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3 months ago
How The New York Times Uses Machine Learning To Make Its Paywall Smarter

Originally published in NYT Open, Aug 10, 2022.  

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.” This strategy has proven successful in generating subscriptions while at the same time allowing for initial exploratory access to new readers.

In fact, in February 2022, when The Times acquired The Athletic Media Company, The Times achieved its goal of 10 million subscriptions and set a new target of 15 million subscribers by the end of 2027. This success has been possible in part due to continuous improvements in the paywall strategy over the years. When the paywall was launched, the meter limit was the same for all users. However, 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.

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