Data Visualization on Tourism
Hanlin Xiao, Jie Cheng, Yunfan Lyu, Yuqing Ma, Dongxu Sun, and Qian Wu
Data Sources
We obtained data from the following sources:
· OCED (The Organization for Economic Co-operation and Development)
database:
The Organization for Economic Co-operation and Development (OECD) is an
international organization that works to build better policies for better lives. The data
extracted from the OCED database are summarized in Table 1:
· STAN (Singapore Tourism Analytics Network) database:
Stan is a data analytics platform to view visualizations and perform analysis on
tourism-related data, aggregated from STB and the industry. These data help to
derive actionable insights about Singapore’s visitors and the data obtained from this
database are shown in Table 2:
Data Visualization and Analysis
In 2020, Singapore tightened the restrictions on border entry. Measures like Stay-athome Notice (SHN) for 7 days or 14 days were required for travelers from most
countries. This lengthens the stays and costs for tourists significantly. Figure 1
H. Xiao (B) · J. Cheng · Y. Lyu · Y. Ma · D. Sun · Q. Wu
Nanyang Business School, Nanyang Technological University, 52 Nanyang Avenue,
Singapore 639798, Singapore
e-mail: hanlinxiao01@outlook.com
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Y. Y. Nguwi (ed.), Tourism Analytics Before and After COVID-19,
https://doi.org/10.1007/978-981-19-9369-5_11
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Table 1 Data in use on
tourism revenue and
expenditure
H. Xiao et al.
Tourism revenue and
expenditure
Tourism revenue by country
Tourism revenue by category
Tourism expenditure by country
Tourism expenditure by category
Hotel supply and demand
Maximum Room/Night
Available Room/Night
Gross Lettings Rooms/Night
Paid Lettings Rooms/Night
Standard Average Occupancy Rate
(%)
Standard Average Room Rate(S$)
Revenue Per Available Room(S$)
Table 2 Data in use on
visitors’ arrivals and hotel
statistics
Visitors arrivals
Visitors arrivals by year
Visitors arrivals by geography
Visitors arrivals by demography
Visitors arrivals by behavior
Hotel statistics
Gazetted Hotel Statistics
Gazetted Hotel Statistics By Hotel Size
Gazetted Hotel Statistics By Hotel Tier
Room Revenue of Gazetted Hotels
Supply of Hotels and Hotel Rooms
Room Stock of Gazetted Hotels
displays the overall trend in the past 10 years since the year 2010. It records the
influence of COVID on Singapore’s Tourism on arrivals, visitor’s days, and receipts.
Although these data experience seasonal fluctuations, the number of arrivals, the
total number of visitor days, and the tourism receipts were on an increasing trend
in the 2010s onwards. The numbers shrank dramatically in the year 2020 due to the
border restrictions and the public’s concerns about COVID-19.
The number of arrivals and the total visitor days plunge drastically in the year
2020 as compared with the years before. Singapore took some active measures to
contain the spread of COVID-19 which led to tourism starting to recover from May.
Although the speed of recovery was slow, it was showing progress.
Another trend that is important to observe is the increase in the average length of
stay as depicted in Fig. 2. This trend can be observed when we compare the situations
in the year 2019 and 2020. Due to the requirement of a Stay-Home Notice, visitors
have to stay longer when they arrive even if their traveling plan was meant to be a
short-term visit.
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Fig. 1 General trends over time on arrivals and tourism receipts
Fig. 2 Detailed trends over time on arrivals and length of stays
The influence of pandemic on tourism is significant not only in terms of time, but
also at countries level and affects the mode of arrivals. Figure 3 compares the visitor
arrivals, visitor days, and the average length of stay of tourists from different regions
in November 2019 and November 2020, without pandemic and with pandemic,
respectively.
Inbound tourists arriving in Singapore mostly come from China and Southeast
Asia regions. Unlike tourists from China, tourists from Southeast Asia prefer a shorter
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Fig. 3 Visitor arrivals, visitor days, and average length of stay trends by region
duration of visit, mainly because of the shorter distance between Southeast Asia and
Singapore and it is easier to set up a short-term visit to Singapore.
There are also some other interesting facts. The growth rate of the average length
of stay of tourists from China is the highest, partly due to concerns about travel
restrictions for going abroad and the strict quarantine regulations. A large number
of tourists canceled their plans to travel to other countries with stricter restrictions.
