How to Segment Participants for User Research in China

Why city tier, age and income alone are not enough to recruit the right participants for user research in China

Sep 11, 2026

ZL

Zippora Lau

UX Director

Beyond Tier 1 and Gen Z in China

What do China’s city tiers actually describe?

City tier is context, not a personality type

What common participant labels can and cannot tell researchers
Common labelWhat it may tell youWhat it cannot tell you
City tierBusiness activity and local infrastructureA person’s income, values or digital confidence
Gen ZA broad age groupStage of life, independence or shared attitudes
Income bandMoney available within a clearly defined measureFreedom to spend, family support or spending priorities
Smartphone userAccess to a mobile deviceConfidence using different apps, tasks and services

Income must be measured separately from location

Ask income questions that match the study

Define “Gen Z” before using it in a China sample

Digital access is not the same as digital confidence

Five things to consider when choosing research participants in China

1. Place and daily surroundings

2. Money and household situation

3. Age group and stage of life

4. Digital habits and confidence

5. Experience with the product category

Turn a broad brief into a sample you can research

Example: choosing a home air purifier

Starting brief

Sample design

Diary structure

Where city tier fits

Example: everyday retail and delivery journeys

Starting brief

Sample design

Diary structure

Where city tier fits

Common mistakes when segmenting Chinese research participants

Using equal city-tier quotas without a clear reason

Using city tier as a measure of income

Calling everyone born within a broad range “Gen Z”

Recruiting only the easiest digital participants

Treating qualitative groups as national estimates

Keep participant groups visible during diary-study analysis

How Recap supports diary research in mainland China

Research in China. Insights anywhere.

Run diary studies, analyse responses in English, and generate evidence-backed insights from a single research workspace.

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