Package Holidays - Africa

  • Africa
  • According to the latest projections, the revenue for the Package Holidays market in Africa is expected to reach US$7.77bn by 2024.
  • The market is anticipated to grow at a Compound Annual Growth Rate (CAGR) of 6.93% between 2024 and 2029, resulting in a market volume of US$10.86bn by 2029.
  • The number of users in the Package Holidays market is expected to reach 47.70m users by 2029, with a user penetration of 2.6% in 2024, which is projected to reach 3.3% by 2029.
  • The Average Revenue Per User (ARPU) is expected to be US$227.10.
  • It is projected that 70% of the total revenue in the Package Holidays market will be generated through online sales by 2029.
  • In terms of global comparison, the highest revenue is expected to be generated China, with a projected revenue of US$49,250m in 2024.
  • Package holidays to South Africa are increasingly popular among adventure-seeking tourists, offering opportunities for safaris, beach vacations, and cultural experiences.

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

 
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Analyst Opinion

The Package Holidays market in Africa has been experiencing significant growth and development in recent years.

Customer preferences:
Customers in Africa are increasingly looking for convenient and hassle-free travel options, which has led to a rise in demand for package holidays. These all-inclusive deals offer a combination of accommodation, transportation, and activities, providing travelers with a seamless vacation experience.

Trends in the market:
In countries like South Africa, Egypt, and Morocco, the Package Holidays market is witnessing a surge in popularity. Tour operators are offering a diverse range of packages catering to different interests and budgets, from luxury beach resorts to cultural tours. This variety is attracting a wider customer base and driving growth in the market.

Local special circumstances:
One of the key factors driving the growth of the Package Holidays market in Africa is the continent's rich cultural and natural heritage. Countries like Kenya and Tanzania offer unique safari experiences, while Egypt boasts ancient historical sites. Tourists are drawn to these destinations for their exceptional offerings, creating a demand for package holidays that cover these iconic experiences.

Underlying macroeconomic factors:
The growing middle class in Africa, coupled with increasing disposable incomes, has contributed to the expansion of the Package Holidays market. As more people have the financial means to travel, there is a rising demand for convenient and affordable vacation options. Additionally, improvements in infrastructure and transportation networks across the continent have made it easier for tour operators to offer a wider range of package holidays, further fueling market growth.

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.

Overview

  • Revenue
  • Sales Channels
  • Analyst Opinion
  • Users
  • Global Comparison
  • Methodology
  • Key Market Indicators
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