In the meantime, the average length of stay of tourists from Europe did not increase
as fast as that of tourists from other regions.
The change in countries of origin can be seen in Fig. 4. Indonesia used to be the
largest group of tourists arriving in Singapore. However, the pandemic situation in
Indonesia was not promising at that point of time and stricter SHN measures were
imposed for Indonesian tourists. China surpassed Indonesia as the largest group of
travelers in Singapore.
The staying preference is different for tourists from different countries as shown
in Fig. 5. The tourists from the two countries with the largest group of tourists, China
and Indonesia, mostly come for a shorter time of visit. This is in sharp contrast to
the other countries with lesser travelers, who typically stay for a longer period of
time during their visits. India and Philippines belong to this group. Worker or student
could made up part of this group from India that explains their longer visits.
Figure 6 shows the details of how the mode of transportation changed during this
pandemic. In the pre-COVID period, the total number of arrivals was increasing by
around 10% yearly, mainly contributed by the increase of arrival by air. Arriving
by air is the largest proportion of arrivals, while sea and land have a much smaller
proportion. The strict traveling ban has taken its toll on the dramatic downturn as
shown in Fig. 6 in the first quarter of the year 2020.
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Fig. 4 Visitor arrivals, visitor days, and average length of stay trends by origins
Fig. 5 Visitor arrivals by average length of stay and visitor days
Figure 7 illustrates the distribution of mode of arrival during the pandemic, it can
be seen as the reflection of travel recovery during COVID. The number of travelers
in all three modes reached a near-zero level in April and May and started to show
signs of recovery in June at a moderate speed. In terms of proportion, traveling by
air is the mode with the largest proportion before COVID, it remains in its place and
makes the most contribution to the total arrivals. Arrivals by sea recovers faster than
arrivals by land, which indicated stricter land regulations that were imposed on land
travelers.
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Fig. 6 Distribution of mode of arrival from the year 2008 to 2020
Fig. 7 Distribution of mode of arrival during the pandemic
We follow by analyzing the impact from a demographic perspective using data
visualizations. Figure 8 records the visitor arrival distribution for different age groups
from the year 2008 to 2020. We can see the total visitor arrival increase mainly comes
from travelers aged 25 and above.
Figure 9 shows the distribution of visitors’ arrival starting from April 2020, this
is the starting point of the recovery for international travel. We can see that travelers
aged from 20 to 44 make up the largest group, while the elderly aged above 55 recover
at a slow speed. The elderly group is less likely to due to high COVID fatality in this
age group.
Gender distribution does not change at a significant level before and during the
pandemic. We now further analyze the change in distribution for the length of stay.
Figure 10 shows the stacked distribution of different lengths of stay. As is shown in
the figure, a length of stay of less than 3 days makes up the majority of travel and
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Fig. 8 Visitor arrival by age groups from the year 2008 to 2020
Fig. 9 Visitor arrival by age groups during the pandemic
contributes to the most increase, more than 70% of travel are less than 4 days and
around 40% of travel only last for 1 day.
Figure 11 shows the distribution of length of stay starting from March 2020, this is
the starting point of recovery for international travel. We can see that the distribution
for length of stay changed dramatically. Travel stay for more than 15 days consists
of the majority of all travel. In months like May, June, and July, more than 80%
of travel lasts for more than 60 days. This drastic change is a good illustration of
the implementation of SHN mentioned previously which lasts for 14 days for all
international travelers from May to July. The average length of stay began to fall in
July, but the travel with less than 10 days of stay remains at a similar proportion as a
result of changing SHN requirements for visitors from several countries and regions.
Also, the increase of 15 to 29 days of length of stay reflects the recovery of leisure
traveling.
This section shows the statistics of standard average occupancy rate (AOR), standard average room rates (ARR), and revenue per available room (RevPAR) by hotel
tier. The hotel tiering system is a reference system developed by the Singapore
Tourism Board (STB) to categorize different hotels in Singapore into tiers based
on a combination of factors that include average room rates, location, and product
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Fig. 10 Length of stay from the year 2008 to 2020
Fig. 11 Length of stay during COVID
characteristics. And the tiers can generally be categorized into Luxury, Upper class,
Midtier, and Economy segments.
