Key regions: South America, Europe, China, Saudi Arabia, Malaysia
Armenia, a small country in the South Caucasus region, has witnessed a significant growth in its ride-hailing market in recent years. This can be attributed to several factors including customer preferences, trends in the market, local special circumstances, and underlying macroeconomic factors. Customer preferences in Armenia have played a crucial role in the development of the ride-hailing market. With the increasing popularity of smartphones and the convenience they offer, customers have shown a preference for on-demand transportation services. The ease of booking a ride through a mobile app and the ability to track the driver's location in real-time have made ride-hailing a preferred mode of transportation for many Armenians. Additionally, the competitive pricing and availability of different vehicle options have also attracted customers to ride-hailing services. Trends in the market have also contributed to the growth of the ride-hailing industry in Armenia. The emergence of local ride-hailing companies, as well as the entry of international players, has created a competitive market environment. This competition has led to innovation and improvements in service quality, as companies strive to differentiate themselves and gain a larger market share. Furthermore, the introduction of additional services such as food delivery and package delivery has expanded the customer base and increased the overall demand for ride-hailing services. Local special circumstances have also played a role in the development of the ride-hailing market in Armenia. The country's geography, with its mountainous terrain and challenging road conditions, has made traditional taxi services less efficient and reliable. Ride-hailing services, with their larger fleet and better navigation systems, have been able to overcome these challenges and provide a more reliable transportation option for customers. Additionally, the high population density in urban areas has created a strong demand for convenient and affordable transportation solutions, further driving the growth of the ride-hailing market. Underlying macroeconomic factors have also contributed to the growth of the ride-hailing market in Armenia. The country has experienced steady economic growth in recent years, resulting in an increase in disposable income and a growing middle class. This has led to an increase in consumer spending on transportation services, including ride-hailing. Furthermore, the government's efforts to promote entrepreneurship and attract foreign investment have created a favorable business environment, encouraging the entry of ride-hailing companies and stimulating market growth. In conclusion, the ride-hailing market in Armenia has experienced significant growth due to customer preferences, trends in the market, local special circumstances, and underlying macroeconomic factors. As the market continues to evolve and adapt to changing customer needs, we can expect further expansion and innovation in the ride-hailing industry in Armenia.
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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