Ride-hailing - Turkmenistan

  • Turkmenistan
  • It is anticipated that Turkmenistan's Ride-hailing market will generate a revenue of €12.67m by 2024.
  • Moreover, the market is expected to grow at an annual growth rate (CAGR 2024-2029) of 10.05%, resulting in a projected market volume of €20.45m by 2029.
  • The number of users in this market is projected to reach 1.69m users by 2029.
  • The user penetration rate is expected to be 18.1% in 2024 and 24.2% by 2029.
  • The average revenue per user (ARPU) is expected to be €10.60.
  • Furthermore, it is projected that 100% of the total revenue in the Ride-hailing market will be generated through online sales by 2029.
  • It is worth noting that in terms of global comparison, China is expected to generate the highest revenue of €54,660m in 2024.
  • Despite the growing popularity of ride-hailing services worldwide, the market remains underdeveloped in Turkmenistan due to regulatory challenges and lack of infrastructure.

Key regions: South America, Europe, China, Saudi Arabia, Malaysia

 
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Analyst Opinion

The Ride-hailing market in Turkmenistan has been experiencing significant growth in recent years.

Customer preferences:
One of the key reasons for the growth of the Ride-hailing market in Turkmenistan is the changing customer preferences. With the convenience of booking a ride through a mobile app, customers now prefer to use ride-hailing services instead of traditional taxi services. The ability to track the driver's location in real-time and the option to pay electronically have made ride-hailing services a popular choice among customers.

Trends in the market:
The Ride-hailing market in Turkmenistan has witnessed several trends in recent years. Firstly, there has been an increase in the number of ride-hailing companies operating in the country. This has created competition in the market, leading to improved services and lower prices for customers. Secondly, ride-hailing companies have expanded their services beyond major cities to cater to customers in suburban and rural areas. This has further contributed to the growth of the market.

Local special circumstances:
Turkmenistan has a relatively small population and a limited public transportation system. This has created a demand for alternative transportation options, such as ride-hailing services. Additionally, the country has a high internet penetration rate, with a large number of people using smartphones. This has made it easier for ride-hailing companies to reach potential customers and provide them with convenient transportation solutions.

Underlying macroeconomic factors:
The growth of the Ride-hailing market in Turkmenistan can also be attributed to underlying macroeconomic factors. The country has experienced steady economic growth in recent years, leading to an increase in disposable income among the population. This has allowed more people to afford ride-hailing services. Furthermore, the government has implemented favorable regulations for the ride-hailing industry, creating a conducive environment for its growth. In conclusion, the Ride-hailing market in Turkmenistan has been growing due to changing customer preferences, the emergence of new trends, local special circumstances, and underlying macroeconomic factors. As the market continues to evolve, it is expected to play a significant role in the transportation sector of the country.

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings and revenues of ride-hailing services.

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, third-party studies and reports, federal statistical offices, industry associations, and price data. 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 demographic data, GDP, consumer spending, internet penetration, and device usage. 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, the S-curve function and exponential trend smoothing methods are applied.

Additional notes:

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

Visión general

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