How to Optimize App Paywall with A/B Testing

This article gives a clear introduction to paywall A/B testing and a step-by-step guide to conduct one to optimize your app paywall.

An app Paywall is a virtual gate that restricts access to monetized services. It can either allow partial access to the application or restrict access altogether without proper payment. The app paywall is of critical importance in subscription-based applications. The partial access paywall is known as a “soft paywall”. A large number of news and online learning services use this model. This model provides a small demo or trial version so that the user gets a sneak peek of the product before charging for full access. The other model is called the “hard paywall”. In this case, the user does not get any kind of free access, the services are completely blocked off without a subscription. This model is most suited for highly unique and exceptional services that have the demand and the pulling power to make subscribers pay upfront. 

What is A/B testing for App Paywall?

A/B testing is an experimental method that juxtaposes the performances of two or more methods for a particular variable to discover the best profitable monetization hypothesis of your subscription app (better conversion rates). The variant tested is called control and the supposed efficient variant is called treatment. 

Therefore, App paywall A/B testing is used to discover the best elements, designs, and pricing strategy of your subscription monetization model. Optimizing the paywall is proving to be a really reliable method of increasing the subscriber base. To understand the best practices that ought to be followed to gain a greater subscriber base, we have to dwell deeper into the importance of paywall.

Importance of paywall for in-app revenue

Multiple claims convey that paywalling applications are one of the most foolproof and infallible methods to generate revenue with successful instances backing up the claims. The methods are as follows:

  • Provides consistent revenue: Subscription-based revenue is a method that ensures a fixed amount of payment in a given time frame while also increasing the customer retention rate in comparison to a single payment model. Customers also benefit by not needing to act on their invoices every billing cycle. This subscription-based system also increases the loyalty of users and hence they will continue to subscribe to the newer schemes introduced.

  • Provides a sense of quality: Paywalls are implicitly associated with high-quality services with proper security. This prompts organizations that require reliability and quality to opt for applications with paywalls.

  • Greater revenue than onClick ads: Onclick ads sound like a really good and surefire way to generate revenue but it requires an astronomical amount of clicks on the ad to get a nominal revenue. Even a nominal paywall can secure more revenue than Advertisements.

How to Conduct A/B Testing to Optimize App Paywall

A/B is a cyclical process that can be used to continually improve the application and the quality of services. The steps are as follows:

Step1: Hypothesis

A proper hypothesis needs to be developed to understand the requirement of the paywall and plan the blueprint accordingly. The available information must be properly analyzed and data-driven decisions are made in order to achieve a paywall that has the features that attract users.

Step2: Subscription pricing strategy

Choosing the appropriate pricing for the application. The pricing must be carefully decided so that it neither drives away potential customers nor makes them underpay for the services. A/B testing is highly important in identifying this price range. With proper A/B testing, the optimal price point ought to be found.

Step3: Device preference

Determining the device mostly used for subscriptions is of high importance. The entire user journey and user experience change depending on the device used. So different default paywalls can be used for different devices to increase subscription rates.

Step4: Content outside paywall:

With all the information gathered with A/B testing, we must strike the right equilibrium between the content inside and outside the paywall. The content outside must be catchy enough to attract users while not giving away too much information. This combination when done right helps increase the subscriber base.

Step5: Review

The acquired outcome is reviewed to ensure that the outcome was similar to the requirement stated by the hypothesis or if minor or major changes need to be implemented.

Step6: Where and when to display the paywall

There is an optimal place and time to display the paywalls. Displaying it too soon and too frequently might have an adverse impact on the users’ mindsets but displaying it late and scarcely could mean that too much content has already been given, hence it lowers the paid content’s value. A proper equilibrium must be struck between the two parameters to get the best results.

Step7: Repetition

There is always a constant scope to improve. The process is cyclic and hence with new requirements, a new hypothesis can be framed and the necessary features can be added, hence further enhancing the application and its services. is the perfect data analysis tool as it can conduct paywall A/B testing and makes A/B testing process smooth. The most important advantage is that it can divide the users into uneven percentages, such as 33% to 67%. also provides data-driven suggestions, which help us better grasp the customer's mindsets, hence being able to ensure the best possible paywall is used.


Tremendous amounts of effort are expended to develop applications. Even with the application being technically sound, a proper paywall is essential to ensure a regular flow of income and also increase the loyalty of the users. Understanding the concept of A/B testing is critical in optimizing the paywalls, hence improving the in-app revenue. I hope this article provided a good insight into ways to Optimize App Paywalls with A/B Testing.

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How to Optimize App Paywall with A/B Testing

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