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Key regions: South America, Malaysia, India, Indonesia, Saudi Arabia
The Bike-sharing market in Mongolia has seen significant growth in recent years, driven by increasing customer preferences for eco-friendly transportation options, the rise of urbanization, and favorable government policies. Customer preferences for bike-sharing services have been a key driver of the market's growth in Mongolia. With growing concerns about climate change and air pollution, many customers are looking for sustainable transportation alternatives. Bike-sharing provides a convenient and environmentally friendly option for short-distance travel, making it an attractive choice for commuters and tourists alike. Additionally, the affordability and flexibility of bike-sharing services appeal to a wide range of customers, further contributing to the market's expansion. Trends in the Bike-sharing market in Mongolia reflect global and regional patterns. One major trend is the adoption of dockless bike-sharing systems, which allow users to pick up and drop off bikes anywhere within a designated service area. This flexibility has made bike-sharing more convenient for customers, as they are no longer limited to specific docking stations. Furthermore, the integration of mobile apps and GPS technology has improved the user experience, enabling customers to easily locate and unlock bikes using their smartphones. In Mongolia, there are also some local special circumstances that have contributed to the growth of the Bike-sharing market. The country's capital city, Ulaanbaatar, has experienced rapid urbanization in recent years, leading to increased traffic congestion and a growing demand for alternative transportation options. Bike-sharing services have emerged as a viable solution to these challenges, providing a convenient and efficient way for residents and tourists to navigate the city. Additionally, Mongolia's vast and scenic landscapes make it an attractive destination for outdoor enthusiasts, further driving the demand for bike-sharing services. Underlying macroeconomic factors have also played a role in the development of the Bike-sharing market in Mongolia. The country's stable economic growth and rising disposable incomes have enabled more people to afford bike-sharing services. Moreover, the government has implemented policies to promote sustainable transportation, including the development of cycling infrastructure and the introduction of bike-sharing programs. These initiatives have created a supportive environment for the Bike-sharing market to thrive. In conclusion, the Bike-sharing market in Mongolia has experienced significant growth due to customer preferences for eco-friendly transportation, the adoption of dockless systems, local special circumstances such as urbanization and scenic landscapes, and favorable government policies. As these trends continue to evolve, the Bike-sharing market in Mongolia is expected to further expand in the coming years.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of bike-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.Mon - Fri, 9am - 6pm (EST)
Mon - Fri, 9am - 5pm (SGT)
Mon - Fri, 10:00am - 6:00pm (JST)
Mon - Fri, 9:30am - 5pm (GMT)
Mon - Fri, 9am - 6pm (EST)