Stop Designing for Demographics. Start Designing for Behavior

The Language That Started Everything

"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 quickly changed our reports to use "older adults" instead, but this small language shift exposed something much larger: our categories weren't reflecting how people saw themselves.

When Age Stopped Predicting Behavior

Initially, we planned to segment our findings by neat age ranges. Logical, right? Except when we tried to analyze the data, 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 their 55-year-old neighbor who refused to touch smartphones.

The revelation hit hard: age doesn't predict behavior. Mindset does.

Those Pokemon Go players wielding 5 phones simultaneously in the park? They weren't outliers in their age group—they were part of a "tech adopter" archetype that spanned generations. The 70-year-old downloading new apps shared more behavioral patterns with a 25-year-old early adopter than with their age-matched peers.

During our ActiveSG research, my team asked what seemed like a reasonable question during focus groups: "What's a good average steps goal that's fair for seniors? 5,000? 10,000?" The room erupted in laughter. The 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 due to our desk jobs.

Here we were, designing fitness solutions for people who were already outperforming us. It wasn't about age-based fitness levels. It was about self-motivated health archetypes that existed across all demographics.

The Moment Ethnicity Categories Cracked

The assumption that truly 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."

That single verbatim exposed how wrong our research categorization was. We had designed separate studies for "Indian communities" and "Malay communities" as if these were distinct, non-overlapping groups. But Singapore's reality is far messier—and more beautiful—than our research boxes suggested.

As we dug deeper, we found countless examples of cultural fluidity:

• 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

Even ethnicity couldn't predict cultural behavior in multicultural Singapore.

Beyond UX: When Categories Miss Reality

This demographic assumption problem extends beyond research. I recently attended a Workers' Party meet-the-people session at Compassvale, and my mentor gave me crucial advice: "Don't ask about dwelling type during registration."

From a policy perspective, we always segment by housing type—1-room to 5-room flats, private housing. It's how government services are designed, how we assume socioeconomic status. But on the ground, it's sensitive for people to reveal. Staying in a 5-room flat doesn't automatically mean you're wealthier, right?

You might be supporting elderly parents across multiple generations, dealing with medical debt, living in inherited property while struggling financially, or sharing space with extended family out of necessity, not choice. The assumptions we make from housing type often miss the lived reality of Singapore families.

The Real Patterns: Archetypes Over Demographics

When we stopped looking at birth years and started examining attitudes, everything clicked. The real segments were based on behavioral archetypes:

• The Cautious Researcher: Extensively studies before trying anything (across all ages and cultures)

• The Social Validator: Needs community approval before adopting new behaviors

• 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

These personas predicted behavior far better than any demographic breakdown ever could.

The Strategic Shift

When presenting research to stakeholders now, I focus on behavioral archetypes rather than demographic breakdowns. Instead of "users aged 50-65 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 prioritization focuses on user jobs rather than stereotypes

• Success metrics measure behavioral change rather than demographic reach