Intro
The challenge
Low-quality trials
The previous model offered a default trial, which attracted users who didn't really intend to purchase. This meant lots of trial starts, but few conversions.
Commitment friction
The team hypothesized that a user who actively chooses a trial is more committed to the product and therefore more likely to convert.
Complex flow testing
Testing an opt-in feature would typically require intricate, time-consuming engineering work to manage the two variants.
The solution
Linearity Curve used an A/B test that directly compared users that opted in versus default users in the conversion funnel:
Control paywall
Featured a default trial offering, where the trial was automatically included in the initial purchase flow.
Variant paywall
Featured an opt-in trial option, requiring the user to take an explicit action to select the trial before continuing.
Analytics allowed the team to measure the user quality from each variant, validating whether the friction of "opting in" was profitable.
The results
Trial conversion rate soared
The opt-in trial variant resulted in a 10% increase in the trial conversion rate, showing that the higher lead quality outweighed the lower trial starts.
Validated commitment model
The test demonstrated that users who actively committed to the trial were more likely to find value and less likely to abandon the subscription.
Data-driven feature gate
The team gained confidence in implementing behavior-based trial offers, knowing that intentional user choices lead to better outcomes.
About Superwall
Superwall is the centralized monetization suite that converts data into revenue. It provides product, growth, and engineering teams with a unified platform to deploy paywalls, manage subscriptions, and ensure every experiment is a profitable, data-backed decision.
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Flibbo
How Flibbo gained 20% growth by moving from static paywalls to instant A/B testing
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