WebinarOnline
Forecasting the future of travel: Economic models, policy impacts and navigating global uncertainties
Friday, 22 January 2027
Learn how forecasts and scenarios can support tourism decisions without hiding uncertainty or promising false precision.
About this event
Forecasts influence hotel investment, transport capacity, workforce planning, municipal budgets and national tourism policy. Geopolitical disruption, economic volatility, public health emergencies and climate shocks demonstrate the limitations of relying on a single estimate of future demand. This webinar examines demand modelling, scenario planning, sensitivity analysis and leading indicators. It will explain how different methods answer different questions and why uncertainty should be represented directly rather than concealed behind an apparently precise headline figure.
Panellists will consider data quality, model assumptions, forecasting horizons and the interpretation of probability ranges. The discussion will examine how structural change can be distinguished from temporary market fluctuation and how policy interventions may alter the behaviour that a forecast attempts to predict. Participants will compare baseline projections with alternative scenarios and stress tests. Attention will also be given to missing data, false precision and the organisational pressures that encourage decision makers to favour confident estimates over conditional evidence.
Participants will learn practical techniques for embedding forecasts within adaptive planning. They will consider how to document assumptions, identify variables with the greatest influence and set review points as new information becomes available. The session will also address clear communication with investors, public authorities and communities whose decisions may be affected by projected demand. Delegates will leave better able to evaluate model outputs, explain uncertainty and use forecasting as a disciplined aid to judgement rather than a substitute for strategic responsibility. This approach supports decisions that remain proportionate when evidence is incomplete and external conditions are changing rapidly.