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How to Use Machine Learning for Your Paywall Strategy

Updated: Mar 10

Tired of static paywalls? Enter machine learning and dynamic paywalls.

Machine learning has turned into a buzzword, but that doesn’t erase the sheer potential of implementations in the realm of subscription models and dynamic paywalls.

What’s great about dynamic paywalls is the basic assumption of the individuality of your readers. So keep on reading to know all about the essential machine learning components, as well as how they power dynamic paywalls.

Machine Learning Components

Before delving into what the important components of machine learning are in the context of dynamic paywalls, let’s give a quick overview of what machine learning is all about.

In the simplest of terms, Machine Learning (ML) is a field of study that focuses on giving computers the ability to learn without being ‘explicitly programmed.’

Once you teach a machine the general ability to learn, it becomes —in the common vernacular— smart.

Sounds good, but what does that do for dynamic paywalls?

Well, it transforms static paywalls into paywalls that behave in different ways according to the readers’ behaviors on your website. For example, if your ML-powered paywall detects that a specific reader of yours is on the edge of unsubscribing from your service, it’ll make the decision of sending them a coupon that would increase their likelihood of staying subscribed for longer.

In another case, the action would be sending them relevant content according to their latest reading interests on your platform.

Which brings us to the components that are involved in such actions.

1. Content Recommendations

Assigning specific interests to reader groups is an extremely powerful tool that allows dynamic paywalls to become effective at raising your number paying subscribers as well as increasing their likelihood of becoming die-hard fans of your content.

It’s done by recommending relevant content based on your reader’s historical data. An example would be a segment of your football fans who keep getting relevant content recommended to them at the end of every content they consumed.

Naturally, they keep consuming more of your content, which —in turn— keeps their interest alive and the time spent on your platform will increase as well.

2. Prediction Scores

This component is a pillar of the machine learning foundations. It’s about calculating predictions. Then, converting them to the likelihood of certain events and actions occurring.

For dynamic paywalls, it’s all about user behavior. There are specific statistics that a dynamic paywall looks at. An example would be the user’s time spent on your website, the rate of visits in a week, and the amount of content they read.

Once you combine all of those factors together, you get a solid correlation between time spent and a user’s likelihood of subscribing as well as churn scores.

An important thing to note would be the difference between correlation and causation. Thankfully, due to the ever-changing and adapting nature of dynamic paywalls, what would have been previously just correlation numbers are now becoming firmly in the causation camp.

With Pelcro providing time series models for prediction scores, it’s tailored for the publishing industry. In addition to propensity scores, subscriber likelihood scores, and churn likelihood scores, you’ll be able to create an effective customer journey.

3. Personalized User Experience

Nothing can feel more empowering than logging onto a site, and having three articles pop up that you’re almost salivating to read.

This can only happen if the platform is dynamic enough to respond to an individual’s distinct preferences. Keeping in mind the years of social media algorithms almost spoonfeeding us personalized content. Thus, readers’ expectations are raised higher than ever.

Raised expectations birth the opportunity for publishers with AI-powered platforms to shine brighter and soar over the competition. As it were, for the majority of the industry’s history, publishing has been —at its core— a top-down mass media experience.

The publisher decides what’s important, so they’re set as a priority by putting them on the first page. In this day and age, we’re moving towards a personalized approach based on individual users

Thus, when you opt-in to personalization, your readers become much more than just a pair of cruising eyes. They become paying customers, and bit by bit, you can establish consumption habits like daily newsletter reading. You become a relevant part of their daily lives.

Here’s an example of the effects of this shift. Let’s assume you have a movie magazine, and you’re in the film critiquing industry.

Natalie has just read a couple of articles about films in the action genre. Then, your dynamic paywall asks for their email address in exchange for two additional free articles. Afterward, Natalie starts receiving a daily newsletter for your action films category.

They begin seeing value in your content and decide to sign up for your yearly digital subscription. Your AI-powered platform sends them two free tickets to the latest action blockbuster. Natalie grabs a friend who becomes rather interested in your services.

Natalie’s friend signs up for your annual subscription three days later. That’s the power of personalized user experience.

We’re Ready for the Future of Publishing, What About You?

Machine learning has moved from the realm of sci-fi movies to our day-to-day lives, yet in most cases, we’re unaware of the sheer amount of possibilities it can provide for publishers.

However, now that you know all about machine learning elements that are used in our dynamic paywalls, it would be a shame not to benefit from its capabilities.

As we always say at Pelcro, it’s only limited by your imaginations. Wondering where to start? Just contact us, and we’ll set you right up.


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