03 / 07 · Case study

Trust & Value

Role

UX Researcher

Discipline

Strategy

Context

Fintech

Year

2025

Original flow vs. segmentedn=10 per segment

Sign-up intent

Original

Segmented

+50%

Referral likelihood

Original

Segmented

+58%

One flow, two very different reasons to care.

So onboarding was split by motivation, not by feature.

Different users needed different reasons to trust Super+. Segmenting onboarding by motivation lifted sign-up intent 50% and referral likelihood 58%.

I led usability research on Super.com's onboarding redesign, working with product design and product management over 4 months to make sure it built trust for every kind of user.

50% increase in willingness to sign up after the redesign

58% increase in referral likelihood after the redesign

10 participants per segment validating the final result

2 user segments tested separately: travel-focused and credit-building-focused

The Challenge

One flow, wildly different reasons to care

Super.com bundles hotel discounts, credit-building, and gamified earning under one premium membership, Super+. That's exactly the problem.

A user chasing cheaper hotels and a user trying to build credit are motivated by completely different value, yet the existing onboarding tried to sell both with the same generic flow. With a major app redesign underway, I was brought in to lead usability research on the new onboarding, working with product design and product management over 4 months to make sure it built trust and made Super+'s value obvious for either kind of user.

One onboarding flow, two user motivations

Same Super+ onboarding flow

One generic pitch, shown to every new user regardless of why they signed up.

Travel-focused user

Value prop: hotel discounts, upgrades, member pricing

Credit-building-focused user

Value prop: credit-building tools, gamified earning

One onboarding flow, two users motivated by entirely different value: the existing flow pitched both the same way.

The Super.com home screen, with cash advance, tasks, games, surveys, location rewards, and hotels all competing for the same users attention — Home: every bundled feature competing for attention at once

Onboarding screen asking new users what they are most interested in, to prioritize the experience by motivation — Onboarding: segmenting by motivation before showing anything else

My Approach

Testing by motivation, not by feature

A single usability test wasn't going to answer this, because "does this work" depends entirely on who's asking.

I split participants into segments by their actual motivation, travel-focused vs. credit-building-focused, so I could measure how well onboarding landed for each group rather than averaging away the difference between them. I also varied scope deliberately: component-level tests isolated individual steps so the team could act on specific fixes fast, while end-to-end tests checked whether the full journey still held together and built trust across the whole flow.

Test coverage by segment and scope

Component-level

End-to-end

Travel-focused

Fast, targeted fixes to hotel-discount and travel messaging

Whole-journey trust check for a travel-motivated user

Credit-building-focused

Fast, targeted fixes to credit-building and earning messaging

Whole-journey trust check for a credit-building-motivated user

Segment and test scope were varied independently, so every combination of user and depth got covered on purpose.

The recruiting strategy evolved too. Early rounds used 6 participants per segment (enough for fast, directional feedback), and once we'd iterated, I expanded follow-up rounds to 10 per segment, because a decision this close to launch needed numbers I could actually trust.

Recruiting expanded across two rounds

Early rounds

6 participants per segment, fast, directional feedback while designs were still moving.

Follow-up rounds

10 participants per segment, enough to trust the lift before reporting it.

Recruiting expanded once early rounds had done their job, so the numbers behind the final result could actually be trusted.

What I Found

Segmentation was the whole answer

The research became a running feedback loop with design and product. The clearest insight: different Super+ features carried the value for different people.

A single generic pitch was underselling the app to everyone, so moving to a segmented, tailored onboarding became the fix.

Continuous research–design–product loop

Research

Testing surfaced what was not landing for each segment.

Design

Iterated the segmented onboarding to address it.

Product

Prioritized what shipped ahead of the next round.

Findings from each round shaped the next design iteration, round after round.

Findings fed a continuous loop with design and product, not a single handoff, reshaping the onboarding across multiple rounds.

Post-redesign testing confirmed it: a 50% increase in users' stated willingness to sign up, and a 58% increase in referral likelihood, compared to the original flow.

Sign-up intent and referral likelihood, before vs. after

Sign-up intentBefore+50%

Referral likelihoodBefore+58%

Both measures indexed to a 100-point baseline from the original flow.

Reflection

What I'd do differently

This project taught me the difference between what a small sample tells you and why it's telling you that.

In a few early rounds, quantitative scores actually regressed for designs the team agreed were clear improvements. A single outlier, or just one harsh rater, can swing an n=6 average dramatically.

In formative research, the qualitative data is the true signal. A small sample's scores are a clue, not a benchmark.

That's why I pushed to expand the follow-up rounds to 10 per segment before trusting the quantitative lift enough to report it as a real result.

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