Market Insights Mobility Travel & Tourism

Package Holidays - Mongolia

Mongolia

It is estimated that the Package Holidays market in Mongolia will experience a steady growth in revenue, with projections indicating that it will reach US$41.18m by 2024.
Moreover, the market is expected to show a Compound Annual Growth Rate (CAGR) of 12.02% between 2024 and 2029, resulting in a projected market volume of US$72.65m by 2029.
In terms of users, the Package Holidays market is expected to have 166.40k users users by 2029.
The user penetration rate is projected to increase from 3.5% in 2024 to 4.5% by 2029.
The average revenue per user (ARPU) is estimated to be US$0.34k.
Additionally, it is expected that 61% of the total revenue generated by the Package Holidays market in Mongolia will come from online sales by 2029.
It is worth noting that, in a global comparison, China is expected to generate most of the revenue in this market, with a projected revenue of US$49,250m in 2024.
Despite being a landlocked country with limited tourism infrastructure, Mongolia offers an adventurous and unique package holiday experience for those seeking to explore its vast landscapes and nomadic culture.

Key regions: Singapore, India, Indonesia, Germany, Saudi Arabia

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Analyst Opinion

The Package Holidays market in Mongolia is experiencing a notable growth trajectory.

Customer preferences: Customers in Mongolia are increasingly seeking hassle-free and convenient travel experiences, which has led to a rise in demand for package holidays. The convenience of having flights, accommodation, and activities bundled together appeals to busy professionals and families looking to maximize their vacation time.

Trends in the market: One significant trend in the Mongolian Package Holidays market is the emergence of niche packages catering to specific interests such as adventure tourism, cultural experiences, and eco-friendly getaways. Tour operators are diversifying their offerings to cater to these specialized preferences, attracting a broader range of customers.

Local special circumstances: Mongolia's unique landscapes, including the Gobi Desert and vast steppes, make it a compelling destination for travelers seeking off-the-beaten-path experiences. The country's rich nomadic heritage and traditional festivals also contribute to the appeal of package holidays that offer insights into the local culture.

Underlying macroeconomic factors: The growing disposable income among the Mongolian population has made travel more accessible to a larger segment of society, leading to an increased demand for package holidays. Additionally, government initiatives to promote tourism and improve infrastructure have boosted the overall tourism sector, benefiting package holiday providers in the country.

Methodology

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.