In this blog, we will focus our attention on an innovative content decision engine, which selects articles most likely to succeed as premium content. The results are from a three-month pilot project implemented on one of the largest U.S. media brands. The program validates our hypothesis that publishers can achieve significant boosts in revenue and subscriptions with minimal lift.
On average, articles selected by the model and set as premium generated nearly 3X more conversions than comparable articles not set as premium. The impact from the model boosted the expected 2021 starts volume by 20% and topline digital revenue by 8% (both figures forecasted for a twelve-month period).
Everyone agrees the adage is true. However, in practice, publishers undervalue their own content when executing premium content strategies.
North American publishers have experimented setting certain articles behind the paywall irrespective of the user or paywall rule. Most publishers in the U.S. target 10-15% of content for premium. The effective percent of articles set to premium is much lower for most publishers as newsrooms tend toward caution and managing page view risk.
Many newsrooms are also strapped for resources to execute effectively. Systematic workflows and decision criteria are elusive. Decisions are often based on gut feel without thorough data review before or after the decision. Sometimes going viral or reaching the widest possible audience (measured by the ubiquitous “page view”) is just too alluring to keep an article behind the paywall.
By design, the approach by most publishers is bound to leave money on the table. Flagging premium articles is an “opt-in” decision. Any marketer will attest that “opt-in” is guaranteed to perform worse than an “opt-out” approach.
The creation of quality journalism is inherently a human activity. The newsroom is the core of the subscription value proposition. The role of data and the job of any embedded data analyst is that of “informer,” nudging journalists but nevertheless deferring decisions to editors. Being data-informed is indeed powerful and has led to success for many publishers.
Most newsrooms have accepted and even welcomed data into their daily operations. Knowing which articles generate the most subscriptions, page views, repeat visits or subscriber views gives journalists tangible evidence of their efforts.
However, balancing quality journalism and data-driven decisions comes down to which decisions are informed vs. automated. Some are better for humans while others can be unburdened onto algorithms.
Once the journalism is written, the decision of how to monetize does not need to remain solely within the newsroom. The “separation of church and state” has existed for decades for good reason. Marketing tactics, pricing, paywall rules and setting premium content are tactical decisions ripe for predictive modeling and automation.
The diagram above shows the workflow of the premium content engine. Listener™ was integrated with the ARC content management system to capture articles before publication. The predictive model uses taxonomy, metadata and natural language processing to predict how likely an article would succeed when set as subscriber-only. The model is initially trained on several months of history but continues to improve with new data and performance tracked every day. A recommendation is sent via Slack alert within minutes of the article being “ready to publish.”
The chart below shows last-click conversion performance of the articles tested over a three-month period:
*recommended non-premium set as premium were rare and excluded from the chart
The key insight here is the middle bar, which indicates part of the lost opportunity. When implemented fully, setting the recommended articles behind the paywall in this market generated nearly 6 conversions per article. Though the recommended articles not set as premium still performed strongly (2 conversions per article). Similar articles set to premium generated nearly 3X the conversion volume.
The net result to the bottom line was a measured 20% boost in subscription start volume and an 8% boost in net digital revenue (accounting for marginally fewer page views from anonymous users).
One limitation in the implementation of this workflow is the reliance on a Slack alert and follow-through by the receiver to implement the recommendation. Even with an automated alert, many recommended articles were still not set to premium, leaving revenue unrealized.
Mather is continuing to work with the publisher referenced in this blog (and others) to evolve and tighten the newsroom workflow. A next step is to directly set the flag within the CMS or paywall system to ensure full follow-through from the recommendations. Since the launch of the program, Mather has also developed a post-publication model to augment the pre-publication decision engine, ensuring real-time article performance is accounted.
Inevitably, the model of the future will combine advanced audience and content analytics in tandem to optimize subscription value. Technology is catching up to the thought-leadership and analytics.
Mather has been fortunate to support news media brands through significant digital transformation over the last decade. Years ago, Mather introduced the Intelligent Paywall™ but paywall technology at the time lagged capabilities to personalize by user certain parameters, such as meter settings, offers and creative. Over time, multiple paywall companies emerged to enable such functionality.
Like the rapid evolution of paywall technology, Mather anticipates a similar modernization of content management systems, not just in the production of content (see headless CMS) but also in supporting analytics and enabling subscription optimization. A “best of breed” solution will likely define the tech stack of the future.
The summary here is a high-level review of cutting-edge analytics being adopted by leading publishers.
Don’t hesitate to connect with the authors to learn more about how to automate your premium content decisions!
Arvid Tchivzhel is managing director, digital services, and Matt Lindsay is president of Mather Economics.
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