Digital Publishing Reader Revenue Top Stories
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How dynamic paywalls help publishers connect potential subscribers with the right offer at the right time

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This article is an excerpt from our special insight report, Paywalls for Publishers. This free-to-download report aims to help you formulate an effective paywall strategy via insights and examples that have worked well for other publishers. The guide also examines how you can leverage your first-party data and build a supportive organizational culture to boost your paywall strategy.

Dynamic or intelligent paywalls have great potential and are being used by many publishers successfully. John Wilpers, author of FIPP’s annual Innovation in Media report called dynamic paywalls the “hottest new tool” that are helping publishers secure, “significant sustainable reader revenue.” 

“It’s amazing, (mind-boggling, actually) to think that in an era of increasing personalization we ever thought a one-size-fits-all paywall would work,” he wrote in the report. 

“How each story can best contribute to the business”

Dynamic paywalls use machine learning and artificial intelligence to adapt to users’ behavior and restrict access to content accordingly. They can automatically alter article limits and even deliver personalized subscription pitches based on readers’ consumption habits. 

For example, a reader who avidly consumes sports content from a publisher using a dynamic paywall may see the paywall go up after consuming a lesser number of sports-related articles compared to other topics. Around one-quarter of respondents subscribe because they want access to specific topics they can only get after paying, according to research from American Press Institute’s Media Insight Project.   

While meters assume that each story’s contribution toward a user’s eventual subscription is in some sense the same, freemium and dynamic models let us think about how each story can best contribute to the business. 

Josh Schwartz, CTO of Chartbeat

Intelligent paywalls can also help optimize your stop rates i.e., increase the percentage of users who are stopped from accessing content and asked to subscribe. This is important as research by the Lenfest Institute of Journalism found that the majority of the publishers lag behind thriving news organizations in their stop rate. 

A news organization’s stop rate often distinguishes high-performing publishers.

Digital Pay-Meter Playbook

The fiftieth percentile of publishers from 500 news organizations in the Lenfest study stopped only 1.8% of their readership with a paywall or meter. In contrast, publishers with “sustainable” digital businesses (between the 80th and 90th percentiles of all publishers studied) reported stop rates at, or above 4.2% of all readers. 

Further, publishers reporting more than 6% of unique visitors reaching their stop threshold had “thriving” digital subscription businesses.

“Maps a more relevant digital subscription offer

A critical limitation of simple paywalls, whether metered, freemium or hybrid, is that they behave in the same way regardless of individual users’ predilections. “They put the content first, rather than the user,” says Gosen, “placing the burden of generating subscriptions on the content alone, which in turn ignores the huge boosts in subscription growth that data-driven, real-time personalization can offer.

“In sharp contrast, a dynamic paywall will be constantly asking questions such as: which readers are most likely to convert to subscribers? Which readers should receive which kind of subscription offer? And when should readers receive an offer? This type of paywall will use the answers to these questions to constantly evolve and bend at will to suit the needs of a publishers’ readership, with no single consumer treated the same.”

Dynamic paywall seeks to identify particular behaviors or audience segments and maps a more relevant digital subscription offer.

Michael Yeon, VP, Admiral

This ability to use real-time insights and adapt to user interests and needs makes dynamic paywalls more effective in building engagement and driving subscriptions. 

Many publishers use paywalls that can assess a user’s propensity to subscribe. This information is used to pitch them the right offer at the right time. “If you recognize someone’s behavior, then it’s easier to figure out at what point you put that wall in front of them,” says Nash. 

We use a dynamic paywall to better target people with the right offers. Maybe someone needs just one more extra article. We use the dynamic paywall to find the right moment in time.

Lindsay Horrigan, GM, Consumer Subscriptions, Bloomberg Media

The Wall Street Journal, one of the earliest publishers to use a paywall, has evolved its earlier freemium model into a highly sophisticated dynamic paywall that measures reader activity across 65 variables. 

They include frequency, recency, depth of read, preferred content types, and favored devices among others. The algorithm takes all of these into account to calculate readers’ “propensity score” or their probability to subscribe. The paywall is adapted according to each reader’s behavior, placing limits on the number of free stories, especially in the areas of their interests. 

“[We have] an intelligence layer, which became a propensity model,” Karl Wells, GM of Membership, Subscription Sales and Marketing, WSJ explained in a Digiday interview.  “It was the driver of determining where you are in the purchase funnel. You come to [the website] we’ll give you a score. That score will determine what your experience will be.

“The first-party data is the most powerful. Your visitation count plays a big role, the device type you’re on — desktop, Android, iPhone — play a role and the type of content you consume can play a role. The third-party data like where you live has a huge bearing on whether you will subscribe or not.” 

“[An adaptive paywall] allows you to pick the threshold based on past patterns of engagement. If there is a certain type of reader who will most likely convert on their fourth visit and another type who most likely converts on the seventh, the model will reflect that.” 

John Wiley, Director, Data Science & Insights, The Wall Street Journal

Swiss newspaper, Neue Zürcher Zeitung (NZZ) has been very successful with dynamic paywalls. Like the Journal, NZZ uses many variables to personalize its paywalls for readers, and calculate their propensity scores. 

“We only want to disrupt when someone is willing to pay”

It uses 100 to 150 of them, including reading history, time spent on articles, frequency, device, newsletters they receive, and the times they visit. The publisher uses this data to tailor its messaging, text, placement, timing and even the color of the pay prompt readers see.

“We play around with the threshold,” Steven Neubauer, former MD, NZZ told Digiday. He explained that people can see payment messages after they have read five, eight, 11 or 13 articles. “Ultimately, the goal is to not disrupt the product experience; we only want to disrupt when someone is willing to pay. 

“You will never hit 100 percent success. Sometimes there is the need to indicate this is a pay product — ideally only when the lead is warm enough, willing to pay, and it’s an offer that fits the needs they are looking for.”

Machine learning and AI take the guesswork away from many critical decisions like how many, or which stories you can allow users to access freely. Insights gained from the data will give you a reasonably good idea about which readers are likely to subscribe after hitting a paywall and the ones unlikely to do so. You can use this information to target each type differently. 

If I get a person’s score, I pretty much know how likely they will be to subscribe.

Karl Wells, GM of Membership, Subscription Sales and Marketing, WSJ

“If you think about paywalls broadly, there have been metered, freemium, and hard paywalls. Metered considers people who will want to read more than, say, five stories. Freemium assumes this and not that is the type of content people will pay for,” Wells told NiemanLab. “This is what we’ve tried to move on from. Our model now is to flip that and start with the reader. The content you see is the output of the paywall, rather than an input.”

Dynamic paywalls will also make better sense in a cookie-less future. They are repositories of first-party data—the source of rich and nuanced audience insights that can help optimize your audience and traffic, and maximize revenue potential.

“Advanced analytics is now key to acquisition, activation and retention, the future of paywalls will be automated based on data-driven insights.”

Grzegorz (Greg) Piechota, Researcher-In-Residence at INMA

Originally published in What’s New in Publishing earlier. While some of the data points may have evolved, the analysis and conclusion remain highly relevant.