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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

The WSJ dynamic paywall

Chris Boyd (The Wall Street Journal), John Wiley (The Wall Street Journal)
13:45–14:25 Thursday, 11 October 2018

Who is this presentation for?

  • Those working at media organizations or those involved in machine learning or propensity modeling

What you'll learn

  • Learn how the WSJ leveraged machine learning to develop and deploy a dynamic paywall based on propensity modeling and customer data


Throughout recent history, paywalls have had one thing in common: access rules are determined by content rather than customer data. Essentially, whether a prospective subscriber has visited a publisher multiple times or they are a first-time visitor, the experience they receive is the same.

The Wall Street Journal wanted to create a new paywall that met three key objectives:

  • The capacity to deal with 15M visitors a week
  • An experience that is dynamic and could flex depending on the users likelihood to subscribe, moving away from giving everyone the same experience (e.g., five articles free per month)
  • The ability to increase subscription sales without impacting advertising capacity

Chris Boyd and John Wiley explain how the Wall Street Journal created a “dynamic paywall” that uses machine learning and a proprietary algorithm to predict the likelihood for someone subscribing—recognizing where they are in the “purchasing funnel.” This likelihood to subscribe dictates the paywall experience the customer receives. This takes place in real time, up to 15M times per week.

Photo of Chris Boyd

Chris Boyd

The Wall Street Journal

Chris Boyd heads up The Wall Street Journal’s digital advertising and membership product management and engineering efforts.  Chris spent most of the last 10 years leading the engineering organization behind the website as consumer technology and news consumption habits have gone through tremendous change.  The last few years Chris led efforts to create an award winning customer and data driven paywall and introduced machine learning models to power customer engagement that has lead to The Wall Street Journal’s highest levels of membership ever.  Prior to his work at the Journal, Chris worked in consulting as a technical architect of mission critical enterprise applications across a variety of industries.

Photo of John Wiley

John Wiley

The Wall Street Journal

John Wiley is a data scientist at the Wall Street Journal, where he manages a team focused on applying predictive analytics in the journal’s membership business. In collaboration with the journal’s product, design, and engineering (PDE) team, he helped develop a suite of machine learning applications enabling the dynamic targeting of paywall experiences based on a reader’s probability of subscribing. The project was recognized by the International News Media Association (INMA) as the best new paid content or subscriber initiative by a global publisher in 2018. John holds a bachelor’s degree in information systems and business analytics from Boston College’s Carroll School of Management.