The New York Times (NYT) launched its paywall in March 2011, beginning its journey as a subscription-first news and lifestyle service. From its inception, this metered access service was designed so that non-subscribers could read a fixed number of articles every month. With the NYT’s technological transformation into a data-driven digital company, we now successfully develop models that intelligently gateway the most worthwhile pageviews in real time within milliseconds. This decision is made to maximize a combination of business objectives such as subscriptions, user engagement, and search visibility, while balancing the tradeoff between them.

This talk will begin with historical context on the digital transformation of The Times and its subscription funnel strategy with users moving from unregistered to registered and subscribed states. Following an introduction of the data science group, we will draw on differences between predictive and prescriptive machine learning models. We will then discuss how Randomized Control Trials (RCTs) can be designed to train causal machine learning models for prescriptive decision making. Modeling details about our real time models for registered and unregistered users will be discussed, highlighting the delicate balance of multiple objectives using Pareto optimization techniques. We will also present engineering technologies utilized at The Times to deploy these models for high throughput inference.