Key regions: United States, Germany, South America, Indonesia, Malaysia
The Car-sharing market in Kazakhstan has been experiencing significant growth in recent years, driven by changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors. Customer preferences in Kazakhstan have been shifting towards more sustainable and cost-effective transportation options, leading to an increased demand for car-sharing services. With rising concerns about environmental issues and the need for more efficient use of resources, customers are increasingly opting for car-sharing as a convenient and eco-friendly alternative to owning a private vehicle. Additionally, the younger generation, who value flexibility and convenience, are more inclined towards car-sharing services, as they provide them with the freedom to access a vehicle whenever needed without the burden of ownership. Trends in the car-sharing market in Kazakhstan reflect the global shift towards shared mobility solutions. The rise of digital platforms and smartphone applications has made it easier for customers to access and book car-sharing services, further fueling the growth of the market. Car-sharing companies have also been expanding their fleets and service areas to cater to the increasing demand, providing customers with a wider range of vehicle options and convenient pick-up and drop-off locations. Local special circumstances in Kazakhstan have also contributed to the development of the car-sharing market. The country's large urban population, particularly in major cities like Almaty and Nur-Sultan, has created a high demand for transportation services. The limited availability of parking spaces and the increasing cost of car ownership have made car-sharing a more attractive option for many residents. Furthermore, the government's efforts to promote sustainable transportation and reduce traffic congestion have created a supportive environment for the growth of the car-sharing market. Underlying macroeconomic factors have also played a role in the development of the car-sharing market in Kazakhstan. The country's growing middle class and increasing disposable incomes have made car-sharing more affordable and accessible to a larger segment of the population. Additionally, the government's investments in infrastructure development and the improvement of transportation systems have made car-sharing services more efficient and reliable, further driving the market growth. In conclusion, the car-sharing market in Kazakhstan is experiencing significant growth due to changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors. The shift towards more sustainable and cost-effective transportation options, the rise of digital platforms, the country's urban population, government support for sustainable transportation, and the growing middle class and disposable incomes are all contributing to the development of the car-sharing market in Kazakhstan.
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of car-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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