User Behavior Analysis
User Behavior Analysis
User Behavior Analysis
Identifying UX issues through data, forming hypotheses, and validating with usability testing.
Identifying UX issues through data, forming hypotheses, and validating with usability testing.
Identifying UX issues through data, forming hypotheses, and validating with usability testing.
#UsabilityTesting #UX #UXmetrics
#UsabilityTesting #UX #UXmetrics
#UsabilityTesting #UX #UXmetrics
2023
2023
2023
Background
Using the "exit behavior from live rooms" on the SWAG platform as a case study, we discovered UX pain points from existing data, proposed optimization hypotheses, and designed usability testing tasks to validate them. Insights were then applied back into product development.
Using the "exit behavior from live rooms" on the SWAG platform as a case study, we discovered UX pain points from existing data, proposed optimization hypotheses, and designed usability testing tasks to validate them. Insights were then applied back into product development.
Using the "exit behavior from live rooms" on the SWAG platform as a case study, we discovered UX pain points from existing data, proposed optimization hypotheses, and designed usability testing tasks to validate them. Insights were then applied back into product development.
Process
Pain Points
Unclear gesture behavior for swipe up/down to switch live rooms.
UX for leaving a free vs. paid live room is inconsistent:
Free rooms: users can swipe up/down or tap the top-right Close button.
Paid rooms: swipe is disabled; users must tap Close, then confirm via a "Are you sure you want to leave?" popup to exit.
The Close button’s placement is not ideal for one-handed use.
Unclear gesture behavior for swipe up/down to switch live rooms.
UX for leaving a free vs. paid live room is inconsistent:
Free rooms: users can swipe up/down or tap the top-right Close button.
Paid rooms: swipe is disabled; users must tap Close, then confirm via a "Are you sure you want to leave?" popup to exit.
The Close button’s placement is not ideal for one-handed use.
Unclear gesture behavior for swipe up/down to switch live rooms.
UX for leaving a free vs. paid live room is inconsistent:
Free rooms: users can swipe up/down or tap the top-right Close button.
Paid rooms: swipe is disabled; users must tap Close, then confirm via a "Are you sure you want to leave?" popup to exit.
The Close button’s placement is not ideal for one-handed use.
Optimization Goals
Enable one-handed switching between live rooms to help users explore content more easily.
Reduce the time and hesitation to leave a live room.
Allow users to discover content they enjoy faster, increasing overall watch time.
Enable one-handed switching between live rooms to help users explore content more easily.
Reduce the time and hesitation to leave a live room.
Allow users to discover content they enjoy faster, increasing overall watch time.
Enable one-handed switching between live rooms to help users explore content more easily.
Reduce the time and hesitation to leave a live room.
Allow users to discover content they enjoy faster, increasing overall watch time.
Hypothesis:
The existing design aimed to prevent users from accidentally leaving paid rooms. But we hypothesized that when users want to leave, their reasons (e.g., disinterest, show hasn’t started yet) outweigh the concern of leaving by accident—just like when you’ve paid for food, you’ll likely return to pick it up even if you step away temporarily.
Hypothesis:
The existing design aimed to prevent users from accidentally leaving paid rooms. But we hypothesized that when users want to leave, their reasons (e.g., disinterest, show hasn’t started yet) outweigh the concern of leaving by accident—just like when you’ve paid for food, you’ll likely return to pick it up even if you step away temporarily.
Hypothesis:
The existing design aimed to prevent users from accidentally leaving paid rooms. But we hypothesized that when users want to leave, their reasons (e.g., disinterest, show hasn’t started yet) outweigh the concern of leaving by accident—just like when you’ve paid for food, you’ll likely return to pick it up even if you step away temporarily.
Baseline Metrics
Tap Close → Confirm to leave
Paid live rooms: 87%
Early bird ticket buyers: 93%
Paid live rooms: 87%
Early bird ticket buyers: 93%
Paid live rooms: 87%
Early bird ticket buyers: 93%
→ High exit rates suggest the extra confirmation step isn’t effective in reducing exits.
→ High exit rates suggest the extra confirmation step isn’t effective in reducing exits.
→ High exit rates suggest the extra confirmation step isn’t effective in reducing exits.
Leave & Return Rates
Paid live rooms: 31% returned
Early bird ticket buyers: 78% returned
Paid live rooms: 31% returned
Early bird ticket buyers: 78% returned
Paid live rooms: 31% returned
Early bird ticket buyers: 78% returned
→ Indicates users leave with a clear intention, sometimes to return later.
→ Indicates users leave with a clear intention, sometimes to return later.
→ Indicates users leave with a clear intention, sometimes to return later.
*Data is for illustration only, processed and not real.
*Data is for illustration only, processed and not real.
UX & UI Optimization
Removed the leave confirmation popup.
Unified the UX so that all live room types support swipe gestures to switch rooms.
Improved one-handed usability.
Added visual prompts to inform users they can swipe to explore other rooms.
