Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in ASEAN is experiencing rapid growth and development.
Customer preferences: Customers in ASEAN countries are increasingly turning to e-scooter sharing services due to their convenience, affordability, and eco-friendly nature. E-scooters provide a flexible and efficient mode of transportation, particularly for short distances or in congested urban areas. Additionally, the younger generation is embracing the trend of using e-scooters as a fun and trendy alternative to traditional modes of transportation.
Trends in the market: One major trend in the ASEAN e-scooter-sharing market is the expansion of service providers. Numerous local and international companies are entering the market, resulting in increased competition. This competition is driving innovation and improvements in service quality, such as the introduction of user-friendly mobile applications and the integration of GPS technology for easy locating and unlocking of e-scooters. Another trend is the integration of e-scooter-sharing services with existing transportation networks. Many cities in ASEAN are implementing smart city initiatives, and e-scooter sharing is seen as a key component of these plans. By integrating e-scooter sharing with public transportation systems, cities are aiming to provide a seamless and sustainable transportation solution for their residents.
Local special circumstances: Each ASEAN country has its own unique circumstances that are influencing the development of the e-scooter-sharing market. For example, in Singapore, the government has implemented strict regulations and licensing requirements for e-scooter-sharing operators to ensure safety and minimize potential risks. On the other hand, in countries like Indonesia and Vietnam, where traffic congestion is a major issue, e-scooter sharing is seen as a viable solution to reduce traffic and improve mobility.
Underlying macroeconomic factors: The rapid growth of the e-scooter-sharing market in ASEAN can be attributed to several macroeconomic factors. Firstly, the region has experienced significant economic growth in recent years, leading to an increase in disposable income and urbanization. This has created a larger customer base for e-scooter-sharing services. Furthermore, the increasing awareness and concern for environmental sustainability in ASEAN countries have also played a role in the growth of the e-scooter-sharing market. E-scooters are seen as a greener alternative to traditional modes of transportation, aligning with the region's commitment to reducing carbon emissions and promoting sustainable development. In conclusion, the e-scooter-sharing market in ASEAN is experiencing rapid growth and development due to customer preferences for convenience and affordability, as well as the integration of e-scooter-sharing services with existing transportation networks. Local special circumstances and underlying macroeconomic factors also contribute to the growth of the market. As the market continues to evolve, it is expected that more innovations and improvements will be introduced to meet the changing needs and preferences of customers in ASEAN countries.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings and revenues of e-scooter-sharing 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.
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