Bike-sharing - APAC
APAC- Revenue in the Bike-sharing market is projected to reach US$****bn in ****.
- Revenue is expected to show an annual growth rate (CAGR *********) of ****%, resulting in a projected market volume of US$*****bn by ****.
- In the Bike-sharing market, the number of users is expected to amount to ****bn users by ****.
- User penetration is projected to be ****% in **** and ****% by ****.
- The average revenue per user (ARPU) is expected to amount to US$****.
- In the Bike-sharing market, **% of total revenue will be generated through online sales by ****.
- In global comparison, most revenue will be generated China (US$*bn in ****).
Definition:
The Bike-sharing market includes short-term bike-sharing services. In bike-sharing services, bicycles are generally owned by a bike-sharing provider and are independently reserved by customers around the clock. Customers are required to open an account with the bike-sharing provider and can then reserve bicycles. This is usually done with a smartphone app, but there are also service providers that allow reservations to be made via the provider's website, by telephone, or at a terminal.
The two most frequently used bike-sharing varieties are the following: station-based (e.g., Stadtrad and Citi Bike New York) and free-floating (such as nextbike and ofo). With station-based bike-sharing, a bicycle is retrieved from a bike-sharing station and returned to either the same station or dropped off at another station. With free-floating bike-sharing, it is possible to find bicycles everywhere within the service provider's business zone and leave the bicycle anywhere in accordance with traffic regulations. Peer-to-peer bike-sharing is not included in the market definition of this market. Bike-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 Bike-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.
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- Free-floating bike-sharing reservations
- Stationary bike-sharing reservations
- Services such as nextbik, ofo, Mobike, and LimeBike
- Peer-to-peer bike-sharing reservations
- Free bike-sharing services, such as Aarhus City Bikes
- Multi-day bike rental offers
- Discounts for customers with long-term subscriptions and other types of discounts
- Electric scooter service providers
Revenue
Sales Channels
Analyst Opinion
Users
Global Comparison
Methodology
Data coverage:
The data encompasses B2C enterprises. Figures are based on bookings, revenues, and online shares of bike-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.Mobility
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