Visitors leave a trail before they arrive and after they leave: what they search for, what they say, where they go, and what they spend. Living Lab reads that trail against the economic model, so behaviour and value are one picture.
One model of the visitor and the place. Search interest by origin market and language, weeks before the booking. Social and news sentiment by place and period, read against the event calendar. Spend by visitor purpose, from the same baskets that feed the economic engine. Flow and dwell by zone as the movement layer goes live. Signal137 shows what visitors did over a period and what it was worth. Demand137 shows how a new event, route, or rule would move the pattern.
What visitors did in a place over a defined period and what it returned. Which origin markets searched, arrived, and spent, segment by segment: business, leisure, sport, visiting friends and family. What they said about it, from social listening, laid along the calendar so a shift in mood lines up with the event that caused it. Spend by sector and by zone, attributed to the events and markets that brought it. Every number marked observed, derived, or inferred.
How the pattern would move under a proposed change: a new event, a route, a rental rule, a transport link. Where the pressure would land by zone and week, against the access ceiling for each. Search interest and sentiment in the origin markets are the leading indicator: a shift shows in the signal before it reaches the booking curve. The segment worth marketing to, identified by spend rather than footfall. The gain from acting priced alongside the cost of waiting.
| 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 and a capacity problem told apart |
| 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 read together, each checking the other |
| 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 |
Flow and dwell by zone, connected to the event and the accommodation cluster. The destination sees which zones benefit and which are bypassed.
Behaviour segmented by origin: what each market searches for before the trip, how long it stays, what it spends and on what, and what it says afterwards. A long-haul visitor and a domestic day-tripper spend differently, and the destination markets to each on evidence.
Sentiment by origin market, laid along the calendar with the events, the news, and the price moves that touched it. A shift shows in the signal weeks before it shows in the bookings, with the cause named, so the destination responds to the mood rather than to the arrivals figure that follows it.
Sentiment by theme, zone, and segment tracked over time. The destination sees what visitors praise, what they criticise, and whether the experience is improving or declining.
Dwell, flow, and sentiment connected to the spend and economic model. The zones where visitors stay longest and spend most identified.
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.
