Quick Commerce - China

China

Highlights

The projected revenue in the Quick Commerce market in China is expected to reach a staggering US$80.84bn by 2024.
This will result in an annual growth rate (CAGR 2024-2029) of 9.48%, leading to a projected market volume of US$127.10bn by 2029.
Furthermore, the number of users in the Quick Commerce market is expected to reach 447.6m users by 2029.
In terms of user penetration, China is anticipated to have a user penetration rate of 21.4% in 2024, which is expected to increase to 31.4% by 2029.
This indicates that China has the highest user penetration rate in the Quick Commerce market.
The average revenue per user (ARPU) is projected to amount to US$263.30.
It is worth mentioning that in global comparison, China is expected to generate the highest revenue in the Quick Commerce market, with a staggering US$80.84bn in 2024.
China's quick commerce market is experiencing rapid growth due to the country's large population, increasing urbanization, and tech-savvy consumers.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Quick Commerce market in China has seen significant growth in recent years, with a variety of factors contributing to its success.

Customer preferences: Chinese consumers have become increasingly reliant on online shopping and delivery services, with many opting for convenience and speed over traditional brick-and-mortar stores. Quick Commerce, which offers delivery times of under an hour, has become particularly popular among urban consumers who value efficiency and time-saving.

Trends in the market: One trend in the Quick Commerce market in China is the rise of new players entering the market, which has led to increased competition and innovation. Established players are also expanding their offerings to include more diverse products and services, such as fresh groceries and pharmaceuticals. Additionally, there has been a shift towards more sustainable and environmentally-friendly practices, with some Quick Commerce companies implementing green delivery options and reducing packaging waste.

Local special circumstances: China's unique market conditions have also contributed to the growth of Quick Commerce. The country's vast population and sprawling urban areas make traditional retail challenging, leading many consumers to turn to online shopping and delivery services. Additionally, the prevalence of mobile payment options in China has made it easier for consumers to make purchases through Quick Commerce platforms.

Underlying macroeconomic factors: China's overall economic growth and rising middle class have also played a role in the success of Quick Commerce. As consumers have more disposable income, they are increasingly willing to pay for convenience and time-saving services. Additionally, the COVID-19 pandemic has accelerated the shift towards online shopping and delivery, further boosting the Quick Commerce market in China.Overall, the Quick Commerce market in China is poised for continued growth and innovation as consumer preferences and market conditions continue to evolve.

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.