Managed optimization
Paywalls that convert like someone designed them for each user.
We run continuous paywall experiments for you using Curious. The copy, the pricing, the layout, the offer. Every element tested and personalized, without your team managing a single experiment.
The problem
Your paywall is one screen doing a lot of work.
It's the moment where a free user decides whether your product is worth paying for. Most teams treat it as a design project: pick a layout, write some copy, choose which plans to highlight, ship it, and move on. Maybe you A/B test a few versions once a quarter.
The problem is that one paywall can't be right for everyone. A user who signed up yesterday and one who has been active for three months have different relationships with your product. Someone coming from a referral link is in a different mindset than someone who hit a feature gate. The copy that convinces one to subscribe might be irrelevant to another. So can the price, the trial length, the plan you lead with, and the way you frame the value.
How most tools approach this
Templates and manual A/B tests.
Superwall, RevenueCat, and similar platforms give you a paywall builder, a template library, and A/B testing. You design variant A and variant B, split traffic, wait for statistical significance, pick the winner. That works, but you're limited by the variants your team thinks to create, and each test takes weeks to resolve.
Some platforms offer AI-driven features like demand scoring or audience segmentation. These help you target paywalls better, but the paywalls themselves are still designed and written by your team. The creative bottleneck doesn't go away.
How we do it
AI generates the variants. Real traffic picks the winner. Nobody declares a winner and walks away.
We connect Curious to your paywall surfaces: the subscription screen, the upgrade prompt, the trial-end nudge, the cancellation save. For each one, we define the goal together (trial starts, paid conversions, LTV, minus refunds, within whatever churn or complaint constraints you set).
Curious generates paywall copy variants using AI, each testing a different hypothesis about what will convert. It might test loss aversion against aspiration, short copy against detailed feature lists, annual-first against monthly-first. Then it runs them through a contextual bandit that learns which combination works for which user based on their behavior, their tenure, their platform, and whatever context you pass.
There's no waiting for statistical significance on a two-variant test. The system is testing many variants simultaneously, shifting traffic toward what's working, and replacing underperformers with new candidates as it learns. Early signals (paywall views, trial starts, session depth) let it learn in minutes while slower outcomes like renewals and refunds mature.
We manage everything: integration, experiment design, copy review, constraint tuning, and ongoing monitoring. Your team doesn't need to design paywall variants, set up experiments, or interpret results. We send you a weekly report showing what's converting, for whom, and why.
What you get
Managed end to end.
Integration.
We work with your team to connect Curious to your paywall rendering. If you're using Superwall, RevenueCat, or a custom implementation, we integrate with what you have. The connection is a single API call that returns the paywall content to display.
Copy and offer generation.
Curious generates the paywall copy, pricing presentation, and offer framing. Each variant is built on a specific hypothesis (this user is price-sensitive, this user values social proof, this user needs reassurance about cancellation). Your team reviews the first round to make sure the voice is right. After that, the system generates within your brand guidelines and content constraints.
Per-user personalization.
This is what separates a contextual bandit from an A/B test. User A (day-one signup, came from an ad, on an iPhone) sees a different paywall than User B (three-month active user, hit a feature gate, on Android). The system learns these distinctions from your traffic, not from rules you write.
Guardrails.
You set the boundaries: refund rate can't exceed X, trial-to-paid churn can't rise past Y, no copy that mentions competitors, no prices below your floor. The system operates within them.
Weekly reporting.
What's converting, what isn't, which user segments respond to which messages, and what changed since last week. We flag anything surprising and discuss whether to adjust the strategy.
Your paywall should work as hard as the product behind it.
Stop testing two headlines and calling it optimization.