Removed the leave confirmation popup.
Unified the UX so that all live room types support swipe gestures to switch rooms.
Improved one-handed usability.
Added visual prompts to inform users they can swipe to explore other rooms.
Removed the leave confirmation popup.
Unified the UX so that all live room types support swipe gestures to switch rooms.
Improved one-handed usability.
Added visual prompts to inform users they can swipe to explore other rooms.
Usability Test Design
Task
After purchasing access, try to leave and explore other live rooms.
- Version A
Original flow with confirmation popup, swipe disabled.
- Version B
Confirmation popup removed, swipe enabled with prompts.
*Usability Testing Tool： Maze.co
Task
After purchasing access, try to leave and explore other live rooms.
- Version A
Original flow with confirmation popup, swipe disabled.
- Version B
Confirmation popup removed, swipe enabled with prompts.
*Usability Testing Tool： Maze.co
Task
After purchasing access, try to leave and explore other live rooms.
- Version A
Original flow with confirmation popup, swipe disabled.
- Version B
Confirmation popup removed, swipe enabled with prompts.
*Usability Testing Tool： Maze.co
Test Results
Participants: 39
Direct Success Rate: A: 76.9% | B: 97.1%
Avg Duration: A: 44.6s | B: 8.7s
In Version A, even though swipe was disabled, many participants still attempted it by instinct.
20% of participants in Version A failed the task, unsure how to leave.
In Version B, with swipe enabled and prompts added, everyone completed the task via swiping.
Participants: 39
Direct Success Rate: A: 76.9% | B: 97.1%
Avg Duration: A: 44.6s | B: 8.7s
In Version A, even though swipe was disabled, many participants still attempted it by instinct.
20% of participants in Version A failed the task, unsure how to leave.
In Version B, with swipe enabled and prompts added, everyone completed the task via swiping.
Participants: 39
Direct Success Rate: A: 76.9% | B: 97.1%
Avg Duration: A: 44.6s | B: 8.7s
In Version A, even though swipe was disabled, many participants still attempted it by instinct.
20% of participants in Version A failed the task, unsure how to leave.
In Version B, with swipe enabled and prompts added, everyone completed the task via swiping.
User Insights
Most participants didn’t realize free rooms allowed swiping, even internal staff.
Regardless of awareness, participants instinctively tried to swipe in paid rooms—showing that swipe-to-exit is a universal behavior.
Users don’t care if it’s paid or free—if they’re not interested, they want to switch.
If users can't easily tell when swiping is allowed, they’re left confused about how to leave.
Most participants didn’t realize free rooms allowed swiping, even internal staff.
Regardless of awareness, participants instinctively tried to swipe in paid rooms—showing that swipe-to-exit is a universal behavior.
Users don’t care if it’s paid or free—if they’re not interested, they want to switch.
If users can't easily tell when swiping is allowed, they’re left confused about how to leave.
Most participants didn’t realize free rooms allowed swiping, even internal staff.
Regardless of awareness, participants instinctively tried to swipe in paid rooms—showing that swipe-to-exit is a universal behavior.
Users don’t care if it’s paid or free—if they’re not interested, they want to switch.
If users can't easily tell when swiping is allowed, they’re left confused about how to leave.
Usability Testing Takeaways
Test tasks should be simple and isolate variables for accurate results.
Always review if the test actually measured the intended behavior.
Testing isn’t about right or wrong—it’s about gaining multi-dimensional user feedback.
Test tasks should be simple and isolate variables for accurate results.
Always review if the test actually measured the intended behavior.
Testing isn’t about right or wrong—it’s about gaining multi-dimensional user feedback.
Test tasks should be simple and isolate variables for accurate results.
Always review if the test actually measured the intended behavior.
Testing isn’t about right or wrong—it’s about gaining multi-dimensional user feedback.
UX Gesture Behavior Learnings
The more intuitive → the less friction → the better the experience.
Every optimization is an accumulation of experience—there’s no perfect solution.
Use qualitative research to sense user behavior, and quantitative data to validate hypotheses.
Behind every data point, understanding the underlying user logic is more valuable than the metric itself.
The more intuitive → the less friction → the better the experience.
Every optimization is an accumulation of experience—there’s no perfect solution.
Use qualitative research to sense user behavior, and quantitative data to validate hypotheses.
Behind every data point, understanding the underlying user logic is more valuable than the metric itself.
The more intuitive → the less friction → the better the experience.
Every optimization is an accumulation of experience—there’s no perfect solution.
Use qualitative research to sense user behavior, and quantitative data to validate hypotheses.
Behind every data point, understanding the underlying user logic is more valuable than the metric itself.
more to explore
pinxichen.design@gmail.com
Website design and content © 2025 Pinxi Chen
pinxichen.design@gmail.com
Website design and content © 2025 Pinxi Chen
pinxichen.design@gmail.com
Website design and content © 2025 Pinxi Chen