Living Lab’s first economic model was for the Singapore Grand Prix in 2012. In 2025 we became a software platform: government-grade intelligence for the visitor economy
Visitor behaviour analysis. Living Lab connects anonymised mobility and dwell data, booking and ticketing patterns, and review and survey text, and turns them into behaviour and sentiment vectors tied to a place and period, so rising footfall is seen arriving with rising or falling satisfaction. Reference data is published by UN Tourism.
Shows what visitors did and felt in a place, over a recent period: dwell, flow, timing, and a sentiment signal by zone and segment, with the aggregation threshold and sources stated. Which streets, events, or hours are under pressure, and whether the people affected are satisfied, indifferent, or leaving unhappy. Signal137 gives the board a pattern it can act on and show, not a profile of any one visitor.
Tests how behaviour would shift under a future scenario: a new event, a marketing push, a dispersal measure. Demand137 takes the same vectors forward, so the intervention is placed where the evidence shows friction will form, not where it was assumed.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Signal137 | Is this place, route, or event under real pressure, or only busy-looking? | Aggregated dwell time and flow by zone and time band, not device-level trails | Management action aimed at the zones and hours where pressure and dissatisfaction coincide |
| Signal137 | Which visitor segment is worth marketing to? | Segment-level behaviour and sentiment linked to spend pattern, not individual profiles | Marketing spend redirected toward the segments correlated with value, not only footfall |
| Signal137 | Where and when should we intervene on visitor experience? | Near-current aggregated flow and sentiment by location, on a defined refresh schedule | Signage, staffing, or dispersal placed where the evidence shows friction |
| Signal137 | Is that complaint a demand problem or an operational one? | Sentiment concentrated on wait time and signage at an interchange, separated from crowding in the core | A low-cost fix identified as such, and a capacity problem as such |
| Signal137 | Does this track individual visitors? | No. Aggregated before an analyst sees it, with small cells suppressed | The data-protection officer can see exactly what the figures represent |
| Signal137 | How is sentiment measured? | Review and social text aggregated by place and period, beside dwell and return-visit patterns | The two together, more reliable than either alone |
| Signal137 | Is this real time? | Refreshed on each source's documented schedule, stated in the brief | The refresh cycle is known, not assumed |
| Demand137 | What would a new event or campaign do to the pattern? | The same vectors run forward under the scenario | The intervention tested before it is placed |
| Both | Can we use our own survey or CRM data alongside yours? | Yes, where governance terms allow | An existing dataset strengthens the aggregated pattern rather than duplicating it |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The evidence survives the change of officer or agency |
International visitors to the United States fell 4.2% in 2025, the first annual decline since the pandemic, while worldwide travel grew. The spending did not disappear. It moved. Five economies are absorbing most of it, and the shift now looks structural.
Oman has announced plans for a high-altitude destination at Jabal al Akhdar, combining sports training, wellness, and tourism at 2,400 metres. It is the kind of project a Gulf government can commit to at once, and the kind that lives or dies on a demand case nobody has yet published.
International arrivals to the United States fell 4.2% in 2025 while worldwide travel grew about 4%. Living Lab’s displacement model puts the forgone spending at roughly US$50bn, and about US$10.6bn of it within reach of five economies. Each is limited by a different constraint.
Most destinations run near capacity for part of the year and well below it for the rest. A visitor added in the peak displaces one already there. A visitor added in the shoulder fills an empty room and is served by staff who would otherwise be idle. Almost no destination runs the calculation before fixing a date.
We turn messy event and tourism data into decisions that help communities grow. Bring curiosity and rigour. We will hand you questions nobody has answered yet.
Get the latest from the Fabrik newsroom
