Onboarding

New Onboarding Flow
Drives Signups to Subscribers

I led the redesign of JEFIT's onboarding from the ground up, reframing it from a fast sign-up flow into a goal-based, conversion-focused experience. Working closely with PM, I ran multiple rounds of A/B testing across flow structure, paywall design, and platform-specific variations.

The new flow outperformed every previous benchmark, driving a ~150%* increase in monthly revenue compared to the same period the prior year.

*Due to NDA, the data are approximate.

Timeline

Q4 2023 - Q4 2024

My Role

Lead Product Designer

Team

Product Manager, Engineers

Scope

Monetization strategy, onboarding UX/UI, rapid prototyping, A/B testing

result

Before & After

Impact

Outperforming Past Revenue Benchmarks

After launching the new onboarding flow, we saw a major shift in when users chose to pay. As shown in the chart, revenue generated during onboarding increased significantly—surpassing both post-onboarding revenue and the platform’s previous overall benchmarks.

PAID CONVERSIONS

Onboarding vs Non-Onboarding

Last 12 months

Onboarding users Non-onboarding users

After the new onboarding flow launched, paid conversions during onboarding surpassed non-onboarding revenue for the first time.

This is an ongoing project where we continuously test ideas and refine every step of the onboarding process.
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Here’s a closer look at my contributions in making it happen.

context

Reframing Onboarding as a Revenue Lever

In early 2024, JEFIT’s revenue began to decline despite stable traffic and no major product regressions. Funnel analysis showed that a large portion of revenue potential lived in onboarding, yet paid conversion during onboarding remained around 15%.

Historically, onboarding had been optimized for fast sign-ups rather than for helping users understand the value of upgrading. I was brought in to rethink onboarding as a conversion-focused experience, without breaking trust or overwhelming new users.

Problems

Where the Onboarding Fell Short

The existing onboarding was intentionally lightweight — users created an account in a few steps and landed in the app with minimal guidance. Three issues emerged:

Paywall before investment

Users hit the paywall before investing real time, effort, or personal input, leaving little emotional commitment to continue.

Designing Beyond One-Size-Fits-All

Key paid features were not surfaced or explained during onboarding, making the upgrade feel abstract

One-size-fits-all questions

Onboarding asked broad, non-specific questions that failed to reflect users’ individual goals. are not personalized for users' needs

Strategy

Build Trust and Relevance Early

Rather than simply redesigning the paywall, I focused on the steps leading up to it. The core strategy was to build trust and relevance before asking users to pay.

Competitor research showed that high-performing fitness apps often help users imagine outcomes early, making the value proposition feel tangible. However, instead of copying generic before-and-after visuals, I aligned the approach with JEFIT’s strengths: personalized plans, structured training, and progress tracking.

The goal was to help users feel that the product was built for them before presenting a subscription offer.

SOlution

Clear Goals, Better Results

During my early exploration of the onboarding experience, I realized we had overlooked a critical step: understanding users' training goals. This gap became clear as I studied competitors who leveraged goal-setting to personalize workout plans. Beyond just customization, it made users feel seen and supported—something our flow was missing.

With this understanding, I revamped our approach by replacing generic options like "Maintain," "Bulk," and "Cut" with user-friendly terms such as "Lose Weight," "Gain Muscle," and others that resonate better with all fitness levels.

Bonus takeaway

In the testing results, another finding is that the majority of users pick "Gain muscle" and "Loss weight" as their primary goal, which leads to a specific paywall targeting these groups in the paywall screen iterations.

Solution

Tailoring Paywalls by Platforms

Through a series of A/B tests, we discovered that the most effective paywall varies between iOS and Android users.

By iterating on layout, visuals, and messaging, we identified high-performing versions tailored to each platform. These tests led to a measurable increase in conversion rates and highlighted the importance of designing with platform-specific user behavior in mind—even for the same core flow.

iOS version

Android version

solution

Pacing Onboarding to Build User Commitment

A common assumption was that a shorter onboarding flow would reduce drop-off. However, through user research and testing, I discovered that a slightly longer onboarding—when designed thoughtfully—could actually improve engagement and conversion.

By breaking the flow into focused, visually engaging steps and adding brief cut-off screens to pace the experience, users felt more guided rather than overwhelmed. This structure gave them time to reflect on their goals, feel invested in the process, and build commitment early on.

Instead of rushing users through, we gave them space to connect with the product, leading to a measurable increase in paid conversions.

Iterations

Final version

Takeaways

Data-Driven Rapid Iteration

This project strengthened my ability to navigate ambiguity and move quickly by collaborating with PMs to run A/B tests. Turning data insights into actionable design changes allowed me to continuously iterate, boosting conversion and sharpening my sense of what works in product design.

Designing Beyond One-Size-Fits-All

We discovered significant differences in preferences between iOS and Android users. By customizing themes and interactions for each platform, we increased user trust and conversion rates. This taught me the importance of deeply understanding user context to deliver truly effective design solutions.