Platform Delivery - Sudan

Sudan

Highlights

The Platform Delivery market in Sudan is projected to reach a revenue of US$6.75m by 2024.
It is expected to show an annual growth rate (CAGR 2024-2029) of 16.60%, resulting in a projected market volume of US$14.55m by 2029.
Additionally, the number of users in the Platform Delivery market in Sudan is expected to amount to 884.2k users by 2029.
The user penetration rate, which is currently at 1.0% in 2024, is projected to reach 1.6% by 2029.
The average revenue per user (ARPU) is expected to amount to US$13.38 in Sudan.
In global comparison, China is set to generate the highest revenue in the Platform Delivery market, with an estimated amount of US$166.80bn in 2024.
Furthermore, China also boasts the highest user penetration rate in the Platform Delivery market, projected at 52.8%.
"Sudan's platform delivery market is experiencing a surge in demand driven by the increasing popularity of online shopping and the need for contactless delivery services."

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Platform Delivery market in Sudan is experiencing significant growth due to various reasons.

Customer preferences: Sudanese customers are increasingly turning to online platforms for their daily needs, including food delivery, grocery shopping, and ride-hailing services. This shift in customer behavior is driven by the convenience and ease of use offered by these platforms. Customers can order from their favorite restaurants or stores with just a few clicks, and have their orders delivered directly to their doorstep. Additionally, customers appreciate the transparency and real-time tracking provided by these platforms, which allows them to monitor the status of their orders at all times.

Trends in the market: The Platform Delivery market in Sudan is witnessing a surge in competition, with local and international players entering the market. This competition has led to an increase in innovation, with platforms introducing new features and services to attract and retain customers. For example, some platforms have started offering subscription-based services that provide customers with discounts and other benefits. Others have introduced loyalty programs that reward customers for their repeat business.

Local special circumstances: Sudan is a country that has undergone significant political and economic changes in recent years. The country has experienced a shift towards a more open and market-oriented economy, which has created opportunities for businesses in various sectors. Additionally, Sudan has a large and growing population, which presents a significant market for Platform Delivery services.

Underlying macroeconomic factors: The growth of the Platform Delivery market in Sudan is also driven by underlying macroeconomic factors. The country has a young and tech-savvy population that is increasingly connected to the internet. Additionally, Sudan has a growing middle class that is willing to spend money on convenience and quality services. These factors create a favorable environment for the growth of the Platform Delivery market in Sudan.

Data coverage:

The data encompasses B2C enterprises. Figures are based on Gross Merchandise Value (GMV) and represent what consumers pay for these products and services. The user metrics show the number of customers who have made at least one online purchase within the past 12 months.

Modeling approach / Market size:

Market sizes are determined through a bottom-up approach, building on predefined factors for each market. As a basis for evaluating markets, we use annual financial reports of the market-leading companies, third-party studies and reports, as well as survey results from our primary research (e.g., the Statista Global Consumer Survey). In addition, we use relevant key market indicators and data from country-specific associations, such as GDP, GDP per capita, and internet connection speed. 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, the S-curve function and exponential trend smoothing. The main drivers are internet users, urban population, usage of key players, and attitudes toward online services.

Additional notes:

The market is updated twice a year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. GCS data is reweighted for representativeness.