11-09, 10:15–11:00 (America/New_York), Central Park West (6th floor)
The New York Times launched its paywall in March 2011, beginning its journey as a subscription-first news and lifestyle service. Since its inception, this “metered” access service has been designed so that nonsubscribers can read a fixed number of articles every month before encountering a paywall; this article limit is widely referred to as the “meter limit.” When the paywall was launched, the meter limit was the same for all users. As The Times has transformed into a data-driven digital company, we are now successfully using a causal machine learning model called the Dynamic Meter to set personalized meter limits and to make the paywall smarter. In doing so, this model optimizes for conversion and engagement while balancing the trade-off between them.
This talk will discuss how we use causal machine learning at The New York Times to optimize the paywall for conversion and engagement while balancing a trade-off between them. We will also show how our model leverages causal meta-learners and how it is deployed using a tech stack that relies on open source tools.
No previous knowledge expected
Rohit Supekar is a data scientist at The New York Times, and he currently works on developing and deploying causal machine learning models to power The Times’s paywall. He is broadly passionate about understanding the world around us using data, building mathematically rigorous models, and deploying them using modern production-quality engineering tools.
Prior to joining The Times, he obtained a Ph.D. in 2021 and a Master's degree in 2017 from M.I.T., and a Bachelor's degree in 2015 from I.I.T. Madras in India. His Ph.D. thesis work involved building mathematical models for active fluids, such as a dense suspension of bacteria, by using a combination of partial differential equations, machine learning, and principles from fluid mechanics.
Outside of work, Rohit enjoys reading, long-distance running, and alpine skiing.