Leveraging AI-powered paywall engines to drive publisher sustainability

Recurring subscription revenue continues to be an increasing piece of the revenue pie for publishers today. For the first time, Mather benchmarking data shows that digital subscribers are now over 50% of paid subscriptions. With print readership on the decline and increasing pressure to drive growth through digital, publishers must optimize their digital acquisition strategy, which comes with its own set of challenges. Advertising revenue is becoming less reliable due to competing platforms and declining budgets, requiring forward-looking companies to adopt innovative paid reader strategies and embrace new technologies to stay relevant and profitable.  

So how are publishers navigating the race to acquire new subscribers in today’s increasingly challenging market for reader revenue? They can boost subscriptions by increasing their paywall stop rate, but at what cost to the user experience and advertising dollars? What is the right balance between driving more subscriptions today vs. cultivating more subscribers long-term? A delicate balance exists and maximizing revenue requires a methodical approach. The implementation of sophisticated paywall engines, including those harnessing the power of AI, has been accelerating across newsrooms globally, with 2024 proving to have the greatest market penetration to date.  

Keeping pace with publisher needs, Mather’s suite of Paywall Decision Engines, powered by the Sophi technology platform, leverages AI to maximize revenue while increasing efficiency, allowing editorial content to deliver the greatest impact to a publisher’s sustainability. Every Mather partnership begins with strategic alignment to ensure the appropriate engine is applied to deliver the winning strategy. 

For publishers who have a freemium model, where the most valuable content is behind a paywall, Sophi’s Content Paywall (SCP) can take this strategy to the next level. SCP utilizes several machine learning models working in symphony to decide if an article should be placed behind the paywall or remain open to support advertising targets. Many publishers task editors with full responsibility for choosing stories to paywall. Sophi can free up this time allowing them to prioritize the creation of more journalistic content.  Sophi’s models are trained to adapt to changing news cycles, monitoring real-time behavior to lock or unlock an article, and it can even identify the best paywall strategy for archive content.    

Publishers with a metered model, or those looking to apply a more dynamic meter, look to Sophi’s Dynamic Paywall (SDP) to maximize revenue from each user that visits the site, removing the guesswork and the one-size-fits-all approach that is common with a traditional meter strategy. SDP is trained to make paywall, regwall, or no-wall decisions based on a series of user characteristics such as device, browser, geolocation, recency, frequency, referrer, and user type. Model-training by ‘micro-segment’ also allows SDP to make decisions without personally identifiable information, eliminating the dependence on a user’s cookies.   

Whether using the Sophi Content Paywall, Sophi Dynamic Paywall (or both!) publishers are empowered to sustainably accelerate subscription growth and mitigate advertising loss. 

For more information about AI-powered paywall solutions, contact us at info@mathereconomics.com

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