First comes the luxury segment. Figure 12 shows the relevant indicators like
occupancy rates, room rates, and revenue per room for luxury hotels over the years
2008, 2012, 2016, and 2020. Looking at the Standard Average Occupancy Rate from
the top graph, we can see that the occupancy rate stays relatively stable with slight
fluctuation across the whole year. On the next two graphs for Standard Average
Room Rate and Revenue Per Available Room, both the price and revenue remain
at a high level across the year. A significant increase can be observed starting from
August, reaching a peak in September and then slowly thins out until December. The
period from August to September is the peak season for tourism as most students are
on vacation, and families have free time to travel. Due to the increase in customer
demand, the average room rate has risen as a result, this is accompanied by an increase
in revenue per available room. However, due to the influence of COVID-19, visitors
from different countries are restricted to enter Singapore and even local residents
are limited in many aspects of activities. The hotel industry is also impacted by the
interplay of viral spread, government policy, and social behavior. The impact of the
pandemic is marked in all the trendlines for the year across all indicators.
What is gratifying is that under the influence of the aggressive epidemic prevention
measures taken by the government, the hotel industry has improved since June and
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Fig. 12 Luxury hotels indicators across years 2008, 2012, 2016, and 2020
has shown a steady upward trend. However, due to limitations like social distancing
and entry restrictions for foreign tourists, both the occupancy rate and revenue per
room stay are at a relatively low level as compared with previous years.
From the above analysis result, both upper-class and midtier segments have similar
trends in the three indexes mentioned above, with a recovery trend slightly earlier than
that in the Luxury segment. We should focus on the performance in the Economy tier,
which shows a significant difference from the other tiers. The figure for the monthly
trend of Singapore economy hotels across the years 2008, 2012, 2016, and 2020 is
shown in Fig. 13. From Fig. 13, we can see that the economy class is faring better
than other tiers when the constraint of social gathering is imposed. It responds to the
restriction more quickly than other tiers of hotels since the occupancy rate climbs
up from April to August and remains high until October. The reason for this may be
that many luxury and upper-class hotels have to close temporarily, economy hotels
can stay open at lower occupancy rates due to lower operating costs. Even though we
can see the standard average room rate and revenue per available room remain low,
better demand and lower operating costs suggest that economy hotels will recover
faster. That would be consistent with what we would normally see in past crises, like
the financial crisis in the year 2008. Hoteliers should build up resiliency and spread
out the risks to different markets.
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Fig. 13 Economy hotels indicators across years 2008, 2012, 2016, and 2020
The final analysis in this by tier hotel partition would be the entire overview across
the years from 2011 to 2020 as shown in Fig. 14. We can see that even though the
number of gazette hotels keeps rising, the revenue of hotels across tiers shows a huge
decline in 2020. This is not the case for previous years, inferring from past trends
we should expect that the revenue of hotels will keep climbing up in the absence of
social gathering constraints. Besides, from the graph of revenue per available room,
we can see the most significant impacts of COVID-19 in luxury hotels, making the
revenue per room decline from $403.90 to $142.10. Both upper-class and midtier
hotels only witness up to 50% decline as compared to the luxury segment.
We next analyze the performance of hotels in another dimension—the hotel size.
The relevant indicators are the standard average occupancy rate (AOR), standard
average room rates (ARR), and revenue per available room (RevPAR) by hotel size.
The hotel sizing system is a reference system developed by the Singapore Tourism
Board (STB) to categorize different hotels in Singapore into different sizes based
on a combination of factors that include average room rates, location, and product
characteristics. The sizes can generally be categorized into large, medium, and small
segments.
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Fig. 14 Overview of hotel performance by tiers from the year 2011 to 2020
Large size hotels usually can accommodate more travelers, but at the same time,
they also need a high average occupancy rate to compensate for higher operation
costs. We look at the monthly trend of Singapore large size hotels across the years
2008, 2012, 2016, and 2020 in Fig. 15. It shows that even though the average occupancy rate shows a quick recovery from 40% to almost 70% due to the fact that
Singapore has acted quickly and has been responsive to changing conditions, the
average room rate and revenue per available room was still heading south. Under
this test of the epidemic, it is difficult for large-scale hotels to increase hotel average
occupancy rates while keeping the existing room rates. Their usual strategy is to
attract more customers by providing higher quality services and lowering the unit
price of hotel stays. Thus, there is no sight of an upward trend in the revenue per
available room.
