Package Holidays - ASEAN

ASEAN

Highlights

It is projected that by 2024, the revenue for the Package Holidays market will reach US$12.34bn in ASEAN.
This is expected to increase with a Compound Annual Growth Rate (CAGR) of 6.08%, resulting in a projected market volume of US$16.58bn by 2029.
Moreover, the number of users is expected to reach 57.75m users by 2029, with the user penetration rate expected to rise from 6.6% in 2024 to 8.0% by 2029.
The projected Average Revenue Per User (ARPU) is expected to be US$270.40.
In ASEAN, it is expected that 76% of the total revenue in the Package Holidays market will be generated through online sales by 2029.
It is noteworthy to mention that in comparison to other countries, China is expected to generate the most revenue in this market, with a projected revenue of US$49bn in 2024.
In ASEAN, package holidays to Thailand are increasingly popular due to its diverse attractions, affordable prices, and well-established tourism infrastructure.

Key regions:

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Package Holidays market in ASEAN is experiencing a significant growth trajectory with evolving customer preferences and unique local special circumstances driving the trends in the region.

Customer preferences: Customers in ASEAN countries are increasingly seeking convenience and hassle-free travel experiences, leading to a rising demand for package holidays. The all-inclusive nature of these packages, which often include accommodations, transportation, and activities, appeals to travelers looking for a seamless vacation experience without the need to plan every detail themselves.

Trends in the market: In Thailand, there is a growing trend towards eco-friendly and sustainable package holidays, reflecting the global shift towards responsible tourism. Travelers are showing a preference for packages that incorporate local cultural experiences and support community-based tourism initiatives, aligning with the country's focus on promoting sustainable travel practices.

Local special circumstances: Indonesia's diverse archipelago offers a unique opportunity for package holidays that cater to different interests and preferences. From beach getaways in Bali to cultural immersions in Yogyakarta, Indonesia's varied attractions allow tour operators to design specialized packages that appeal to a wide range of travelers. Additionally, the country's rich cultural heritage and warm hospitality contribute to the popularity of package holidays among both domestic and international tourists.

Underlying macroeconomic factors: The increasing disposable income levels in ASEAN countries are fueling the growth of the package holidays market, as more people have the financial means to invest in travel experiences. Additionally, the region's improving infrastructure and connectivity make it easier for travelers to explore different destinations within ASEAN, contributing to the overall expansion of the package holidays market in the region.

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings, revenues, and sales channels of package holidays.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market. As a basis for evaluating markets, we use financial reports, the Global Consumer Survey, third-party studies and reports, data from industry associations (e.g., UNWTO), and price data of major players in respective markets. To estimate the number of users and bookings, we furthermore use data from the Statista Consumer Insigths Global survey. In addition, we use relevant key market indicators and data from country-specific associations, such as country-related GDP, demographic data (e.g., population), tourism spending, consumer spending, internet penetration, and device penetration. This data helps us estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, ARIMA, which allows time series forecasts, accounting for stationarity of data and enabling short-term estimates. Additionally, simple linear regression, Holt-Winters forecast, and exponential trend smoothing methods are applied. A k-means cluster analysis allows for the estimation of similar countries. The main drivers are tourism GDP per capita and respective price indices.

Additional notes:

The data is modeled using current exchange rates. The market is updated twice a year in case market dynamics change.