How to turn tourism data into meaningful insight?
Why do we use Business Intelligence?
What is BI?
Many articles have differing narratives. In ‘simple language’ I’d like to explain my view on how Data Science & BI impact the attraction & experience industry, especially in Asia.
01. Asian Tourism: Greatest Opportunities Post-COVID
02. What is BI (Business Intelligence)?
03. Data’s relationship with time
04. Tourism Industry’s Classification Of Data
05. How To Turn Data Into Meaningful Insight?
At 6:30am, I start my day in the gym. The treadmill records data useful for measuring the effects of my workout. Sometimes I exchange this data with friends who also love running, we encourage and challenge each other by sharing our relatable numbers. Also, my monthly InBody test of fat and muscle composition allows me to combine diet with exercise to improve imbalanced indicators as I adapt to my irregular and high-pressure start-up life.
Tracking changes and exchanging relevant information is the simplest way to use data.
But the power of ‘data science’ is of course much bigger. People think this is a very abstract topic. When it comes to in-depth applications such as AI (Artificial Intelligence) and BI (Business Intelligence), some people’s reaction often detached, only commenting “I’ve heard about it.”
In fact, data science has become within reach, and it has quietly changed the world.
Let’s take a look at some trends in the tourism industry after the pandemic.
According to data from the Global Business Barometer of the Economist Intelligence Unit: After COVID, “digital agility” tops the list of areas of importance for companies. “More innovative offerings” and “More informed strategic planning” also ranked high.
Asia’s tourism industry will need to be more concentrated on the application of BI (Business Intelligence) to better prepare for the next crisis.
So, what is Business Intelligence?
Visual analytics platform, Tableau explains “BI combines business analysis, data mining, data visualization, data tools and infrastructure, and various best practices to help organizations make more data-based decisions.”
“In business, you use modern business intelligence to fully understand the organization’s data, and further use these data to drive changes, improve efficiency, and quickly adapt to changes in the market or supply.”
Let’s take a step back, first dividing data by it’s relationship with time:
Analyzing and formulating strategies based on past data is the most traditional data application. Most ‘Things To Do’ companies use historical data to analyze business. Real-time data requires a certain amount of investment, and what it means to be ‘real-time’ is interpreted differently depending on your business’s data culture.
Knowing your business performance of that day does not mean you have mastered real-time data.
Real-time analysis is a discipline that applies logic and mathematics to data, to help you provide immediate insights for pending decisions.
Within a few seconds or minutes of new data arriving useful information is available for action.
Systems can also alert decision-makers when an event occurs where reaction time is imperative to success.
According to real-time data analytics by YOU. Group, I personally think the biggest advantage of real-time data is tourism companies can understand sudden changes, including shifts in markets and tourists.
Change, risk, and opportunity usually happen at the same time, but they are all fleeting: real-time data can undoubtedly show you all three at once.
So-called ‘future data’ is a term for predictive analysis.
By combining historical data and current data, technologies such as mining, statistics and machine learning can be applied to predict future results.
For example, if an attraction wants to promote tickets in a certain market in Asia, we can model the attraction based on all the historical data related to the attraction in this market. This allows us to figure out what price is most suitable in this market, suggesting “which channel” and “how to promote” to achieve “what effect.” The forecast range can be from a few milliseconds in the future to trends and behaviors likely to take place in a few days or years.
Let’s look at the classification of data.
Taking the tourism industry as an example, our data can be roughly divided into:
- Tourism product data (flights, hotels bookings, attraction tickets)
- Digital channel analysis (official website, social media, search engine)
- Tourist personas (preferred destinations, experience sentiment analysis)
- Demographic data (age, nationality, gender)
- Company internal business data (financial reports, reservation system information)
In the ‘tourism product data’ category, the data ecosystem for air tickets and hotels has become more mature. These two sectors also tend to have an internal revenue management team to help analyze data.
However, in the Tours & Activities sector – lack of an internal team for data and limited resources to gather external data is common, some third-party suppliers exist to fill the gap.
Until recently, there’s been a lack of ‘product data analysis’ specifically focused on attractions in the Asian market.
The other four data categories are relatively well used.
There are many digital marketing agencies and research companies that help travel companies maximize their digital platforms, boasting techniques for improved websites and social media campaigns. But imagine the possibilities of a tourism industry where a data community could be used to integrate and leverage all 5 types of data categories.
Understanding real-time data in multiple dimensions could produce more accurate forecasting analysis.
Still, data doesn’t mean anything until it’s correctly applied to the right scenario.
In the tourism industry, we normally use data for:
- Internal analysis (finance, marketing, CAPEX & strategic plan)
- External environment analysis (statistics of outbound and inbound visitors)
- Benchmark data analysis (competitors, penetration rate and market share)
I think everyone is familiar with the first two applications, but how important are competitors and market share?
When you ask a hotel revenue manager –
“Does a full house occupancy mean business is good?”
She will tell you –
“not necessarily, because your competitors all have a full house, and the other hotel’s price is twice as high as yours!”
Similarly, when a hotel owner is challenging your occupancy rate of less than 50%, you can use benchmarking data to prove you are still getting your market share since your competitors who are averaging a 30% occupancy rate.
Hotels and airlines have been part of a benchmarking data ecosystem for more than 15 years. But the attraction ticket industry has not yet started.
The biggest obstacle for businesses in the ‘Things To Do’ Industry is we are not able to measure:
- ‘Who are my competitors?’
- ‘How can I find the same dimension for benchmarking?’
- ‘Which attractions will tourists consider & compare?’
The answers to all these are available in the 5 data categories we talked about earlier.
The greatest value of tourism Data Science & BI is it’s capability to unite product, channel, persona and demographic data by using real-time data and predictions.
Harnessing the potential of internal, external, and competitor data analysis will ultimately help tourism business grow while reducing operating costs and boosting tourist satisfaction.
by Jenny You, YOU. Group Co-Founder & CEO
YOU. Group provides an automated data insight and business intelligence tool for the Tours & Activities Industry.
Have a question about how BI or data science can be applied to your business – get in touch here.