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
Travel demand data partnership. Living Lab does not compete for a platform’s transaction data. It connects and geotags the structured context that data usually lacks, and hands over a vector set with the source, method, and confidence stated for each component, so your product can explain a demand shift rather than only display one. Reference data is published by UN Tourism.
Explains what a demand shift was: the event, the route change, or the price move that caused it, attributed rather than guessed. Your customers, a destination or a DMO, have to defend your number to their own board. Signal137 gives them the working behind it.
Adds the layers your own signal cannot see: the specific driver and expected duration of a demand step-change, whether the demand can be met, and what happens to price if it cannot. Demand by origin, route, timing, stay, and purpose, delivered on agreed data terms. Demand137 is built for integration into your product, not as a replacement for it.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Whether to build a demand-forecasting layer in-house or integrate one | A fair comparison of build cost and time against what a connected signal already adds | The fastest defensible path to a differentiated product |
| Demand137 | What connected context adds beyond our own data | The driver and duration of a demand step-change, and whether supply can meet it | A platform that explains demand before the customer notices it moved |
| Demand137 | How do we explain a demand shift to customers with confidence? | An explainable, attributable method rather than an opaque score | A demand answer customers can act on and justify to their board |
| Demand137 | Can this run as an API or a feed, not a report? | A licensed feed, an API, or a co-developed model built to your specification | Integration into your product, on your timetable |
| Demand137 | What does a proof of concept look like? | One market or signal type, connected once, with what it adds made visible | Your team assesses the value before committing to a wider integration |
| Signal137 | Why did that market move last quarter? | The event, route, or price driver attributed, with the counterfactual stated | The rear-view explanation your customers can quote |
| Signal137 | Which of our customers' campaigns actually moved demand? | Attribution of demand shift to campaign, net of what would have happened anyway | A story your destination customers can defend to a funder |
| Both | Does a partnership strengthen or dilute our own data asset? | Clear terms on ownership, governance, and exclusivity, set before anything moves | Capability added without ceding the asset |
| Both | Are you building a competing consumer-facing platform? | No. Living Lab stays as a data and method layer | The customer-facing product, brand, and relationship remain yours |
| Both | What data do we need to share? | Often none; where aggregated data helps, terms are set out first | Nothing moves until the boundary is agreed |
A hotel pipeline decision is usually made on an occupancy trend, extrapolated forward. That answers a different question from the one a planner, a developer, or an investor faces: is the market short of rooms overall, or short of rooms on the 18 nights a year when demand peaks?
A new route photographs well on launch day. Whether it is still flying in year three, without deep discounting, and still worth the airport incentive that started it, is a quieter question, and it is the one that comes up at renewal.
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.
During the 2026 World Cup, host-city hotel rates rose 13% on average. Miami rose 30.3%. New York fell 4.5%. The average was true and useless: it would have told all 16 cities the same thing, and been wrong for most of them.
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.
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