However, cases are quite different in the performance of medium and small-size
hotels. For instance, from Fig. 16. Below, we can see that it is quite easy for smaller
size hotels to recover their average occupancy rate. Since smaller hotels do not need
to consider the average occupancy rate, they can spend more time on the quality of
the services they provide and increase the unit price of the hotel in parallel with the
additional service provided. Thus, the revenue is shown in pick-up.
For the yearly trend of the standard average occupancy rate (AOR), standard
average room rates (ARR) and revenue per available room (RevPAR) across hotel
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Fig. 15 Large size hotels indicators across years 2008, 2012, 2016, and 2020
sizes from the year 2008 to 2020, we can conclude that the hospitality industry is
sensitive to social responsibility in a pandemic, this is a classical example of a black
swan event. Under the challenge, strategic adjustments need to be made to the hotel
in time for different tiers and sizes.
Recommendation
The economic impact of COVID-19 has clearly battered the entire tourism industry.
The pandemic has also accelerated the push for going online and shifted to online
channels and experiences such as electronic payments and early check-ins. Changes
in consumer preferences and behavior are likely to continue. Hence, businesses need
to be agile in adapting to the new paradigm.
One recommendation is to take measures to attract travelers for long-term visits.
The average length of stay for tourists increases a lot in the year 2020 and a larger
fraction of them came to Singapore for long-term visits. As compared with those
coming to Singapore for short-term visits, long-term visitors generally do not take
traveling as their main purpose of staying in Singapore but studying or working
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Fig. 16 Small size hotels indicators across years 2008, 2012, 2016, and 2020
instead. This group of visitors, typically visit attractions on weekends or public
holidays. They have a more spaced-out time frame for touring.
Therefore, considering this trend is likely to continue, tourism agencies can
conduct a series of measures to attract long-term visitors for traveling. Holiday and
weekend discount on tickets for example. This fits the travel pattern of this group of
visitors.
Following the analysis of changes in demographic distribution and length of stay,
we can see that although the length of stay may be influenced by the SHN period,
the length of stay is still longer than ever before the pandemic after deducting the
SHN period. Previously, the majority of travel plan only lasts for less than 3 days,
but this situation changes dramatically during COVID.
Moreover, in the analysis of demographic distribution, especially the age distribution, we can see that the average age of travelers is lower because of the lesser
elderly travelers. It is, therefore, recommended that local travel agencies and hotels
adjust for younger travelers and cut down on resources or packages planned for the
older age group. Travel agencies and hotel operators should consider this dynamic
change and react to these changes to better allocate resources and budgets.
The next step is to transit to a more technologically enabled transformation. Artificial Intelligent (AI) solutions with machine learning algorithms connect to big
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data and provide precise estimates across various relevant industry and risk management metrics, enabling businesses to significantly improve their decision-making
capabilities.
The adoption of AI is gaining traction in the hospitality industry like chatbots,
which aims at improving the experience of hotel stay and addressing guests’ queries
promptly. Hotel chatbots analyzed data from a wide array of sources (interactions
with guests in the hotel app, gathering purchase history, food preferences, stored
payment options, spa, amenity usage, etc.) to provide a personalized experience.
The more the data collected for the chatbot’s algorithms to learn from, the better
the delivered outcome and the chatbot’s suggestions are. Furthermore, AI-driven
chatbots have a quick response time: guests can receive answers to their queries
almost immediately as if they speak to serving staff facing them directly.
Chatbots are positioned to alter the operational backbone of the hospitality
industry, starting with business processes like the booking process and streamlining
workflows at call centers and other hotel support units. Machine Learning (ML)
algorithms in chatbots can be trained to utilize historical calls with customers and
their booking behavior on a hotel website, offering them the most relevant booking
options.
Mobile app will continue to be the backbone in the process of improving the
technology behind the next-generation hotel experience. A better hotel app allows
for two-way communication between guests and the hotel operator: guests can access
hotel services and other information anytime (for example, order room service dinner
while they are still in the spa). The hotel can use the application to get in touch with
guests at the right moment like sending important notifications, updates, offers, and
alerts.
Some important features a hotel app can offer are listed below:
·
·
·
·
·
·
·
·
Booking options
Remote check-in/check-out
Restaurant booking with in-app menus
Chat with staff
Guest services (in-room dining, laundry, etc.)
