If You Already A/B Test Your Paywall, Why Not A/B Test Your Onboarding?

If you are running a subscription app, there is a good chance you are already A/B testing your paywall. You have tested different price points, different layouts, different copy, maybe even different trial lengths. And you should be. The paywall is where money changes hands, so it makes sense to optimize it.

But here is the question nobody seems to ask: how many users actually see your paywall?

The funnel above the paywall

Your paywall does not exist in isolation. Users have to go through your entire onboarding flow before they get there. And if your onboarding is losing 60% of users along the way, you are optimizing a paywall that only 40% of your users will ever see.

Let me put some numbers on this. Say you get 10,000 new users a month. Your onboarding completion rate is 35%. Your paywall conversion rate is 8%. That gives you 280 paying users per month.

Now say you run a bunch of paywall experiments and get that 8% up to 10%. Great work. You are now at 350 paying users. A 25% improvement.

But what if, instead, you spent that same effort optimizing your onboarding and got the completion rate from 35% to 50%? At the original 8% paywall rate, you are now at 400 paying users. A 43% improvement. And you have not touched the paywall at all.

Now do both, and you are at 500 paying users. Nearly double where you started.

Why teams optimize the paywall first

There is a practical reason for this. Paywall testing tools are mature and widely available. RevenueCat, Adapty, Superwall, and others make it easy to create paywall variants, split traffic, and measure conversions. The tooling is great, so teams use it.

Onboarding testing has historically been harder. Your onboarding flow is usually hardcoded in your app. Changing it means changing native code, submitting a new build, and waiting for review. Running a proper A/B test means building the infrastructure to show different flows to different users, track which variant each user saw, and measure outcomes. Most teams look at that and decide it is not worth the engineering investment.

So they optimize what is easy to optimize (the paywall) and leave the rest alone. It is rational behavior given the constraints. But it leads to a lopsided funnel where the bottom is polished and the top is leaking everywhere.

What happens when you test both

The most successful subscription apps we see are the ones that treat the entire user journey as a connected funnel and optimize every stage of it. They do not just test whether a $9.99 or $12.99 price point converts better. They test whether showing a personalization screen before the paywall increases the perceived value. They test whether moving the sign up wall from screen 2 to screen 5 changes how many people make it to the paywall at all.

These are not hypothetical experiments. Reordering onboarding screens is one of the highest impact, lowest effort changes you can test, and it routinely moves completion rates by 10 to 20 percentage points.

Think about what your onboarding is actually doing. It is building context. It is building trust. It is helping the user understand what your app does and why it matters to them. By the time they reach the paywall, their willingness to pay is largely determined by how well that onboarding went. A great onboarding does not just get more users to the paywall. It gets more convinced users to the paywall.

Making onboarding testing as easy as paywall testing

The reason paywall testing took off is that tools made it easy. The same thing needs to happen for onboarding. You should be able to create a variant flow, split traffic, and see the results in a dashboard, without writing code or waiting for app review.

That is exactly what Noboarding does. It is a server driven onboarding platform that lets you design flows in a visual editor, push changes over the air, and A/B test variants with built in analytics. It even integrates with RevenueCat, so you can track the impact of onboarding changes all the way through to revenue.

You would not launch a paywall without testing it. Why would you treat your onboarding any differently? The math says onboarding optimization has an equal or greater impact on revenue. The only reason it gets less attention is that it used to be harder to test. That is no longer the case.

Ready to optimize your onboarding?

Build, A/B test, and update your onboarding flow over the air. No app review required.