Predictive Analytics for Tourism

John Wilson (PI), Mark Dunlop and Andreas Komninos

Raw tourism data can be collected by stakeholders and manually fed into visualisations that provide insight (essentially aggregation and reporting) into tourism data and trends. Such users are typically aiming to maximise the value of internal and external historical datasets, utilising these assets to predict future tourist trends and activities, as well as to inform strategic decision-making. Utilising a mix of datasets can to help develop predictive analytical and modelling capabilities. The functionality that can be used to predict likely trends and scenarios promotes the move from reactive to pro-active intervention models for informed management in the tourism industry.

The project supports a broader data-related strategy focused on leveraging data from digital technologies that influence consumer behaviours. The aim is to help potential users to gather and use more dynamic, live information thereby enabling businesses to respond more quickly to changing marketplaces and to deliver a personalised service to tourists and visitors. From a tourism economy perspective, the new insights help with the complex process of identifying visitors before they reach a destination and promoting meaningful engagement opportunities.

Project funded by DataLab and run in close collaboration with Glasgow City Marketing Bureau.

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