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Every organization applying artificial intelligence (AI) and machine learning (ML) to their business is looking to use these powerful technologies to tackle thorny problems. For the New York Times, one of the biggest challenges is striking a balance between meeting its latest target of 15 million digital subscribers by 2027 while also getting more people to read articles online.

These days, the multimedia giant is digging into that complex cause-and-effect relationship using a causal machine learning model, called the Dynamic Meter, which is all about making its paywall smarter. According to Chris Wiggins, chief data scientist at the New York Times, for the past three or four years the company has worked to understand their user journey and the workings of the paywall.

Back in 2011, when the Times began focusing on digital subscriptions, metered access was designed so that non-subscribers could read the same fixed number of articles every month before hitting a paywall requiring a subscription. That allowed the company to gain subscribers while also allowing readers to explore a range of offerings before committing to a subscription.

Now, however, the Dynamic Meter can set personalized meter limits. By powering the model with data-driven user insights, the causal machine learning model can be prescriptive, determining the right number of free articles each user should get so they seem interested enough in the New York Times to subscribe in order to continue reading.

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According to a blog post written by Rohit Supekar, a data scientist on the New York Times algorithmic targeting team, at the top of the sites subscription funnel are unregistered users. At a specific meter limit, they are shown a registration wall that blocks access and asks them to create an account. This allows them access to more free content, and a registration ID allows the company to better understand their activity. Once registered users reach another meter limit, they are served a paywall with a subscription offer. The Dynamic Meter model learns from all of this registered user data and determines the appropriate meter limit to optimize for specific key performance indicators (KPIs).

The idea, said Wiggins, is to form a long-term relationship with readers. Its a much slower problem [to solve], in which people engage over the span of weeks or months, he said.

The most difficult challenge in building the causal machine learning model was to set up the robust data pipeline that helps the algorithmic targeting team understand activity for over 130 million registered users on the New York Times site, said Supekar.

The key technical advancement powering the Dynamic Meter is around causal AI, a machine learning method where models are built which can predict not just will happen, but what would have happened.

Were really trying to understand the cause and effect, he explained.

If a particular user is given a different number of free articles, what would be the likelihood that they would subscribe or the likelihood that they would read a certain number of articles? This is a complicated question, he explained, because in reality, they can only observe one of these outcomes.

If we give somebody 100 free articles, we have to guess what would have happened if they were given 50 articles, he said. These sorts of questions fall in the realm of causal AI.

Supekars blog post explained that its clear how the causal machine learning model works by performing a randomized control trial, where certain groups of people are given different numbers of free articles and the model can learn based on this data. As the meter limit for registered users increases, the engagement measured by the average number of page views gets larger. But it also leads to a reduction in subscription conversions because fewer users encounter the paywall. The Dynamic Meter has to both optimize for and balance a trade-off between conversion engagement.

For a specific user who got 100 free articles, we can determine what would have happened if they got 50 because we can compare them with other registered users who were given 50 articles, said Supekar. This is an example of why causal AI has become popular: There are a lot of business decisions, which have a lot of revenue impact in our case, where we would like to understand the relationship between what happened and what would have happened, he explained. Thats where causal AI has really picked up steam.

Wiggins added that as more and more organizations bring AI into their businesses for automated decision-making, they really want to understand what is going on, at all angles.

Its different from machine learning in the service of insights, where you do a classification problem once and maybe you study that as a model, but you dont actually put the ML into production to make decisions for you, he said. Instead, for a business that wants AI to really make decisions, they want to have an understanding of whats going on. You dont want it to be a blackbox model, he pointed out.

Supekar added that his team is conscious of algorithmic ethics when it comes to the Dynamic Meter model. Our exclusive first-party data is only about the engagement people have with the Times content, and we dont include any demographic or psychographic features, he said.

As for the future of the New York Times paywall, Supekar said he is excited about exploring the science about the negative aspects of introducing paywalls in the media business.

We do know if you show paywalls we get a lot of subscribers, but we are also interested in knowing how a paywall affects some readers habits and the likelihood they would want to return in the future, even months or years down the line, he said. We want to maintain a healthy audience so they can potentially become subscribers, but also serve our product mission to increase readership.

The subscription business model has these kinds of inherent challenges, added Wiggins.

You dont have those challenges if your business model is about clicks, he said. We think about how our design choices now impact whether someone will continue to be a subscriber in three months, or three years. Its a complex science.

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How machine learning helps the New York Times power its paywall - VentureBeat