Key regions: China, Germany, Thailand, Saudi Arabia, India
The E-Scooter-sharing market in MENA is experiencing significant growth and development, driven by changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors.
Customer preferences: Customers in the MENA region are increasingly looking for convenient and sustainable transportation options. E-Scooter-sharing provides a flexible and eco-friendly solution, allowing users to easily navigate through congested urban areas and reach their destinations quickly. The convenience of booking and unlocking e-scooters through mobile applications is also appealing to tech-savvy customers in the region.
Trends in the market: One of the key trends in the E-Scooter-sharing market in MENA is the expansion of service providers. Both local startups and international companies are entering the market, leading to increased competition and a wider range of options for customers. This trend is driving innovation and improvement in service quality, as companies strive to differentiate themselves and attract more users. Another trend in the market is the integration of E-Scooter-sharing with other transportation modes. Many cities in the region are implementing multimodal transportation systems, where e-scooters are seen as a last-mile solution that complements existing public transportation networks. This integration allows users to seamlessly switch between different modes of transport, enhancing the overall efficiency and convenience of their journeys.
Local special circumstances: The MENA region has unique characteristics that contribute to the growth of the E-Scooter-sharing market. One of these is the high population density in urban areas, which creates a strong demand for alternative transportation options. Additionally, the region's warm climate makes e-scooters a popular choice for short-distance travel, as they provide a comfortable and efficient mode of transportation in hot weather conditions. Furthermore, the presence of a young and tech-savvy population in the region is driving the adoption of e-scooter-sharing services. The popularity of smartphones and mobile applications has made it easier for users to access and use e-scooters, further fueling the growth of the market.
Underlying macroeconomic factors: The E-Scooter-sharing market in MENA is also influenced by underlying macroeconomic factors. Economic growth and urbanization in the region have led to increased demand for transportation services, creating opportunities for e-scooter-sharing companies to thrive. Additionally, government initiatives to promote sustainable transportation and reduce traffic congestion are supporting the growth of the market. In conclusion, the E-Scooter-sharing market in MENA is experiencing significant growth and development due to changing customer preferences, emerging trends in the market, local special circumstances, and underlying macroeconomic factors. As the market continues to evolve, it is expected to play a vital role in the region's transportation landscape, providing a convenient and sustainable solution for urban mobility.
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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