Funding: UMAMI is the interdisciplinary research project funded by Innovation Fund Denmark
Project members:
Academic Institutions: Copenhagen Business School (Project Leader: Alexander Josiassen. Project Manager (deputy): Fumiko Kano Glückstad), Technical University of Denmark (DTU Compute contacts: Mikkel N. Schmidt (Technical Manager), Morten Mørup, Kristoffer Jon Albers). Industrial Partners: Visit North Sealand, Visit Carlsberg, Wonderful Copenhagen, Visit Denmark
Duration: 2017-2020
Summary
Tourists increasingly expect to be addressed and met on their own terms. This can only be achieved if businesses can handle complex insights regarding culturally diverse subgroups of tourists, and their behavior across the global market place. The key challenge is to understand how tourists prioritize and make choices. A recent and powerful technique to address this is Big Data analysis. Any analysis, however, is meaningless without fully comprehending the mental representations that the target group has of the destination. Thus innovative and intelligent use of Big Data is a key to the success of a small country like Denmark. “Regional vækst- og udviklingsstrategi” by the Greater Copenhagen states that 20% of the employment in the region is supported by the tourism and creative industries, and 10% of export from the capital region is supported by the creative industry. This clearly indicates that an improvement of the image that foreign tourist hold of Denmark will have a substantial effect on increasing employment and export sales.
A specific project scope is to acquire a deeper understanding of tourists from emerging tourist countries (TETC) with special focus on Chinese tourists, by developing a formalized framework that investigates travel motivations, goals as well as mental pictures that TETC tourists have of Denmark as a tourist destination. This approach is accompanied by a complementary analysis of performance drivers of the tourism industry that enables us to measure the competitiveness and growth potential of the Danish tourism industry. Another vital scope is to integrate the aforementioned novel theoretical framework into “a segment-based data collection platform” enabling the intelligent analysis of complex and diverse intercultural segments of potential TETC, by employing state-of-the-art machine learning technologies. This can provide an efficient “segment-specific” communication strategy to attract more TETC tourists to Denmark. The project further proposes a process to tailor Danish tourism offerings to different types of potential TETC. Finally, the project provides insights into the exciting possibility of spill-over effects on Danish exports in the tourist’s home country.
/By Fumiko Kano Glückstad, CBS, 03/01/2017