Living Lab does three things for a route development team. First, the economic impact report: what a route delivers to the destination in visitor spend, room nights, GVA, tax, and jobs. The subsidy and value conversation with an airport or a government then starts from evidence. Second, an economic layer on the route decision. When two city pairs are equally profitable, Living Lab evaluates each on what it returns to the economy and how well it fits the host nation’s strategy, at both ends of the route. Third, sequencing: the order in which candidate routes should open to grow the economy and attract investment fastest. All three on one model of every route, its seat capacity, the hotel supply at each end, the event calendar, and the demand from each origin market.
The economic impact report for a route already flying, at both ends: visitors by origin, spend, room nights, GVA, tax, and jobs. Each is attributed to the route and net of what other routes would have carried. Whether a route incentive paid back. Which routes create destination value and which only fill seats. Season over season, on one method, so the renewal or the subsidy review starts from a track record.
Equally profitable city pairs ranked on what each returns to the destination and how it aligns with the national strategy, at both ends: sector priorities, target origin markets, investment themes. The sequence in which candidate routes should open to grow the economy and attract investment fastest, with the hotel supply and the calendar at each end as the ceiling. Every number marked observed, derived, or inferred.
| Product | Your question | You get | What it changes |
|---|---|---|---|
| Demand137 | Is this route viable at launch, and at what frequency? | Demand vectorised by origin, timing, purpose, and price sensitivity for that specific city pair | The route sized to the demand that is there, not the comparator route's demand |
| Demand137 | When to grow, hold, or withdraw an existing route | Current performance against the vectorised demand case, not load factor alone | The network change timed to the evidence, not the review calendar |
| Demand137 | How much destination value does the route deliver? | Route demand connected to accommodation, event, and investment signals in the destination | Incentive funding tied to destination value, not passenger volume |
| Demand137 | Should we commit airport incentive or public co-funding? | A bounded case with a stated counterfactual and downside | Funding committed only where it demonstrably changes the outcome |
| Demand137 | Is the extra daily frequency worth committing now? | The daily case from month six, subject to a load-factor trigger, net of the added capacity cost | The frequency released when the stated trigger is met |
| Demand137 | Can this work for a route with no comparator anywhere? | Built up from origin-market and destination-side signals, not borrowed from another city pair | No dependence on another route looking similar enough |
| Signal137 | What did the route we supported actually deliver? | Attributed destination value against the case that funded it | The renewal negotiated from evidence |
| Signal137 | Did the incentive buy anything? | The route's result set against the counterfactual of no support | Public money is not spent on demand the market would have delivered anyway |
| Both | Does this need airline booking data we cannot share? | No. Route demand vectorised from destination-side and market signals | The airline's data is not a precondition |
| Both | Who holds this, and where? | Model, data, and a trained team inside your jurisdiction | The capability survives the change of planner or official |
Living Lab separates induced demand from displacement. A route that moves passengers from a competing hub delivers less to the destination than one that creates visits that would not otherwise have happened.
Attribution runs annually on the same spine as the original case. The airport board gets a like-for-like comparison between what was committed and what arrived.
Each candidate is scored on visitor spend, hotel nights, GVA, tax, jobs, and FDI fit in the destination, and comes back ranked. The route that fills the most seats does not always deliver the most value. The order that builds destination value fastest rarely matches the yield table.
Living Lab runs the route through the destination’s economic engine with SAF cost carried. It shows base, downside, and supported scenarios, the counterfactual stated, and the return on public co-funding, so the board sees the margin before it commits.
The methodology is identical in every one of the 49 countries on the spine. A route assessment in Edinburgh is directly comparable to one in Doha or São Paulo.
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.
A race track, an arena, a stadium: each is built for a peak it reaches a few dozen times a year. The gap between peak and off-peak operational need is the commercial challenge behind every sports-anchored development, and the reason mixed use is the answer rather than the fashion.
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.
