Restaurant Delivery - Indonesia

Indonesia

Highlights

The Restaurant Delivery market in Indonesia is projected to reach a revenue of US$2.08bn in 2024.
It is expected to grow at an annual rate of 2.56%, resulting in a projected market volume of US$2.36bn by 2029.
By that year, the number of users in the Restaurant Delivery market is expected to reach 44.9m users.
The user penetration rate is projected to be 14.1% in 2024 and is expected to increase to 15.4% by 2029.
The average revenue per user (ARPU) is estimated to be US$52.78.
In terms of global comparison, United States is projected to generate the highest revenue in the Restaurant Delivery market, amounting to US$36.95bn in 2024.
On the other hand, South Korea is expected to have the highest user penetration rate in the Restaurant Delivery market, with a projected rate of 44.8%.
The restaurant delivery market in Indonesia is experiencing a surge in demand due to the growing urban population and the increasing popularity of online food ordering platforms.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Indonesia, the world's fourth most populous country, is home to a diverse and dynamic restaurant delivery market.

Customer preferences: Indonesian consumers are increasingly turning to online food delivery services, driven by the convenience and variety offered by these platforms. In particular, younger consumers are more likely to order food online, and are attracted to the ease of ordering and the ability to track their delivery in real-time. Additionally, Indonesian consumers are highly price-sensitive, and are drawn to platforms that offer competitive pricing and promotions.

Trends in the market: The Indonesian restaurant delivery market has seen significant growth in recent years, with a number of local and international players entering the market. These platforms are leveraging technology to improve the customer experience, with features such as real-time tracking, cashless payments, and personalized recommendations. In addition, there is a growing trend towards healthier and more sustainable food options, with many platforms partnering with local restaurants and suppliers to offer organic, locally-sourced, and plant-based dishes.

Local special circumstances: Indonesia's diverse culinary landscape presents a unique opportunity for restaurant delivery platforms, with a wide range of local and regional cuisines available. However, the country's vast geography and complex logistics infrastructure can present challenges for delivery providers, particularly in more remote areas. Additionally, Indonesia's regulatory environment can be complex and fragmented, with different regions and municipalities imposing their own rules and regulations on food delivery services.

Underlying macroeconomic factors: Indonesia's rapidly growing middle class, coupled with increasing urbanization and a tech-savvy population, has created a favorable environment for the restaurant delivery market. Additionally, the COVID-19 pandemic has accelerated the shift towards online food delivery, as consumers have been forced to stay at home and avoid crowded restaurants. However, ongoing economic uncertainty and the potential for further disruptions from the pandemic could impact consumer spending and demand for restaurant delivery services in the near term.

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.