Demonstrating a new sense of relevancy if not urgency for applying data science to a wide cross section of industries, The New York Times has hired Columbia University applied mathematician Chris Wiggins as its first “chief data scientist.” Wiggins, who builds predictive computer models, will now lead a small data science group that uses mathematics to help the 162-year-old publisher maintain or expand its subscriber base – using machine learning to predict who is going to unsubscribe before it happens.
Wiggins says he took the part-time job because he’s a big fan of the publication and wants to help keep its 1,200 editors and reporters in their jobs. He thinks data science can help evolve a business model that has been severely disrupted. A major problem is that advertisers are buying far less newspaper ads. Ad sales for U.S. newspapers collapsed from $47 billion in 2005 to $19 billion in 2012. Online advertising hasn’t come close to making up the shortfall. As a result, the New York Times is depending more on subscriptions. It has raised the cost of the printed paper and is selling digital subscriptions with significant success. This February, the company said it had seen a 19 percent increase in digital-only subscribers during 2013.
The company employs a staff of around 500 in IT, technology, and programming. A year ago the company pulled together information that had been in various silos and formed a business intelligence group to start exploiting it. Wiggins will head a small team of 3-4 people with a mandate to propose data experiments and to determine whether the company’s business problems can be solved with machine learning.
The New York Times has an abundance of data assets—its readers make nine million visits a day to its home page. The goal is to predict when a customer might decide when to cancel based on insights about why people subscribe and how to retain them.
Other traditional media organizations also are making inroads. News Corp., publisher of the Wall Street Journal, recently hired Rachel Schutt (see review of Schutt’s book), another Columbia statistician, as senior vice president for data science, a newly created job title. Schutt will be a centralizing force for a number of machine learning and predictive modeling projects that were already under way.
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