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 “metred” access service has been designed so non-subscribers can read a fixed number of articles every month; this article limit is called the “metre limit.”

This strategy has successfully generated subscriptions while allowing 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.

When the paywall was launched, the metre 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 personalised metre limits and make the paywall smarter.

Our paywall strategy

The company’s paywall strategy revolves around the concept of the subscription funnel. At the top of the funnel are unregistered users without an account with The Times. Once they hit the metre limit for their unregistered status, they are shown a registration wall that blocks access and asks them to log in or make an account with us.

Doing this gives them access to more free content and allows us to better understand their current appetite for Times content through their ID-linked reading history. Once registered users hit their metre limit, they are served a paywall with a subscription offer.

This is the moment the Dynamic Meter model controls. The model learns from the first-party engagement data of registered users and determines the appropriate metre limit to optimise for one or more business KPIs (key performance indicators).

What it optimises for

The Dynamic Meter model must play a dual role: It should support both our mission to help people understand the world and our business goal of acquiring subscriptions. This is done by optimising for two metrics simultaneously — the engagement that registered users have with Times content and the number of subscriptions the paywall generates.

These metrics have an inherent trade-off since serving more paywalls naturally leads to more subscriptions but at the cost of article readership.

We measure this trade-off by setting up a randomised control trial (RCT) that randomly assigns users different metre limits. For larger metre limits, average engagement gets larger and is accompanied by a reduction in the conversion rate for subscriptions.

Conversely, a larger amount of friction due to tighter metre limits also impacts readers’ habituation and they are less interested in our content. In essence, the Dynamic Meter must optimise for conversion and engagement while balancing a trade-off between them.

How it works

The Dynamic Meter is a prescriptive machine learning model that learns the causal effect of assigning different meter limits on each user’s engagement and subscription likelihood. A weight factor combines these two objectives and can be adjusted based on business requirements, allowing the model to change the desired conversion rate flexibly. The model is trained using historical RCT data and its performance is assessed by comparing its KPIs with the RCT.

This strategy allows us to tune the level of friction based on our business goals and, at the same time, smartly target users so we can obtain a lift in engagement and conversion rate compared to a purely random policy.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .