Define a consumer, pick a market, and see what they actually value in a place.
The model holds an illustrative panel of 12,000 people across 19 markets, each scoring how much a factor matters to them from 1 to 10. This tool turns that into a dial: build a persona, choose a location, and watch the factors re-rank and pull away from the global average. Where a group over-indexes is where a place should lead. Every figure here is illustrative: swap in a real benchmark extract to make it live.
Persona signature
Experience factor benchmarks
Where this group diverges from global
What people value in a place doesn't move in a vacuum — it shifts with region, market and life-stage.
An illustrative benchmark set and a Hong Kong market read, layered alongside the persona model to show how the signals shaping demand move together.
Demonstration data only
Where APAC leads, and where it doesn't
APAC delivers the most consistent experience globally — an HX score of 92. Focused work is best supported here: 86% positive versus 64% globally. Yet the tension is real — 6 of the 10 worst-performing factors still relate to individual work, and current designs over-index on collaboration. Nutrition, sound privacy and meeting rooms are the factors people are most emotional about.
Human Experience score by APAC market
Demographics move the dial
Who shows up — and what they need — splits sharply by life-stage. The under-24s attend least but want the most flexibility; men attend ~9% more than women, who still carry more caregiving. Bar = current days, marker = preferred.
The market read · Hong Kong, 1Q26
What the model assumes · where to swap in real data
The engine. Each factor carries a global baseline score, then shifts with the persona along three axes the model moves on — life-stage (younger ↔ older), affluence (income + employment), and household (living arrangement) — plus a per-market tilt for development stage, tech optimism and sustainability focus. Scores are clamped 1–10. The global baseline marker is the same factor with persona set to "All" and market "Global".
It's illustrative. The baselines, axis coefficients and market tilts are synthetic — calibrated to be directionally plausible, and drawn from no real response set. Sample sizes scale off the model's own per-market counts and demographic splits, so narrowing a persona thins the base the way a real one would (benchmarks suppress below 5 respondents).
To make it real, swap three things for the Benchmarks Extract: the factor baselines, the persona deltas (replace the coefficient model with grouped benchmark scores by characteristic), and the per-country respondent counts. The interaction, ranking and divergence logic stay exactly as built.
Contour · a working prototype · illustrative data throughout