Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Indonesia has experienced significant growth in recent years, driven by changing customer preferences and favorable market trends.
Customer preferences: Indonesian consumers are increasingly looking for convenient and eco-friendly transportation options. E-Scooter-sharing services provide a flexible and cost-effective alternative to traditional modes of transportation such as cars or motorcycles. The ease of access and affordability of E-Scooter-sharing services make them particularly attractive to young urban dwellers who are looking for convenient ways to navigate congested city streets. Additionally, the popularity of E-Scooter-sharing services is also driven by the desire for a more sustainable and environmentally friendly mode of transportation.
Trends in the market: One of the key trends in the E-Scooter-sharing market in Indonesia is the rapid expansion of service providers. Several local and international companies have entered the market, leading to increased competition and innovation. This has resulted in improved service quality, as companies strive to differentiate themselves and attract more customers. Additionally, there is a growing trend towards the integration of E-Scooter-sharing services with other transportation options, such as ride-hailing platforms. This allows users to seamlessly switch between different modes of transportation, further enhancing the convenience and accessibility of E-Scooter-sharing services.
Local special circumstances: Indonesia is a country with a large population and a high rate of urbanization. This presents unique challenges and opportunities for the E-Scooter-sharing market. The dense urban areas, particularly in cities like Jakarta, provide a large customer base and high demand for alternative transportation options. However, the lack of proper infrastructure and regulations can pose challenges for E-Scooter-sharing service providers. Companies need to navigate complex regulatory frameworks and invest in infrastructure development to ensure the safety and reliability of their services. Additionally, the tropical climate in Indonesia can also affect the durability and performance of E-Scooters, requiring companies to adapt their offerings to local conditions.
Underlying macroeconomic factors: The growth of the E-Scooter-sharing market in Indonesia is also influenced by underlying macroeconomic factors. The country has experienced steady economic growth in recent years, leading to an increase in disposable income and a growing middle class. This has created a larger consumer base with the ability to afford and access E-Scooter-sharing services. Additionally, the government has shown support for sustainable transportation initiatives, which has further encouraged the development of the E-Scooter-sharing market. These macroeconomic factors provide a favorable environment for the growth and expansion of E-Scooter-sharing services in Indonesia.
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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