Retail Delivery - China

China

Highlights

The Reail Delivery market in China is expected to reach a revenue of US$185.20bn by 2024.
This projection indicates an annual growth rate (CAGR 2024-2029) of 12.10%, resulting in a projected market volume of US$327.90bn by 2029.
Additionally, the number of users in the Reail Delivery market is projected to amount to 553.8m users by 2029.
The user penetration rate is expected to increase from 27.4% in 2024 to 38.9% by 2029.
Furthermore, the average revenue per user (ARPU) is anticipated to be US$472.70.
In terms of global comparison, United States is expected to generate the highest revenue in the Reail Delivery market, with US$185.20bn in 2024.
On the other hand, United States is projected to have the highest user penetration rate, reaching 30.4%.
China's retail delivery market is experiencing a surge in demand due to the rise of e-commerce giants like Alibaba and JD.com.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Retail Delivery market in China has been rapidly developing in recent years, driven by a variety of factors unique to the region.

Customer preferences: Chinese consumers have a strong preference for online shopping, with a significant portion of retail sales now taking place online. This has led to a surge in demand for fast and reliable delivery services, as customers expect to receive their purchases quickly and efficiently. Additionally, there is a growing demand for same-day and even one-hour delivery options, particularly in urban areas.

Trends in the market: One major trend in the Retail Delivery market in China is the increasing use of technology, such as artificial intelligence and robotics, to improve efficiency and reduce costs. Delivery companies are also expanding their services to include not just last-mile delivery, but also warehousing and inventory management. Another trend is the emergence of new delivery models, such as crowd-sourced delivery and self-service lockers, which are gaining popularity among consumers.

Local special circumstances: The sheer size of China's population and geography presents unique challenges for the Retail Delivery market. With a population of over 1.4 billion people spread across a vast territory, delivery companies must contend with a wide range of logistical challenges, from traffic congestion to rural areas with limited infrastructure. Additionally, the highly competitive nature of the market means that companies must constantly innovate and adapt in order to stay ahead of the competition.

Underlying macroeconomic factors: Several macroeconomic factors are driving the growth of the Retail Delivery market in China. The country's rapidly expanding middle class has more disposable income than ever before, and is increasingly turning to e-commerce for their shopping needs. Additionally, the Chinese government has been investing heavily in infrastructure and technology, which has helped to improve the overall efficiency of the delivery market. Finally, the COVID-19 pandemic has accelerated the shift towards online shopping and home delivery, further boosting demand for Retail Delivery services in China.

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.