From Intern to Solo Designer: Growth (opens in new tab)

As a Toss Bank product design intern, Jeon Nuri designed experiments to improve non-member sign-up conversion. She prioritized the funnel using speed and impact, studied previous experiments, and learned that clear, narrowly defined hypotheses were more valuable than constantly generating new ideas. The experience showed that failed experiments can still guide better decisions when they produce actionable learning.

Prioritizing the Right Funnel Stage

• The largest drop-offs occurred in the intro, consent, and identity-verification screens.
• Consent and identity verification were shared modules requiring legal and compliance review, making rapid iteration difficult.
• The intro screen could be changed more quickly and had the potential to affect the greatest number of users.
• Based on this speed-versus-impact assessment, she chose the intro screen as the starting point.

Learning from Previous Experiments

• Instead of immediately designing new concepts, she reviewed existing experiments, including both winners and unsuccessful variations.
• She examined:
  • The problem each experiment addressed
  • The reasoning behind its hypothesis
  • How the test variation was designed
• Experiments from unrelated screens were also useful because their problem definitions and hypothesis structures could be adapted.
• The main lesson was that inexperienced experimenters benefit more from systematically analyzing existing learning than from rushing to create new ideas.

First Experiment: A Counselor Concept

• The first variation presented benefits as if they were being recommended by a counselor and offered a small number of choices.
• The hypothesis was vague: fewer choices would increase conversion.
• The result was negative:
  • Click-through rate fell by more than 10%.
  • Conversion rate fell by more than 3%.
• The design actually introduced more choices than the original, which had only one CTA button.
• The experiment also failed to consider why users had entered the screen and whether they needed recommendations.
• This led her to analyze the existing screen and user context before creating a hypothesis.

Identifying and Solving Concrete Problems

• Rather than inventing an entirely new design, she identified two specific weaknesses in the existing version:
  • The copy did not clearly communicate benefits users cared about.
  • Images loaded slowly, taking two to three seconds on low-end devices.
• Previous experiments showed that users responded well to messages about high interest rates and receiving interest daily.
• She incorporated those themes into the copy and optimized the visuals with newer graphics and lower-weight image formats.
• Both click-through rate and conversion rate increased, demonstrating that a hypothesis grounded in clear problems can provide a stable direction for design.

Making Benefits Easier to Imagine

• Building on the earlier results, she changed functional wording into language that helped users imagine a concrete situation and immediate benefit.
• Instead of simply explaining that interest could be earned after depositing money for one day, the revised copy foregrounded the moment when users would experience the benefit.
• Copy alone increased CTR by 5% and also produced a meaningful improvement in CVR.
• The result reinforced that different expressions of the same information can create significantly different first impressions.

Principles for Designing Experiments

• Break the funnel into stages and prioritize opportunities by speed and potential impact.
• Understand the existing context before defining the core problem.
• Study previous experiments through their hypotheses and problem definitions, not just their numerical outcomes.
• Establish a clear hypothesis and success metric before designing the variation.
• Make sure the experiment visibly tests the stated hypothesis.
• Treat failure as input for the next decision rather than as wasted effort.

A practical starting point for new designers is to begin with a small, focused experiment—but make the hypothesis precise enough to guide both the design and the next iteration.