Quick Commerce - ASEAN

ASEAN

Highlights

The Quick Commerce market in ASEAN is anticipated to generate a revenue of US$4.80bn by 2024.
It is expected to exhibit a compound annual growth rate (CAGR 2024-2029) of 8.85%, leading to a projected market size of US$7.33bn by 2029.
The number of users in the Quick Commerce market is projected to reach 63.8m users by 2029.
The user penetration rate is estimated to be 6.8% in 2024 and is projected to increase to 8.9% by 2029.
The average revenue per user (ARPU) is expected to be US$102.00.
In terms of global comparison, China is forecasted to generate the highest revenue, amounting to US$80.84bn in 2024.
China also has the highest projected user penetration rate in the Quick Commerce market, estimated at 21.4%.
In the quick commerce market, Indonesia is experiencing a surge in demand for on-demand grocery delivery services.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Quick Commerce market in ASEAN has been rapidly growing in recent years, driven by the increasing demand for fast and convenient delivery services.

Customer preferences: Customers in ASEAN have shown a strong preference for on-demand delivery services, which has led to the rise of Quick Commerce platforms. These platforms offer a wide range of products that can be delivered within a few hours of ordering, making them a popular choice for busy consumers who value convenience and speed. In addition, customers in ASEAN are becoming increasingly tech-savvy, with a growing number of people using smartphones and other mobile devices to shop online.

Trends in the market: The Quick Commerce market in ASEAN is highly competitive, with a large number of players vying for market share. One trend that has emerged in recent years is the consolidation of the market, with larger players acquiring smaller ones to expand their reach and capabilities. Another trend is the focus on sustainability, with many Quick Commerce platforms introducing eco-friendly packaging and delivery options to appeal to environmentally conscious consumers. Finally, there has been a growing trend towards hyperlocalisation, with Quick Commerce platforms partnering with local businesses to offer a wider range of products and services to customers.

Local special circumstances: Each country in ASEAN has its own unique set of circumstances that have influenced the development of the Quick Commerce market. For example, in Indonesia, the market is dominated by Gojek and Grab, two home-grown companies that have leveraged their extensive networks of drivers to offer fast and affordable delivery services. In Singapore, the market is more fragmented, with a number of smaller players competing with the larger platforms. Meanwhile, in Vietnam, the market is still in its early stages, but is expected to grow rapidly in the coming years as more consumers adopt online shopping.

Underlying macroeconomic factors: The growth of the Quick Commerce market in ASEAN can be attributed to a number of underlying macroeconomic factors, including the region's large and growing population, rising incomes, and increasing urbanisation. In addition, the COVID-19 pandemic has accelerated the shift towards online shopping and home delivery, further boosting demand for Quick Commerce services. However, challenges remain, including the need to address logistical and regulatory issues, as well as the need to ensure that delivery services are accessible and affordable for all consumers, regardless of their location or income level.

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.