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How ML Helps The New York Times Power its Paywall

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By powering the new Dynamic Meter 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 subsc

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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.

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