical Chemistry from the University of Iowa, where she researched cancer-related drug development. Though a far cry from her work at Wayfair today, her work as a grad student and subsequently, a post-doc at Albert Einstein College of Medicine in New York, gave her the opportunity to cultivate a deep interest and expertise in the rich predictive capabilities of machine learning. In 2016, she joined Wayfair's data science team, where she now leads a team that supports display and direct mail marketing. In her session, she will discuss how data scientists at Wayfair take data-driven approaches to analyze causal effects of advertising and drive incremental revenue.
Causal Inference and Uplift Modeling in Digital Marketing
Traditional marketing strategies target customers who are most likely to respond to ads. This can cause wasted marketing investment since those customers may buy even without seeing an ad. Uplift modeling predicts the causal effect of marketing campaigns by comparing the conversion rates of both treated and control groups, and thus selects the most “persuadable” customers for targeting. In this presentation Jen will discuss how data scientists at Wayfair take uplift modeling approaches to drive incremental revenue and improve marketing RoI.