A data-driven approach to paywall strategy

The paywall strategy is crucial to improve conversion rates. In the case of Fast Rhymes, the paywall was only displayed when a user attempted to access a premium feature. This method seemed logical - a user expresses interest in a feature, pays, and immediately gains access. Visually communicated and seemingly user-friendly. However, this is isn't always as effective as it seems. In this article, we will explore the impact of a data-driven strategy on app conversion rates. Learn how Fast Rhymes leveraged A/B testing to increase conversions by 70%.

Why Data-Driven?

You start out with a hypotheses, which in my case was that if I show the paywall to everyone after the first download the app and complete the onboarding, the conversion to paid users will increase. However, there is a lot we don't know, and can't predict. How much will it increase the conversion rate? Will it have a negative effect in terms of bad app reviews? Nobody knows, because every userbase is different. Just because something worked for one app, doesn't mean it will necessarily work in your case. There is only one way to find out, and that's to test it on your userbase.

It's easy to get put off by being concerned about bad app reviews when optimizing for revenue. That's where remote feature toggling and A/B testing is a huge help. If something is not received well, you can easily deactivate it. If it's something you don't want to test on your entire user base, you can split the A/B test group into a smaller audience. This saves you from having to revert the changes, deploying a new version of the app, waiting for app review, then waiting for the users to update their app.


Fast Rhymes was already using Firebase, so it was natural to use their Remote Config service. I created a boolean “showPaywallAfterOnboarding” in the config. Then this was implemented in the app, so the “Get started” button on the last page of the onboarding would open the paywall if this value was set to true.

Next, I utilized the Firebase A/B Testing service, which integrates with their Remote Config service. Set up an A/B test where the primary metric to track was the purchase event (this includes trial starts as well). Then I ran this for about a month. The experiment showed that users exposed to the paywall after onboarding increased the conversion rate by a staggering 70%. With no negative feedback from users.


In conclusion, a data-driven approach to paywall strategy is a great way to test a hypoteses on your userbase, while mitigating the risk of negative feedback from users. Leveraging Firebase's Remote Config and A/B Testing services, the implementation of showing the paywall after onboarding led to a remarkable 70% increase in conversion rates without negative user feedback. This success highlights the importance of empirical testing, emphasizing that what works for one app may not necessarily translate universally, reinforcing the value of tailored, data-driven strategies. To delve deeper into the project, visit fastrhymes.com.

Andor Davoti - 08/01/2024

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