As the lead Interaction Designer for Xiaozhitiao — Kwai's first interactive feature — I spearheaded data-driven UX optimization, mastering how to leverage analytics insights to fuel iterative product enhancements.

Role

Interaction Designer

Duration

2024

Type

Kwai · Social Business

What is Xiaozhitiao?

Xiaozhitiao is Kwai's social interaction feature enabling users to exchange real-name and anonymous messages with mutual friends or strangers.

Whimsical message-delivery feature for friends

Adolescent social behaviors are driven by status display and emotional companionship

Anonymous chats and randomized message drops to strangers

Limited diversity in in-platform interactive mechanics

What did I do in the project?

Mapping user cognition

Define interaction paradigms

Through systematic audit of Kwai user-generated content, I identified dominant user cognition of Xiaozhitiao as an information carrier with higher-engagement.

User perception

Paper Plane

"Game"

User perception

XiaoZhiTiao

"Messaging"

Analyze feature-specific metrics

to drive core KPIs

Post-MVP launch, we leveraged funnel conversion rates and core action CTR analytics to refine page experiences.

UX Optimization

11

Message Send Rate

+7.87%

Home Feed Impressions

+13.86%

Negative Feedback

-65.75%

Harness cultural insights

to enrich feature experiences

User content analysis and interviews revealed collectible culture and style personalization as dominant trends among our Gen Z demographic.

This inspired the launch of customizable note themes.

MVP

Post-Optimization​

Theme Previews

User Behavior Patterns

What did I learn in the project?

Let users choose a single dimension

Don't make the topic name semantic.

Pre-Optimization

Represent theme selections via textual buttons

Exposure of non-default theme is

2% - 10%

of default

Message volume is

10% - 50%

of the default

Post-Optimization​

Prioritize visual theme previews over semantic labeling

Exposure of non-default theme is

37%

of default.

Average message volume is

57%

of the default

Maintain isolated single-variable manipulation

per experimental group

A single test group manipulated with two variables invalidates causal attribution

Isolating individual impact is statistically impossible

More…

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