The rise in reader revenue in the news industry has been a welcome panacea for our woes — not least the declining print market, our collective reliance on high-traffic advertising and changing reader habits. Over the past 20 years, we have seen a variety of paywall models, including the somewhat elusive “dynamic” paywall, which has seemed to date to be the preserve of only the most sophisticated and technically advanced of news organizations.
But as technology improves and publishers become more proficient in data handling, dynamic paywalls are becoming more commonplace.
The Financial Times launched a dynamic paywall about a year ago, using Zuora as a tech partner. The AI paywall has become a key driver of subscription growth, with a 92% increase in conversion rate, a 118% increase in progression through the subscription funnel and a 78% uplift in subscriber lifetime value. Besides these excellent numbers, the new paywall has also reduced manual overhead, freed up engineering capacity, and empowered the FT to pursue bold new growth ambitions with confidence.
(If you would like to learn more about the FT’s paywall, you can watch our webinar on-demand here, in which FT experts and Zuora discuss what it takes to build and launch a dynamic wall.)
During the INMA Subscriptions Summit this year, it was highlighted that 22% of 264 news brands declared using a hybrid / dynamic / smart paywall, the proportion of which has increased four times since 2020.
In our work with publishers around the world, FT Strategies has seen how paywalls tailored to content preferences, user behavior and demographic data can more effectively convert engaged readers while retaining those with lower willingness to pay through ad revenues, compared to a one-size-fits-all paywall.
As INMA highlights in its article on dynamic paywalls: “For years, dynamic paywalls were a bit like driverless cars — everybody talked about them, but only few had seen them.” Similarly, everybody seems to have different ideas of what dynamic paywalls actually do. So, this blog series will walk you through what to consider and how to build your dynamic paywalls. First of all, let’s start with some simple questions facing publishers looking to introduce or optimize a dynamic paywall.
A dynamic paywall is a system that adjusts both when to show a paywall and what to show on it. Dynamic paywalls exist on a spectrum: at one end are rules-based systems that rely on pre-set rules and reader segmentation, while at the other are what Jonathan Harris from Zuora calls "self-learning" AI paywalls that leverage multi-agent reinforcement learning.
The key difference between a classic paywall and a dynamic one is that the former is triggered based on a fixed rule applied to all readers (e.g., a standard paywall appears after three articles within a 30-day period), while the latter is triggered at different times or with different subscription offerings based on user behavior and characteristics.
As Harris explains: "The key difference with true AI paywalls is that they learn and adapt automatically. Rules-based systems, even those that use propensity scoring, require constant manual adjustments — you're always chasing audience behavior. With reinforcement learning, the system explores what works and doubles down on successful patterns, optimizing for long-term subscriber value rather than just immediate conversions."
What elements become dynamic can vary by case, depending on data availability, user needs and historical trends. Some of the elements that make a paywall dynamic include the following:
Rules-based approaches are often a practical starting point, particularly when data is limited or internal capabilities are nascent. More advanced systems using AI enable real-time personalization and optimization at a large scale.
To dynamically adjust the elements mentioned above, publishers often build or refine a machine learning model, typically a classification model, to predict how likely a user is to subscribe based on available behavioral and contextual data. This model then triggers tailored responses (e.g., locking content, adjusting call-to-actions) in real time or near real time. Clean and structured data is essential for ensuring the model generates accurate and effective predictions. The specific dataset used to train the model will depend on data availability, but a typical minimum set of data includes:
In the process of training a model and creating preset rules for a dynamic paywall, other types of data, especially first-party data such as age, location and work role, can offer significant advantages to news publishers. As many in the industry recognize, first-party data holds enormous potential, and with the right strategies, publishers can unlock substantial value from it. The Wall Street Journal, a pioneer in dynamic paywalls, uses more than 60 user signals, while the FT’s latest experiment orchestrates across about 250 user states to inform its AI-powered paywall, highlighting the scale and diversity of data required for effective personalization.
A clear understanding of dynamic paywalls helps you properly scope your project, while clean datasets are a key enabler of long-term success. With this foundational understanding in place, the next blog post will walk you through how to build and optimize a dynamic paywall with a step-by-step guide.
About FT Strategies:
At FT Strategies, we offer a range of support services that news publishers can leverage to optimize their paywall strategies. These include building data infrastructure suited to dynamic paywalls, advising on Build or Buy decisions, supporting vendor selection, developing predictive financial models and implementing your paywall strategy. To find out more about how we can support you, please get in touch with our expert team today.
