Data Can Help Publishers Understand What Readers Want
What it means for the New York Times to hire a scientist [Source: https://www.youtube.com/watch?v=XWIZOu_XZTs]
Despite the vast amount of digital content available today, I love my daily newspaper. Many readers, like me, continue to enjoy reading newspaper for its depth of coverage and unique experience. However, there is no ignoring the fact that newspaper circulation has dropped significantly over the last three decades.
Publishers are still trying to come to grips with a new generation of digitally-connected consumers, who use non-traditional channels such as mobile devices to access news from social media and apps. At the same time, newer entrants to the publishing market such as The Huffington Post and BuzzFeed are attracting more readers with free, shareable online content, that increases engagement and readership.
Although increasing digitization presents several challenges, it also offers possible solutions. Traditional publishing houses have begun to experiment with new technology-driven approaches to stave off the competition and maintain their leadership. They are realizing the power of data and focusing on data-driven business models, leveraging technologies such as Big Data analytics.
Traditionally, publishers were the ultimate decision-makers who chose and published what they thought readers would want to see. Advanced analytics, however, is rapidly reshaping the way content is created, by providing insights into both direct and indirect reader preferences. It is helping industry players create a more agile publishing model.
In 2012, the University of Bristol studied the types of content that tend to generate the greatest interest among readers. The researchers then used a machine learning technique to predict the appeal of news articles. Insights from such research and analytics can be used to build and adopt new methods such as micro-targeting i.e. segmenting the overall readership and publishing customized content suitable to each segment. In the end, this helps traditional media publications serve their readers better and form more enduring relationships with them.
Interestingly, last year, The New York Times hired Columbia University's applied mathematician Dr. Chris Wiggins to fill its newly created 'chief data scientist' position. With this move, the publication made it clear that it hopes to leverage predictive analytics as a significant part of its business model to gain competitive edge.
Wiggins leads a data team that uses predictive analytics algorithms to understand how people become subscribers, how to influence and attract new subscribers, and even predict who might unsubscribe beforehand. The team also uses natural language processing to understand topics that create more engagement among readers. This helps marketers narrow down their focus on the types of content that is likely to generate more interest.
Identifying valuable content can also help attract a paying audience. Analytics can help news organizations get a clear picture of what content should be made available on a paid model and what can be provided free. By leveraging analytics, The New York Times and other publishers such as The Wall Street Journal are increasing their revenues from paid readers who subscribe to digital only, or a combination of both print and digital.
However, it must be noted that new entrant digital media companies are yet to create a successful business model. They are still a long way from realizing their financial potential. This leaves the field open for traditional media to consolidate their position before it becomes too late.
For traditional media, adopting data-driven business models and new technologies could pave the way for a successful resurgence. Analytics has helped businesses emerge successfully into the new age of news publishing. The key to success lies in changing outdated approaches and helping traditional media organizations view data analytics as a potential solution to address their readership challenges.