Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Thailand has experienced significant growth in recent years, driven by changing customer preferences, emerging trends in the market, and local special circumstances.
Customer preferences: Customers in Thailand are increasingly looking for convenient and sustainable transportation options, and e-scooter sharing provides an attractive solution. E-scooters are compact, easy to use, and can navigate through congested urban areas, making them a popular choice for short-distance travel. Additionally, the younger generation, who are more environmentally conscious, are embracing e-scooter sharing as a greener alternative to traditional modes of transportation.
Trends in the market: One of the key trends in the e-scooter-sharing market in Thailand is the integration of technology. E-scooter rental companies are leveraging mobile applications, GPS tracking, and digital payment systems to enhance user experience and streamline operations. This allows customers to easily locate and unlock e-scooters, track their usage, and make payments seamlessly. Furthermore, the integration of IoT (Internet of Things) technology enables companies to monitor the condition of their e-scooters in real-time, ensuring their safety and maintenance. Another trend in the market is the expansion of e-scooter-sharing services to tourist destinations. Thailand is a popular tourist destination, and e-scooter sharing provides tourists with a convenient and efficient way to explore the country's attractions. E-scooter rental companies are strategically placing their stations near tourist hotspots, hotels, and transportation hubs, making it easy for tourists to access and return the e-scooters.
Local special circumstances: Thailand's densely populated cities and traffic congestion issues have contributed to the growth of the e-scooter-sharing market. With limited parking spaces and increasing traffic, many people are opting for e-scooters as a practical and time-saving mode of transportation. Additionally, the government's efforts to promote sustainable transportation and reduce air pollution have created a favorable environment for the e-scooter-sharing industry to thrive.
Underlying macroeconomic factors: Thailand's growing middle class and increasing urbanization have played a significant role in the development of the e-scooter-sharing market. As more people move to cities and seek affordable and convenient transportation options, the demand for e-scooter sharing is expected to continue rising. Moreover, the government's investment in infrastructure development and the promotion of smart cities further support the growth of the e-scooter-sharing market in Thailand. In conclusion, the e-scooter-sharing market in Thailand is experiencing significant growth due to changing customer preferences, emerging market trends, local special circumstances, and underlying macroeconomic factors. As the demand for convenient and sustainable transportation options continues to rise, e-scooter sharing is expected to become even more popular in the country.
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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