New York Times sees return on investment from descriptive analysis initiative

By Jeremy C. Fox

Editor’s note: This is one of 17 case studies featured in INMA’s strategic report “Making Big Data Smarter For Media Companies,” released in December.  

As one of the premier news media companies, The New York Times has a unique set of challenges and opportunities for engaging its audience. It has embraced and monetised the possibilities that emerging data sources and systems have provided.

“We’ve been using data to enhance [the user] experience, marketing effectiveness, ad sales, all of those things for a very long time,” in both print and digital products, says Sonia Yamada, vice president for consumer insights at The New York Times. “We wouldn’t be doing all that if we weren’t getting a substantial return on our investment.”

Shane Murray, executive director of analytics within the consumer insight group, says The New York Times is deeply involved in descriptive analysis to give its product managers, marketers, and newsroom better information on its audience.

The company is also putting its first-party data to work in making article recommendations at various levels on each of its platforms. It is using data to support search engine optimisation and social media initiatives and to analyse their efficacy, regularly applying both A/B and multivariate testing, and optimising recommendation engines through continuous testing. 

The A/B and multivariate testing has been a big factor in the success of the Times’ online pay model, Murray says, with a marketing team working with analytics and technology staff to continually test and optimise its subscription flow. 

The New York Times uses propensity modeling and churn modeling to identify visitors likely to subscribe and subscribers who are likely to churn, and to predict which subscription bundles will work best with which readers. This has become more important as the company offers a wider range of products.

The Times combines its internal data with third-party data in its data management platform and segments users for ad-serving, satisfying advertisers’ growing desire for better targeting to the appropriate segment of the Times’ large and heterogeneous readership, Murray says. 

To pursue its varied initiatives, The New York Times has data analysts partially embedded across different business units. But those analysts also work closely with each other “to enable the kind of analytic support that’s needed across product marketing, newsroom, and other teams,” Murray says.

Dedicated data analysts are partnered with business and newsroom teams to deliver data-driven insights. A data science and engineering group supports data collection, access, and the automation and scientific rigor of various activities across the business. 

Murray says The New York Times has, in recent years, moved from a more centralised data and analytics structure — with teams in its technology and consumer insight groups working outwardly and consulting with different business units — toward a more embedded model.

Centralisation is beneficial in tool management and ensuring consistent methodology and reporting, but embedding analysts gives the teams greater business understanding and a chance to respond more proactively to problems and opportunities.

While Times staff sees data management as a core competency for the business, staff frequently compare internal capabilities to the best-of-breed third-party products — especially data tools such as clickstream analytics, A/B and multivariate testing, and machine learning tools — to determine whether there may be advantages in embracing some of those tools, Murray says.

About Jeremy C. Fox

By continuing to browse or by clicking ‘I ACCEPT,’ you agree to the storing of cookies on your device to enhance your site experience. To learn more about how we use cookies, please see our privacy policy.