Key regions: India, Thailand, Malaysia, China, South America
The E-Scooter-sharing market in Kazakhstan has been experiencing significant growth in recent years. Customer preferences, trends in the market, local special circumstances, and underlying macroeconomic factors have all contributed to the development of this market.
Customer preferences: Kazakhstan's population has shown a growing interest in alternative modes of transportation, particularly in urban areas. E-Scooter-sharing provides a convenient and environmentally friendly option for short-distance travel, attracting a wide range of customers. Younger generations, in particular, have embraced this trend, valuing the flexibility and affordability that E-Scooter-sharing services offer.
Trends in the market: The E-Scooter-sharing market in Kazakhstan has witnessed the entry of several international and local players, leading to increased competition. This competition has driven innovation and improvements in the quality of service, such as the introduction of more durable and efficient scooters, enhanced mobile applications, and improved customer support. Additionally, partnerships with local businesses and municipalities have helped expand the reach of E-Scooter-sharing services, making them more accessible to a larger population.
Local special circumstances: Kazakhstan's urban areas, especially the major cities like Nur-Sultan and Almaty, face challenges related to traffic congestion and limited parking spaces. E-Scooter-sharing services offer a solution to these issues by providing a convenient mode of transportation that can navigate through congested streets and be easily parked. The compact size of e-scooters makes them ideal for navigating narrow streets and crowded areas, making them a popular choice for short-distance trips.
Underlying macroeconomic factors: Kazakhstan's growing economy and increasing urbanization have contributed to the development of the E-Scooter-sharing market. As more people move to urban areas for employment and education opportunities, the demand for efficient and cost-effective transportation options has risen. Additionally, the government's focus on sustainable development and reducing carbon emissions has created a favorable environment for the growth of E-Scooter-sharing services. In conclusion, the E-Scooter-sharing market in Kazakhstan is experiencing significant growth due to customer preferences for alternative transportation, market trends driving competition and innovation, local circumstances such as traffic congestion and limited parking spaces, and underlying macroeconomic factors such as urbanization and government policies promoting sustainable development. As the market continues to evolve, it is expected that further advancements and expansion will take place, providing commuters with more convenient and environmentally friendly transportation options.
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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