Dynamic Paywalls for Streaming Services
What would happen if we started to have dynamic paywalls for streaming services? “AI could start to optimise paywalls and subscription offerings based on my [user] behavior so that it could maximise revenue and minimise friction for our user experience,” says Liat Ben-Zur, CEO of LBZ Advisory.
Leveraging user data, machine learning, and generative AI could create offers based on consumption patterns. Some companies are dabbling in this, but now we have the technology to really start developing it.
“Everyone’s talking about workflow automation and efficiency,” Ben-Zur says. But the more interesting discussions concern business model innovations. “How can we use AI not just to improve what we’re already doing today, but also to find new content syndication opportunities?”
We need to look at personalizing content licensing, in which users can access and pay for content based on specific needs or build AI-powered content marketplaces where big media companies might leverage generative AI to connect content creators with relevant audiences and facilitate new transactions. “All the business models, all the ways you reach your customers, all the platforms of the past are still the primary platforms of today,” Ben-Zur says. “AI is either disrupting it or accelerating some of the efficiencies. What we haven’t yet cracked is how to use AI to innovate with business models.”
Dynamic paywalls can adjust based on a viewer’s propensity to view. Roku will supply content ad free if you’ve watched enough other episodes of a show in one sitting. Other services have premium subscription levels to provide early access to new shows.
Any service that chooses to use dynamic paywalls could A/B test to see what consumers would be interested in, whether it’s specific content, removing ads, gaining early access to content, or obtaining other exclusive privileges. A data loop could provide more details on what a person or group would consider valuable. Right now, the data loop consists of “I watch your content, I churn, and you never know why.”
Backlash follows when subscription prices rise. Consider how testing various other revenue-building strategies could create goodwill if done correctly. This could increase consumer engagement and find out what they value and what they don’t. The data collected could transform a streaming service’s business.
Lots of companies and industries use dynamic pricing, such as airlines, Amazon, Uber, or even advertising, where the value of CPMs depends on various factors—type of content, viewer demographics, location, time of day— and the price can rise when bidders decide that placement is worth more.
Piano uses generative AI to help companies understand consumer behavior. It provides a ranking of page views based on expected ad revenue versus subscription revenue, helping to identify the best segments for targeting with a paywall or allowing free access. This “revenue auto optimization” uses propensity models based on user and content types. The company also works with subscription and advertising revenue data. This is the kind of insight the streaming industry needs.
Piano and Digiday’s 2024 “The State of Publisher Revenue” report surveyed 76 publishing professionals. They found that respondents planned to use ad revenue optimization platforms, machine learning user propensity models, and AI-powered personalization engines in 2024. In addition, 82% said they planned to use adaptive and dynamic pricing, such as paywalls and ad optimization.
An FT Strategies study of 35 publishers across Europe and North America found that the use of dynamic paywalls has grown to 20% in the past 5 years. “This is most prevalent in North America, with dynamic models that lock content and/or showcase different prices based on user engagement [such as The New York Times, The Wall Street Journal, or The Globe and Mail].”
Offerings need to be consistent, transparent, and designed to serve the viewer. Anything else will make consumers complain the same way they do when subscription prices rise.
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