Key regions: South America, Europe, China, Saudi Arabia, Malaysia
The Ride-hailing market in Estonia has been experiencing significant growth in recent years. Customer preferences for convenient and affordable transportation options have contributed to this trend. Additionally, local special circumstances and underlying macroeconomic factors have also played a role in shaping the development of the market.
Customer preferences: In Estonia, customers have shown a strong preference for ride-hailing services due to their convenience and ease of use. The ability to book a ride through a mobile app and track the arrival of the driver in real-time has made ride-hailing a popular choice among commuters. Furthermore, the competitive pricing offered by ride-hailing companies has made them an attractive alternative to traditional taxi services. Customers appreciate the transparency in pricing and the ability to estimate the cost of their ride before booking.
Trends in the market: One of the key trends in the ride-hailing market in Estonia is the increasing popularity of shared rides. Customers are opting to share rides with other passengers heading in the same direction in order to reduce costs and minimize environmental impact. This trend is driven by both the cost-saving benefits and the growing awareness of sustainability among customers. Another trend in the market is the expansion of ride-hailing services beyond major cities. Initially, ride-hailing services were concentrated in urban areas, but they have gradually expanded their operations to cover smaller towns and rural areas. This expansion has been driven by the demand for reliable transportation options in these areas, as well as the desire of ride-hailing companies to tap into new markets.
Local special circumstances: Estonia has a high smartphone penetration rate, which has facilitated the adoption of ride-hailing services. The widespread use of smartphones has made it easy for customers to access ride-hailing apps and book rides on the go. Additionally, the country has a well-developed digital infrastructure, which has supported the growth of the ride-hailing market.
Underlying macroeconomic factors: The strong economic growth in Estonia has contributed to the development of the ride-hailing market. As the economy has expanded, more people have entered the workforce and the demand for convenient transportation options has increased. Ride-hailing services have provided a flexible and affordable solution for commuters, especially in urban areas where parking and congestion are major challenges. In conclusion, the Ride-hailing market in Estonia has experienced significant growth due to customer preferences for convenient and affordable transportation options. The increasing popularity of shared rides and the expansion of services to smaller towns and rural areas are key trends in the market. The high smartphone penetration rate and well-developed digital infrastructure in Estonia have also played a role in shaping the market. Furthermore, the strong economic growth in the country has contributed to the increasing demand for ride-hailing services.
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.
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