November 20, 2020

Accurate forecasting is crucial to enable hoteliers to efficiently allocate hotel resources and refine pricing strategies” is just as true today as it was when this group decided to study hotel demand using a DMO’s web traffic data.  Their belief was that other forecasting models of the day were very susceptible to accuracy reduction from any dramatic changes in the economy” and the accompanying shocks to the tourism industry.  Their idea was to focus on hotel demand combined with looking at a new type of online data, namely, the web traffic volumes.” 

What peaked my interest about this article is that just seven short years ago these researchers saw the thought of looking at website visits as an indicator of visitation the new frontier for research.  Another interesting idea they brought forward was the use of varying types of online pulse data such as search engines and social media to predict various economic activities in the hospitality industry. 

As for the website visits, the researchers saw them as the logical next step in the purchase process after a consumer finished combing through search engines.  They found website visits to be “even closer to the actual conversion.”  The data from various traveler surveys further corroborated the importance of a DMO website in trip planning. 

 The researchers utilized Charleston South Carolina as the target destination for their case study.   Just as we would today, they utilized Google Analytics for the website data and STR for hotel demand and occupancy.  The main research question was to find out whether the website traffic data from a local DMO helps to improve the accuracy of the forecast for hotel rooms and hotel occupancy rates.   

To confirm the relationship between web traffic data and hotel demand, one approach was to conduct a Granger causality test.  The results showed a significant reciprocal Granger causality between the two sets of variables: web traffic volume tends to Granger-cause hotel demand/occupancy, while hotel demand/occupancy would also Granger-cause web traffic volume.”  They looked at  several other models as well but the bottom line was that they found that “the significance of DMO web traffic data in predicting the demand for hotel rooms validates the crucial role of DMOs in promoting a destination and connecting travelers with local tourism and hospitality services. 

I found this ultimate validation very interesting from the standpoint that CVBs are often found in the position of having to justify their value.  This study was also very forward thinking in that it was seven years ago and they were certainly on the right track to connect the value of looking at website visits to predict future hotel demand.  Companies like ADARA today used this as a foundation to build a business on watching searches and demand to predict trends in visitation. 

Submitted by:  Dee Ann McKinney      Missouri Divison of Tourism

Editor Note: The study the author is reflecting upon is available free to the public until September 2021 as a part of the Tribute to TTRA’s 50th anniversary from the Journal of Travel Research.