E-Scooter-sharing - Germany | Statista Market Forecast

The E-Scooter-sharing market in Germany is predicted to witness a substantial rise in revenue, with a projected amount of US$212.70m anticipated by 2024.
Furthermore, the market is expected to grow at an annual growth rate of 3.70% between 2024 and 2029, which will result in an estimated market volume of US$255.00m by 2029.
Moreover, the number of users in the E-Scooter-sharing market is expected to reach 12.79m users by 2029, with a projected user penetration of 13.8% in 2024 and 15.4% by 2029.
The average revenue per user (ARPU) is predicted to amount to US$18.51.
It is noteworthy that by 2029, 100% of the total revenue in the E-Scooter-sharing market in Germany will be generated through online sales.
Additionally, in a global comparison, the highest revenue is expected to be generated in United States, with a predicted revenue of US$730m by 2024.
Germany is a key player in the E-Scooter-sharing market, with multiple companies offering eco-friendly transportation options in cities across the country.

Definition:

The E-Scooter-sharing market comprises e-scooter-sharing services that provide short-term rentals of electric motorized scooters (stand-up scooters). In e-scooter-sharing, scooters are generally owned by an e-scooter-sharing provider and can be reserved independently by customers around the clock. Customers are required to open an account with the e-scooter-sharing provider and can then reserve the vehicles, typically with a smartphone app. Providers normally offer dockless services, so it is possible to find e-scooters everywhere within the provider’s business zone, e.g., on sidewalks, and to leave the scooters anywhere in accordance with traffic regulations. Moped-sharing services are not available in all countries; thus, only a limited number of countries and regions can be selected.

Additional Information:

The main performance indicators of the E-Scooter-sharing market are revenues, average revenue per user (ARPU), user numbers and user penetration rates. Additionally, online and offline sales channel shares display the distribution of online and offline bookings. The ARPU refers to the average revenue one user generates per year while the revenue represents the total booking volume. Revenues are generated through both online and offline sales channels and include exclusively B2C revenues and users for the mentioned market. User numbers show only those individuals who have made a reservation, independent of the number of travelers on the booking. Each user is only counted once per year.

The booking volume includes all booked rides made by users from the selected region, regardless of where the ride took place.

For further information on the data displayed, refer to the info button right next to each box.

Free-floating e-scooter-sharing services
Services such as Lime, Tier, Bird, and Movo

Private e-scooter usage
Multi-day e-scooter rentals
Peer-to-peer e-scooter reservations
Bike sharing and other forms of micro-mobility

The E-Scooter-sharing market in Germany has been experiencing significant growth in recent years.

Customer preferences: Customers in Germany are increasingly opting for E-Scooter-sharing services due to their convenience and eco-friendly nature. E-Scooters provide a quick and efficient mode of transportation for short distances, especially in urban areas with heavy traffic. Additionally, the younger population in Germany is more inclined towards using sustainable transportation options, making E-Scooter-sharing services an attractive choice.

Trends in the market: One of the key trends in the E-Scooter-sharing market in Germany is the emergence of new market players. Several international companies have entered the German market, leading to increased competition. This has resulted in improved service quality and lower prices for customers. Additionally, there has been a trend towards the integration of E-Scooter-sharing services with existing transportation networks, such as public transport systems. This integration allows customers to easily switch between different modes of transportation, enhancing the overall convenience of using E-Scooters.

Local special circumstances: Germany has a well-developed infrastructure and a strong focus on sustainability. This makes it an ideal market for E-Scooter-sharing services. The country has a high population density in urban areas, which increases the demand for alternative transportation options. Furthermore, the German government has implemented policies to promote sustainable mobility, including the expansion of bike lanes and the introduction of incentives for using electric vehicles. These factors create a favorable environment for the growth of the E-Scooter-sharing market in Germany.

Underlying macroeconomic factors: The growth of the E-Scooter-sharing market in Germany is also influenced by underlying macroeconomic factors. The country has a strong economy and high disposable income levels, which allows customers to afford the cost of using E-Scooter-sharing services. Additionally, the German government has introduced regulations to ensure the safety of E-Scooter users, which has increased consumer confidence in these services. Overall, the combination of customer preferences, market trends, local special circumstances, and macroeconomic factors has contributed to the rapid development of the E-Scooter-sharing market in Germany.

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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