Package Holidays - Tanzania

  • Tanzania
  • It is projected that the revenue in Tanzania's Package Holidays market will reach US$231.80m by 2024.
  • Furthermore, an annual growth rate of 5.30% is expected between 2024-2029.
  • As a result, the market volume is projected to reach US$300.10m by 2029.
  • In Tanzania's Package Holidays market, the number of users is anticipated to amount to 2.65m users by 2029.
  • The user penetration rate is projected to increase from 2.2% in 2024 to 3.3% by 2029.
  • It is also expected that the average revenue per user (ARPU) will be US$148.90.
  • By 2029, 64% of the total revenue in the Package Holidays market will be generated through online sales.
  • In comparison to the global market, China is expected to generate the most revenue with US$49,250m in 2024.
  • Tanzania's package holiday market is booming due to its stunning natural attractions like Mount Kilimanjaro, Serengeti National Park, and Zanzibar's beaches.

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

 
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Analyst Opinion

The Package Holidays market in Tanzania is experiencing significant growth and development, driven by various factors influencing consumer behavior and market dynamics.

Customer preferences:
Customers in Tanzania are increasingly seeking convenience and hassle-free travel experiences, which has led to a growing demand for package holidays. These all-inclusive trips appeal to busy individuals and families looking to simplify their vacation planning process and ensure a seamless travel experience.

Trends in the market:
In Tanzania, there is a noticeable trend towards experiential travel, with travelers showing a preference for unique and authentic experiences. Package holidays that offer cultural immersion, adventure activities, and opportunities to explore off-the-beaten-path destinations are gaining popularity among tourists looking for more than just a traditional beach holiday.

Local special circumstances:
Tanzania's diverse natural landscapes, including iconic destinations like Serengeti National Park and Mount Kilimanjaro, make it a prime location for package holidays focused on wildlife safaris and outdoor adventures. The country's rich cultural heritage and warm hospitality also contribute to the appeal of packaged trips that highlight local customs, traditions, and cuisine.

Underlying macroeconomic factors:
The growing middle class in Tanzania, coupled with increasing disposable incomes, has made travel more accessible to a larger segment of the population. This rise in purchasing power has fueled the demand for package holidays as consumers look for value-added offerings and experiences within a fixed budget. Additionally, improvements in infrastructure and transportation networks have made it easier for tour operators to design and promote package holidays to a wider audience across 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.

Overview

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