Team

Myself, a product manager, a product designer, a product design intern, engineer team, Content designer, Brand designer

Platforms

Native iOS and Android apps, mobile and desktop web

Timeline

4 sprints over 8 weeks

The company had previously launched a failed referrals program, attempting to get users to refer Super+, a premium subscription with little perceived value. Without strong incentives, adoption was low.

This time, leadership wanted to leverage a proven in-app feature—Earnings—where users could make real money through offerwalls. The goal was to create a referral system that incentivized sharing and sustained engagement.

As a hybrid IC and design manager, I supported the design effort, managed an intern and senior designer, and advocated for research, process improvements, and higher-quality execution within a fast-moving, delivery-focused team.

For our business

Add a new, retentive feature for customers that would:

• Lower the cost to acquire customers

• Increase net revenue from earnings

• Accelerate user growth

Competitive analysis

Validated commission-based incentive model

Narrowed design choices for 2-sided experience

Identified industry best practices

User journey mapping

Built customer empathy with cross functional team and stakeholders

Surfaced unexpected pain points

Clarified achievable scope

Uncovered overlooked experience needs

Future exploration wireframes

Aligned cross-functional team and stakeholders

Prioritized order of feature development

Influenced roadmap

Output a synthesis of research, best practices, industry standards

Unmoderated usability test of both sides of experience

Though formally out of scope as part of the PDLC, I independently planned, scripted, designed, prototyped, analyzed and reported out findings from unmoderated testing on usertesting.com

• Validated core user flows for Referrer and Referred

• Identified unforeseen pain points that required iteration before release

• Uncovered a key insight that drove future planning and cross-functional collaboration: Participants claimed they wouldn’t refer unless they trusted the platform to deliver on promises. Since we didn’t own experiences outside these flows, solving for this trust issue required support from a partner vertical.

The scope of the Referrer experience for MVP was focused on primary entry points and primary landing page. We would gather data on user behavior and focus additional resources on the Referred MVP.

I designed and drove implementation for the most successful referrals entry point (the pink banner animated from the bottom of the screen).

I identified the opportunity based on existing data and understanding of user behavior, but it was initially scoped out of MVP.

To get this rescoped, I leveraged my relationships with engineering and found solutions. We successfully delivered and built this component resulting in:

• The highest percentage of successful referrals

• Highest CVR from click to send link

• 2nd highest overall CTR

The scope of the Referrer experience for MVP was focused on a linear onboarding flow with gamified activities.

Referrer user journey

You are invited to make your first referral

Referred user journey

Your friend sends you an invitation to sign up for Super.com

A K-factor of 1.0+ means your product is viral, and the goal of the referral program was ultimately to to achieve that goal. It took 3 weeks to achieve statistical significance in the experiment.

• Primary KR was achieving a K-factor of 0.20 (20%) and we managed 0.022 (2.2%)

• Secondary KR was to acquire 3k monthly earners (not just sign-ups) and we acquired 774

Our team knew these launch KRs were a likely out of reach. But we learned a lot. One important learning: the assumption that that the financial incentive alone would be enough to motivate our customers to refer another user. This assumption was wrong. Post-launch iterations would subsequently focus on remedying this failed assumption.

Super.com is a data-driven, highly iterative company. I have never worked anywhere quite like it. The shipping cadence was incredibly fast and the release cycle was measured in days, not weeks or months. The final MVP lacked many of the features I would have wanted — but that wasn’t the goal of the initial release. It was for gathering data to make more informed decisions for the next iteration. To that end, we immediately got to work identifying which user cohorts were most engaged, what entry points were most effective, and if the program had the potential to scale.

For additional details to this case study, reach out and let’s set up a time to chat!