Quick Commerce - Indonesia

Indonesia

Highlights

The Quick Commerce market in Indonesia is projected to see a substantial increase in revenue, reaching US$2.82bn in 2024.
This is expected to result in an annual growth rate of 7.38%, leading to a projected market volume of US$4.03bn by 2029.
Furthermore, the number of users in the Quick Commerce market is estimated to reach 26.3m users by 2029.
In terms of user penetration, it is anticipated to be 6.9% in 2024 and is projected to reach 9.1% by 2029.
The average revenue per user (ARPU) is expected to be US$145.80.
When comparing the Quick Commerce market globally, China is expected to generate the highest revenue, amounting to US$80.84bn in 2024.
Additionally, China also boasts the highest user penetration rate in the Quick Commerce market, projected to be 21.4%.
The Quick Commerce market in Indonesia is booming, with a wide range of tech-enabled delivery platforms catering to the country's growing demand for convenience and speed.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Indonesia, the world's fourth most populous country, has become a hub for Quick Commerce (Q-Commerce) services, which are rapidly gaining popularity.

Customer preferences: Indonesians are known for their love of convenience, which has led to the growth of Q-Commerce. Customers prefer Q-Commerce because it provides fast and reliable delivery of goods and services. Additionally, the availability of a wide variety of products at reasonable prices has made Q-Commerce a popular choice among Indonesian consumers.

Trends in the market: One of the key trends in the Indonesian Q-Commerce market is the increasing number of players in the industry. The competition is fierce, with both local and international players vying for market share. Another trend is the use of technology to improve the efficiency of Q-Commerce operations. For instance, some players are using artificial intelligence and machine learning to optimize their supply chains.

Local special circumstances: Indonesia's archipelago geography and poor infrastructure have posed significant challenges to the Q-Commerce industry. However, Q-Commerce players have adopted innovative solutions such as using motorcycles and bicycles to deliver goods in congested areas. Additionally, the high level of smartphone penetration in Indonesia has made it easier for Q-Commerce players to reach customers.

Underlying macroeconomic factors: Indonesia's rapidly growing middle class is a key factor driving the growth of Q-Commerce. The country's economic growth has led to an increase in disposable income, which has in turn led to an increase in consumer spending. Additionally, Indonesia's young population, with a median age of 30.5 years, is more tech-savvy and comfortable with online shopping, which has further fueled the growth of Q-Commerce. Finally, the COVID-19 pandemic has accelerated the adoption of Q-Commerce in Indonesia, as consumers have become more reliant on online shopping due to social distancing measures.

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.