Welcome to the Future of Tourism: How the Tourism Sector is using your Data
With proper access to big data and the analytical tools to draw insights from our travels, the tourism sector could vastly improve world GDP: 10% of global GDP is already attributed to the tourism sector, according to the World Tourism Organization.
Customers can be targeted throughout their travel booking process as well as their entire stay, maximising the effects of advertising and holiday packages as well as the preparedness of local areas to support the tourist’s needs precisely.
This story looks briefly at how big data can improve the tourism sector in the future can pave the way for a more digital, connected, and personalised travel experience that can be tailored based on real-time data.
4 Types of Big Data that are improving the Tourism Sector
Data from GPS
The previous and current model for understanding where locals and tourists are moving around to, and why, came from surveys. For many researchers and companies, this still provides the most complete information regarding movements, distance and travel for work or leisure. The issue with this is that surveys for this type of data collection are conducted by landline, which means half of the population, who no longer use landlines, are not included in these vital statistics for improving tourism.
GPS data from our mobile phone itself or installed apps such as Google Maps or social media sites, collects superior movement data that can help local tourism companies direct their resources to the most popular tourist spots. This is particularly essential for boosting big data on tourist spots in remote locations where other data is not available, such as from restaurants and hotels, and can allow analysts to understand and respond to peaks in new locations.
Protecting privacy is essential and collecting the right data without being invasive is key.
Data from Web
Unlike feedback data from surveys, which are delayed by time, web data is proving to be the key to understanding trends in tourism, right down to the second. Data from online searches will indicate what places are becoming popular, and at which times of day resources are best used to promote certain locations. Sites such as Wikipedia and TripAdvisor form a surprising resource for tourism research, as it indicates which pages are being updated more frequently, or new locations that are added to their site, in addition to flows of page views. This can help the tourism sector track interest in a precise and time-related fashion.
An issue with this is that web searches don’t always relate to actual interest in travelling to an area, but by using this information in combination with other searches, such as travel information or places to stay, will help analysts to refine their understanding of true interest. Another thing to bear in mind is that big data analysis in this way captures data from a variety of sources, some of which are third parties. Not only does the travel company who needs this tourism data not own it, but they have no control over its quality. In a world where data is becoming more and more central to how businesses work, especially in the tourism industry, it is becoming vital that each company holds their own data so they are in control of their own insights. This will become even more essential amidst ever-updating data-sharing policy which is closing down on who can share what data with who. Protecting privacy is essential and collecting the right data without being invasive is key, according to the World Travel and Tourism Council’s report.
Data from Payments
A tourist’s trace is scattered like breadcrumbs through their trail of payments in hotels, restaurants, shops and attractions. Comparing seasonal fluctuations in retail and hospitality turnover can help to identify how much of their profit is coming from tourists, especially in regard to the most popular sectors, for instance whether tourists in Milan are especially good at bringing in money through retail or those visiting London have a higher chance of boosting trade on local attractions. The downside to this sort of data analysis for the tourism sector is that while the world is becoming ever more digital, many of those travelling will still choose to withdraw cash, especially if changing currency, meaning that a lot of this data is not gathered. That said, businesses can still interpret their overall turnover in relation to seasonal flows. Banks that allow overseas payments without exchange fees will be essential for the tourism sector to be able to utilise big data for the benefit of tourism and the local communities.
Data from Crowdsourcing and Social Media
Tourism and photo taking have always been well paired together. Today, with social media, many of these photos go online, often with geo-tags identifying the exact location. This provides analysts with the information they need to know exactly what spots tourists are visiting, and how many people are engaging with that area in relation to other areas. In addition to this, crowdsourcing sites such as Wikipedia provide information on which places are most frequently updated on the site, suggesting possible popularity. Flickr was recently used to study the footprint of summer tourism in data poor areas such as the Artic, where no information is collected other than photos posted online. This allowed the researchers to identify a 600% rise in local tourism the last 10 years.
It will be important for businesses doing this sort of analysis that they own this data themselves, not only for data privacy reasons but so they have constant access to the flow of data. If they were renting information from a site such as Flickr or Instagram, they never have control and may lose their vital connection to their business insights if anything went wrong with the business, or new data sharing legislation is to be put in place.
“Data is increasingly central to business. We have noticed big companies are now using data for better performance or for AI projects. But it seems businesses are cornering themselves by relying on third parties for Data. We think this is a bad idea, because this data is not collected in the same context as the business needs to apply it. Resulting in inaccuracies and higher risk of failing data project” says Humayun Qureshi, Co founder of Digital Bucket Company.
He added, “We encourage our clients to develop their own data collecting capabilities, and at first this seems far fetching. But with a little more insight in the data needed and technologies it is possible with a much far reaching advantage”.
In addition, there is an issue with using data in relation to time-stamps. Posting time is not always accurate with the time the area was actually busy, and in addition, posts could originate from locals and not tourists, therefore biasing the data towards assuming all posts are from external visitors. A good way around this is using algorithms that can determine the usual location of user, helping parse locals from tourists.
Conclusion
For the time being, household surveys remain a useful tool for data collection, but big data is becoming increasingly more important for the tourism sector, allowing them to collect insightful data with more granularity and accuracy thanks to mobile phone data and advancing machine learning algorithms. Newer sources of data, from geo-tagging, user images, transactions and web searches will shape our understanding of tourism and how it is evolving.
The COVID-19 pandemic has already seen a globally adopted campaign to trace who is travelling where and for what purpose, for the safety of the community. Knowing more detailed information about our travels, through online data as well as track and trace forms, will become a vital tool for understanding global movements in an attempt to tackle a pandemic and simultaneously boost the economy using the big data they collect.
This was written by a researcher at a specialist data company. The Digital Bucket Company operates in the UK and works with clients in overcoming data challenges including privacy concerns.
