The Language That Started Everything
When the client brief landed on my desk with “design for seniors,” I thought it was straightforward. Then our users started talking back.
“We hate being called seniors,” the 50+ participants told us bluntly during our first research session. “That term is for the old and frail.” For them, “senior” carried connotations they rejected: weakness, dependence, irrelevance. We changed our reports to use “older adults,” but that small language shift exposed something larger. Our categories were not reflecting how people saw themselves.
When Age Stopped Predicting Behaviour
Initially, we planned to segment findings by neat age ranges. Except participants in the same age bracket gave us completely opposite feedback. A 55-year-old power user had more in common with a tech-savvy 25-year-old than with a 55-year-old neighbour who refused to touch smartphones.
The revelation hit hard: age does not predict behaviour. Mindset does.
Those Pokemon Go players wielding five phones in the park were not outliers in their age group. They were part of a tech-adopter archetype that spanned generations.
During ActiveSG research, my team asked what seemed like a reasonable question: “What is a good average steps goal that is fair for seniors? 5,000? 10,000?” The room erupted in laughter. Older participants shared that they were walking far more than all of us “youngsters,” averaging 15,000 steps daily while we researchers barely hit 5,000 because of our desk jobs.
The Moment Ethnicity Categories Cracked
The assumption that shattered our approach came during Indian outreach research. One participant looked directly at us and said: “Why do you separate Indian or Malay? I am ethnically Indian but I eat Malay food with my family.”
We had designed separate studies for “Indian communities” and “Malay communities” as if these were distinct, non-overlapping groups. Singapore’s reality is messier and more beautiful than our research boxes suggested.
- Indian families who primarily spoke Malay at home
- Malay participants who identified more with Chinese health practices
- Mixed heritage individuals who moved fluidly between cultural contexts
Beyond UX: When Categories Miss Reality
This problem extends beyond research. From a policy perspective, we often segment by housing type: 1-room to 5-room flats, private housing. But on the ground, that can be sensitive and misleading.
A 5-room flat does not automatically mean someone is wealthier. They might be supporting elderly parents, dealing with medical debt, living in inherited property while struggling financially, or sharing space with extended family out of necessity.
The Real Patterns: Archetypes Over Demographics
When we stopped looking at birth years and started examining attitudes, the real segments became behavioural archetypes:
- The Cautious Researcher: extensively studies before trying anything
- The Social Validator: needs community approval before adopting new behaviours
- The Convenience Optimizer: adopts anything that saves time or effort
- The Authority Skeptic: questions institutional recommendations
- The Tech Adopter: embraces new technology regardless of age
- The Digital Resister: avoids technology regardless of capability
The Strategic Shift
When presenting research now, I focus on behavioural archetypes rather than demographic breakdowns. Instead of saying “users aged 50 to 65 prefer simple interfaces,” we say “cautious researchers need extensive information before committing, regardless of demographics.”
This changes everything. Design decisions become clearer when based on motivations rather than assumptions. Feature prioritisation focuses on user jobs rather than stereotypes. Success metrics measure behavioural change rather than demographic reach.
The hardest part is not convincing users to change. It is convincing stakeholders that their demographic assumptions are wrong. People are who they are, not what our spreadsheets say they should be. Our job as researchers is to understand that complexity, not simplify it away.