Hotel map
Other timely information (flight schedules, hotel entertainment)
Room key functionality.
Today’s smart room apps represent only the first iteration on the way to nextgeneration hotel rooms. There is plenty of room for innovation, to help shape the
future of tourism industry with fresh ideas.
The other future trend is the adoption of AI for hotel pricing. The challenge of
changing dynamic in tourism industry presents opportunity on how to set appropriate
price for hotels of different tiers and sizes. One suggested way is to use Artificial Intelligence to identify leading indicators that signal and recommend pricing strategies as
markets decline or recover. During this significant period of disruption, travelers are
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certainly more price sensitive. Anxious hoteliers engaging in price slicing wars may
suppress pricing to reduce losses. Intelligent pricing is a channel-agnostic approach.
Instead of direct bookings, for example, smart pricing looks at every distribution
channel’s relative value and assesses how much each channel can drive guest room
demand and help to achieve the overarching goal.
The next generation, AI-powered revenue management, is the next exciting opportunity in tourism industry. AI-powered revenue management is also about smart
pricing. The aim is to use market estimates, cost sensitivities, and competition rates,
demand drivers like special event activities, seasonality, and day of week variations
to optimize room occupancy at best possible price.
It is also important to develop a marketing plan that ramps up with travel demand.
Strategizing a month-to-month marketing plan that progressively builds up with the
expected rise in travel demand with the relaxation of entry and exit policy over
time will fuel the booking funnel and maximize revenue. In the case of hotel that is
currently closed, the plan should start one month prior to hotel reopening and continue
to shift from upper-funnel to lower-funnel targeting month to month. Considerations
for marketing plans are shown as below:
· Month 1: Focus on the target of the upper channel, mainly the local feeder market,
60–90 days of booking period and market to interested audiences, of which 80% of
the budget is allocated to the upper channel plan. Exclude demographic information for individuals who are unlikely to travel. Messaging should be time-sensitive
and focus on increasing the interest in hotel openings and the attractiveness of
destination content.
· Month 2: Turn to medium to low channel positioning, start to target viable fly-in
markets, and implement website remarketing as the pool grows. The budget allocation should be roughly 60% of the upper channel and 40% of the lower channel.
Message delivery should focus on the hotel’s unique selling point and special
offers and packages.
· Month 3: As travel demand continues to grow, hotels should continue to improve
the goals for travel intent and begin to increase auxiliary income opportunities
through scheduled spas and meals. The budget allocation should be approximately
30% of the upper channel and 70% of the lower channel. Message delivery should
focus on ways to increase stay time, increase sales of ancillary products, and
maximize average booking value.
· Month 4 and onwards: Prioritize lower channel plans and focus on locating potential guests based on real-time travel intent to the destination. Budget allocation
should account for approximately 20% of the upper-level channels and 80% of
the lower-level channels. Hotel operator should continue to adjust the respective
strategy to reflect changes in travel needs. As travel demand begins to stabilize,
messaging should focus on meeting key business needs and typical seasonality.
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Conclusion
Through the visualization and analysis of the impact of COVID on tourism industry,
we have a deeper and more thorough understanding of this pandemic and what
difference it has made in terms of macro factors including the number of arrival
visitors, length of stay, age group distribution and length of stay distribution, as well
as micro factors like revenue and occupancy rate in different room types.
Suggestions were proposed for the tourism industry, including the deployment
of new technologies like dynamic pricing system, more efficient boarding policies,
adjustment of the target customer, and better marketing plans.
The outbreak of COVID pandemic is a sudden and damaging catastrophe to all
industries, especially so for tourism with profound and long-lasting effects. Prompted
changes have to take place during this pandemic and new strategies should be made
to survive and re-thrive.
References
1. Calderwood LU, Soshkin M (2019) The travel and tourism competitiveness report 2019, World
Economic Forum 2019
2. Channel News Asia The big read: A vital economic pillar, S’pore’s tourism sector faces a brutal
test of mettle amid COVID-19 fallout. https://www.channelnewsasia.com/singapore/big-readsingapore-tourism-sector-covid-19-fallout-977476
3. OECD (2020) Mitigating the impact of COVID-19 on tourism and supporting recovery. https://
doi.org/10.1787/47045bae-en