Research at MODUL University Vienna shows value of Google Analytics’ data for predicting tourist numbers
Data extracted by Google Analytics from travel information sites can contribute to better and more accurate predictions of tourist numbers for large cities, especially for time periods within the next three to twelve months. This is the recently published result of a research project conducted at MODUL University Vienna that analysed the use of selected Google Analytics data sets in estimating future tourist numbers for large cities. The project formed part of the university’s research focus on the use of new media for modern management.
Travel information sites are widely used by people interested in travelling in the near future. Google Analytics, a software designed to track and report web site traffic, collects data on visitors’ usage patterns and provides anonymous and averaged statistics. Currently, this information is mainly used by IT departments in order to optimise web design. Dr. Ulrich Gunter and Dr. Irem Önder at the Department of Tourism and Service Management at MODUL University Vienna in Austria have now proven that there is more to these data than has previously been shown. They evaluated the power of these data for forecasting future tourist numbers for large cities, an important factor for resource management in tourism.
WEB VISITORS & CITY TOURISTS
“We actually analysed www.wien.info, a central web site for city tourism in Vienna”, Dr. Gunter explains, commenting on the work published in “Annals of Tourism Research”. “Altogether we used eleven variables for our forecast models. Ten were provided by Google Analytics for web traffic on this site plus the total number of arrivals. Our results clearly demonstrate that complementing certain forecast models with Google Analytics data can make the models quite powerful for predicting future numbers of tourists to a given destination.”
The Google Analytics variables used by Dr. Gunter and Dr. Önder included Average Session Duration, Average Time on Page, Bounce Rate, New Sessions, Page Views, Returning Visitors, Social Network Referrals, Total Sessions, Unique Page Views, and Users. All of these data were extracted during a period of time beginning in August 2008 and ending in October 2014.
MANY VARIABLES & ONE RESULT
The team used the data for so-called vector autoregression (VAR) models. These are econometric forecast models suitable for cases where several mutually causal variables need to be integrated. “Overall we found that models that included the Google Analytics data predicted future tourist numbers better for time periods of three to twelve months ahead than models without these additional data,” explains Dr. Önder. “Whereas for shorter time frames, models without them performed better.” In order to assess the performance of the respective models, the team compared predicted tourist numbers with actual values provided by the TourMIS database, a leading European database for tourism information developed by research associates of MODUL University Vienna.
“Our main challenge was the amount of the available data, making our project a big data challenge”, adds Dr. Gunter. “We were able to apply appropriate data shrinkage and forecast combination methods that allowed us to feed all ten data sets from Google Analytics into our forecast models. Here, the significant know-how in big data analysis at MODUL University Vienna was a great support as was the data access generously provided by the Vienna Tourist Board.”
RESEARCH ACROSS BOUNDARIES
In fact, the project undertaken by Dr. Gunter and Dr. Önder exemplifies the interdisciplinary approach that MODUL University Vienna takes when it comes to research. “Our students benefit from rich research activity”, explains Prof. Dr. Sabine Sedlacek, the recently inaugurated Vice President of MODUL University Vienna. “We love to cross the borders of traditionally defined research areas and combine them in ways that are necessary to meet the global challenges of tomorrow. My colleagues’ project is a great point in case, where new media analysis, big data shrinkage and research in tourist movements are combined to create a powerful tool for the tourism industry. That is the kind of stuff that we want to develop in our research activities and teach our students.”
Forecasting city arrivals with Google Analytics, U. Gunter & I. Önder, Annals of Tourism Research 61 (2016) 199 – 212.
MODUL University Vienna is an international private university in Austria and is owned by the Vienna Chamber of Commerce. It offers study programs (BBA, BSc, MSc, MBA and PhD programs) in the areas of International Business and Management, New Media Technology, Public Governance and Sustainable Development, as well as Tourism and Hospitality Management (www.modul.ac.at/study-programs). The study programs meet strict accreditation guidelines and, due to their international focus, are conducted in English. The university campus is located at Kahlenberg, in Vienna’s 19th district. Research at the Department of Tourism and Service Management focuses on Policy Evaluation, Perspectives on Tourism Demand, Entrepreneurial Challenges in Fast-Changing Environments and Destination Competitiveness and Development.