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Predicting subscriber churn at The New York Times

Is Christine Hung, the head of data science and engineering at The New York Times Co., stealing a page from the telecommunications playbook? At the recent CDO Summit in New York City, Hung explained how she’s trying to predict subscriber churn by building predictive models.

Hung, who joined The Times in 2013 after a four-year stint as the global analytics manager of iTunes retail at Apple Inc., is analyzing data The Times has collected, such as a reader’s web browsing history or mobile application usage. “We build a model to predict who is going to cancel their subscription,” she said during a panel discussion.

It’s an example of a predictive analytics application that yields concrete business results. Like any media company, The Times has two revenue streams: advertisements and subscriptions. “If you think about the subscription business,” Hung said, “it’s very hard for us to acquire new customers.” So why not invest more time and energy in keeping the subscribers The Times already has?

The analytics experiment is helping The Times uncover key insights. Hung discovered that readers of general news are much more likely to churn than those who use The Times more extensively. “If you go deeper into the content — if you read a lot of op-eds, if you read a lot of style or travel — you’re more likely to be retained,” Hung said.

That is the kind of insight that helps create more nuanced strategy: Rather than focus on why subscribers leave, The Times can work on how to push general news readers deeper into the product “so that they can really see our value,” Hung said.

Digiart2001|jason.kuffer available under Creative Commons

Digiart2001|jason.kuffer available under Creative Commons

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