Key regions: South America, Europe, China, Saudi Arabia, Malaysia
The Ride-hailing market in Kenya has experienced significant growth in recent years, driven by changing customer preferences, emerging trends, and local special circumstances.
Customer preferences: One of the main reasons for the development of the Ride-hailing market in Kenya is the shift in customer preferences towards convenience and affordability. With the rapid urbanization and increasing traffic congestion in major cities like Nairobi and Mombasa, customers are seeking alternative transportation options that can save them time and provide a hassle-free experience. Ride-hailing services offer a convenient and affordable solution, allowing customers to book a ride with just a few taps on their smartphones and avoid the challenges of finding parking or dealing with public transportation.
Trends in the market: The Ride-hailing market in Kenya has witnessed the emergence of several key trends. Firstly, there has been a rise in the adoption of cashless payment methods, with customers preferring to pay for their rides using mobile money platforms such as M-Pesa. This trend is driven by the high mobile penetration rate in the country and the convenience of digital payments. Another trend is the increasing popularity of motorcycle ride-hailing services, also known as "boda-boda" services. These services have gained traction in Kenya due to their ability to navigate through traffic more efficiently and provide a faster mode of transportation, especially for short distances. Boda-boda services have become particularly popular in urban areas where traffic congestion is a major issue.
Local special circumstances: The Ride-hailing market in Kenya has also been shaped by local special circumstances. One such circumstance is the lack of a well-developed public transportation infrastructure in many parts of the country. This has created a gap in the market that ride-hailing services have been able to fill, providing reliable transportation options to customers who would otherwise struggle to find convenient and affordable rides. Additionally, the high unemployment rate in Kenya has contributed to the growth of the Ride-hailing market. Many individuals have turned to ride-hailing services as a source of income, either by driving their own vehicles or partnering with existing ride-hailing platforms. This has created a win-win situation, where customers have access to affordable rides, and drivers have the opportunity to earn a living.
Underlying macroeconomic factors: Several underlying macroeconomic factors have also played a role in the development of the Ride-hailing market in Kenya. The country's growing middle class and increasing disposable income levels have made ride-hailing services more affordable and accessible to a larger population. Additionally, the government's efforts to improve internet connectivity and promote digital innovation have created an enabling environment for ride-hailing platforms to thrive. In conclusion, the Ride-hailing market in Kenya has experienced significant growth due to changing customer preferences, emerging trends, local special circumstances, and underlying macroeconomic factors. As the market continues to evolve, it is expected that ride-hailing services will play an increasingly important role in the transportation landscape of the country.
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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