3 Ways Publishers Are Using AI to Meet Reader Expectations
Companies like Netflix, Amazon, and Spotify are raising the bar on consumer expectations for personalized experiences. These companies are using technologies like artificial intelligence (AI) to provide customized recommendations to subscribers and customers in real-time. Consumers have come to appreciate this approach and now expect other brands — including publishers — to cater to their individual preferences.
Responding to consumer expectations, publishers are embracing AI to tailor content to their readers across distribution channels with the aim of increasing audience engagement, growing readership, and boosting ad revenue. Some areas where AI is proving specifically helpful for publishers include content categorization, data-driven content personalization, and targeted marketing.
AI and machine learning (ML) technology are being used to classify content to make it more easily sortable and searchable. By optimizing the accuracy of search results, publishers can boost content visibility and engagement. Large publishers, in particular, are increasingly relying on this application of AI to meet the challenge of classifying and organizing the sheer volume of content they’ve been creating for years.
AI technology allows publishers to scan an article and create tags based on the content to apply structure to unstructured information and make content easier to find and use. This process can also be applied to images, video, and audio. Additional technologies like speech-to-text, ML, and visual recognition software are making images, video, and audio more discoverable than ever.
Better organized content is easier for publishers to repackage and monetize with readers and advertisers. Because it's easier to sort and search, publishers are able to mine their archives to compile special anniversary issues or themed collections that give existing content new life, providing monetization opportunities and value to readers.
Data-Driven Content Personalization
One of the most significant ways publishers are using AI is by monitoring reader behavior and suggesting relevant content. A 2018 survey by the Reuters Institute for the Study of Journalism found that 60% of publishers use some form of AI to improve content recommendations.
News outlets like The Wall Street Journal and The New York Times are taking a page out of Netflix’s book by using AI-powered recommendation engines to anticipate the content a specific reader is likely to engage with, and then serving related content to that reader accordingly. Through its My WSJ feature on mobile, The Wall Street Journal delivers a “recommended” list of stories to readers derived from their viewing history. The New York Times’ recommendation module, Editors’ Picks, uses contextual bandit tools to make article recommendations based on reader geographic location. These regionally relevant recommendations have allowed the Times to increase the click-through rate on the Editors’ Picks module by 55%.
As audio becomes a preferred format for content consumption, publishers are also finding ways to use AI to custom-tailor user experiences with audio assets. For example, by analyzing the author name, audio content can be delivered to listeners in the gender of the content author. This application can even be extended to reading article content in the language of the listener.
Leveraging real-time reader insights is also helping publishers become more agile in their marketing strategies. For example, AI enables publishers to leverage data on reader behavior to create customized emails, newsletters, and ads for each individual subscriber. The technology makes it possible to determine email subject lines, body copy, and images that will resonate with each reader and automates the process, delivering the right content to the right reader at the right time.
The New York Times is using AI to develop email newsletters that serve as marketing tools for developing one-on-one relationships with readers. The media company, which offers more than 65 newsletters, found that readers who subscribe to the Times’ free newsletters are twice as likely to become paying subscribers. Its Your Weekly Edition uses machine learning algorithms to deliver personalized newsletter content; articles are algorithmically selected based on content readers have read, as well as stories that other Times readers found interesting.
The Wall Street Journal is using AI to power paywalls that adapt to visitor behaviors. These paywalls, sometimes called intelligent paywalls or flexible paywalls, use machine-learning algorithms to track variables like visit frequency, preferred content, and preferred devices to determine how many articles and what kind of articles visitors can read for free. Using AI helps the publication better communicate the value of a subscription and make its subscription model more predictive.
The media industry's embrace of AI-driven personalization is boosting publishers' bottom lines through growing subscriber bases and increased ad revenues. A report by McKinsey Analytics helps quantify this, revealing that AI is projected to add a value of more than $448 billion to the industry. It is easy to predict that publishers will continue looking for new ways to leverage AI and provide increasingly targeted, hyper-personalized reader experiences.
Paul DeHart is Co-owner and Chief Executive Officer of BlueToad, Inc. In 2007, he and a group of partners launched BlueToad. BlueToad has grown over the years into one of the leading distributors of digital content, with more than a billion page views a year, and it has helped thousands of content creators build audience relationships around the globe.