Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in Africa has been experiencing significant growth in recent years, driven by customer preferences for convenient and eco-friendly transportation options.
Customer preferences: Customers in Africa are increasingly seeking alternative modes of transportation that are affordable, efficient, and environmentally friendly. E-Scooter-sharing services provide a convenient solution for short-distance travel, allowing users to easily navigate congested urban areas and avoid traffic. The popularity of E-Scooter-sharing is also driven by the growing trend of urbanization in African cities, where there is a need for efficient and sustainable transportation options.
Trends in the market: One of the key trends in the E-Scooter-sharing market in Africa is the rapid expansion of service providers. Both local and international companies are entering the market, offering their services in major cities across the continent. This increased competition is leading to innovations in technology, pricing models, and customer experience, as companies strive to differentiate themselves and attract a larger user base. Another trend in the market is the integration of E-Scooter-sharing services with existing transportation infrastructure. Many cities in Africa are implementing policies and infrastructure to support the use of E-Scooters, such as designated parking areas and charging stations. This integration with existing transportation systems makes E-Scooter-sharing a more viable and convenient option for users.
Local special circumstances: Africa has unique characteristics that influence the development of the E-Scooter-sharing market. One of these is the lack of reliable public transportation options in many cities. E-Scooter-sharing services fill this gap by providing a flexible and affordable mode of transportation for short trips. Additionally, the relatively young and tech-savvy population in Africa is more likely to embrace new technologies and adopt E-Scooter-sharing services.
Underlying macroeconomic factors: The growth of the E-Scooter-sharing market in Africa is also supported by favorable macroeconomic factors. The continent has been experiencing rapid economic growth in recent years, leading to an increase in disposable incomes and urbanization. This has created a larger middle class population with the means to afford E-Scooter-sharing services. Additionally, the push for sustainable development and the need to reduce carbon emissions are driving governments and organizations to invest in alternative transportation solutions like E-Scooter-sharing. In conclusion, the E-Scooter-sharing market in Africa is growing due to customer preferences for convenient and eco-friendly transportation options, the rapid expansion of service providers, integration with existing transportation infrastructure, local special circumstances such as the lack of reliable public transportation options, and favorable macroeconomic factors such as rapid economic growth and the push for sustainable development.
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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