Onboarding & Subscreen

From clearer music choices to a personal practice plan. Six A/B experiments exploring how onboarding and subscription design affect ARPU.

Role
Sr. Growth Designer
Team
PM, Data Analyst, Full-Stack Developer
Responsibilities
Ideation, UX/UI design, A/B testing

Experiment 01

Clearer choices, less visual noise

Problem
Dense lists made instruments and genres difficult to scan.
Hypothesis
Visual genre and artist choices are familiar from services such as Spotify and Apple Music.
Goal
Make preference selection easier to understand.
What was done
Replace dense lists with recognisable icons, artist portraits and clearer selection states.
ACurrent version
Bright tour — Current version: five onboarding screens
BTesting version
Bright tour — Testing version: five onboarding screens
+3.45%ARPU

Statistical significance: 92%

Conclusion
Familiar patterns from music services may have made onboarding easier to navigate. The modest uplift encouraged us to explore whether showing the value of personalisation could have a greater impact.

Experiment 02

Make personalisation visible

Problem
Users answered preference questions without seeing how those answers shaped the app.
Hypothesis
Showing preparation could make the promise of a personal experience more tangible.
Goal
Communicate the value of the answers before the offer.
What was done
Add progress screens explaining how songs and recommendations are being selected.
ACurrent version
Personalisation flow — Current version
BTesting version
Personalisation flow — Testing version
0%ARPU

Statistical significance: 90%

Conclusion
Low contrast and hard-to-read copy may have left users unsure what was happening or why personalisation would benefit them.

Experiment 03

An app made for your music

Problem
The first preparation sequence had not communicated enough personal value.
Hypothesis
Artist artwork could make the promise feel more connected to the music users love.
Goal
Make the transition from preferences to a personalised app more convincing.
What was done
Explain why we ask about songs and add an artist-cover preparation screen before the subscription offer.
ACurrent version
Artist personalisation flow — Current version
BTesting version
Artist personalisation flow — Testing version
+5.62%ARPU

Statistical significance: 95%

Conclusion
Artist covers may have made the app feel like a personal fit, suggesting that music for practice had been selected specifically for the user.

Experiment 04

Carry the promise into the offer

Problem
The personalised preparation screen led to a visually generic payment screen.
Hypothesis
Keeping the artist artwork could connect the offer to the experience just promised.
Goal
Keep personalisation consistent through the subscription decision.
What was done
Use the same artist-cover background on the payment screen.
ACurrent version
Personalised offer — Current version
BTesting version
Personalised offer — Testing version
+11.06%ARPU

Statistical significance: 97%

Conclusion
Keeping favourite artists on the payment screen preserved the personalisation context and may have made payment feel like the next step to access that tailored experience.

Experiment 05

Turn motivation into a practice plan

Problem
Beginners wanted to play music they loved, but lacked a clear path to progress.
Hypothesis
A realistic practice commitment can connect effort to an achievable milestone.
Goal
Make learning progress feel personal and attainable.
What was done
Added a weekly practice choice and a graph showing the path to the next skill level.

Research

My approach. Segment the audience, understand their motivations and challenges, then design a personalised experience for the selected groups.

What beginners dream about. Analytics showed that 60% of the audience we needed to influence were newcomers to music. Qualitative research revealed two motivations: play a favourite song and perform popular songs for friends.

What gets in the way. Progress feels slow, regular practice is difficult without a clear goal, and a favourite song can be too challenging. When the effort is greater than expected, beginners quit.

From research to a practice plan. Six music teachers recommended regular practice and a minimum weekly commitment. I developed around 40 personalisation ideas from these findings. In this experiment, users chose their weekly practice time, and a graph showed how many months it could take to reach the next level.

ACurrent version
Practice plan — Current version: onboarding and subscription flow
BTesting version
Practice plan — Testing version: onboarding and subscription flow
+9%ARPU

Statistical significance: 95%

Conclusion
Seeing a timeline to the next level and clear practice steps may have made long-term learning feel achievable, turning the subscription into a commitment to keep practising.

Experiment 06

From a plan to a reminder

Problem
A practice plan still needed to become a regular habit.
Hypothesis
We hypothesised that motivated learners would want a reminder after choosing a plan.
Goal
Help users turn their intention into a practice routine.
What was done
Offer a daily reminder after the practice plan and personalisation screen.
ACurrent version
Practice reminder flow — Current version
BTesting version
Practice reminder flow — Testing version
+1%ARPU

Statistical significance: 86%

Conclusion
Users may not be ready to commit to reminders before trying the app, or know a fixed practice time when their routine depends on the day and their mood. A next test could offer reminders again after their first tab view.
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