Ride-hailing - Southeast Asia

The Ride-hailing market in Southeast Asia has experienced significant growth and development in recent years. Customer preferences, trends in the market, local special circumstances, and underlying macroeconomic factors have all contributed to the expansion of this industry. Customer preferences in Southeast Asia have played a crucial role in the rise of the Ride-hailing market. The convenience and affordability of booking a ride through a mobile app have attracted a large customer base. Moreover, the increasing use of smartphones and internet connectivity in the region has made it easier for people to access these services. Customers also appreciate the transparency and safety features offered by ride-hailing platforms, which have helped to build trust and loyalty among users. Trends in the market have further fueled the growth of the Ride-hailing industry in Southeast Asia. One notable trend is the increasing popularity of motorbike taxis in countries like Indonesia, Vietnam, and Thailand. These two-wheeler options provide a faster and more cost-effective transportation solution, particularly in congested urban areas. Additionally, ride-hailing platforms have expanded their services beyond just transportation, offering options like food delivery and grocery shopping, catering to the evolving needs of customers. Local special circumstances have also influenced the development of the Ride-hailing market in Southeast Asia. For example, the lack of reliable public transportation systems in many cities has created a demand for alternative transportation options. Ride-hailing platforms have filled this gap by providing a convenient and reliable means of transportation for both locals and tourists. Additionally, the high population density in urban areas has made ride-hailing a lucrative business opportunity, as there is a constant demand for transportation services. Underlying macroeconomic factors have also contributed to the growth of the Ride-hailing market in Southeast Asia. The region has experienced rapid economic growth, leading to an increase in disposable income and a growing middle class. As a result, more people can afford to use ride-hailing services on a regular basis. Furthermore, the rise of the gig economy and flexible work arrangements has created a larger pool of potential drivers, enabling ride-hailing platforms to meet the growing demand for their services. In conclusion, the Ride-hailing market in Southeast Asia has flourished due to customer preferences, market trends, local special circumstances, and underlying macroeconomic factors. The convenience, affordability, and reliability offered by ride-hailing platforms have resonated with customers in the region, driving the demand for these services. As Southeast Asia continues to experience economic growth and urbanization, the Ride-hailing market is expected to further expand and evolve to meet the changing needs of customers.

Data coverage:

The data encompasses B2C enterprises. Figures are based on bookings and revenues of ride-hailing 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.