Quick Commerce - Belgium

Belgium

Highlights

The Quick Commerce market in Belgium is projected to reach a revenue of US$99.64m by 2024.
This is expected to show an annual growth rate (CAGR 2024-2029) of 7.89%, resulting in a projected market volume of US$145.60m by 2029.
In Belgium's Quick Commerce market, the number of users is expected to amount to 949.9k users by 2029.
The user penetration is projected to be 5.6% in 2024 and is expected to increase to 8.0% by 2029.
The average revenue per user (ARPU) is projected to be US$151.70.
In global comparison, China is expected to generate the highest revenue in the Quick Commerce market, reaching US$80.84bn in 2024.
Additionally, China has the highest user penetration rate in the Quick Commerce market, projected to be 21.4%.
The Quick Commerce market in Belgium is rapidly expanding, with an increasing number of consumers opting for convenient, on-demand delivery of goods and services.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Quick Commerce market in Belgium has been steadily growing in recent years, driven by changing consumer preferences and advancements in technology.

Customer preferences: Belgian consumers are increasingly looking for convenience and speed in their shopping experiences, leading to a rise in demand for Quick Commerce services. With busy lifestyles, consumers are looking for ways to save time and effort, and Quick Commerce companies are meeting this demand by offering fast and efficient delivery options. Additionally, the COVID-19 pandemic has accelerated the shift towards online shopping, further boosting the demand for Quick Commerce services.

Trends in the market: One trend in the Quick Commerce market in Belgium is the rise of partnerships between Quick Commerce companies and traditional retailers. This allows Quick Commerce companies to expand their offerings and reach a wider customer base, while also providing traditional retailers with a way to compete with online-only retailers. Another trend is the increasing use of technology such as artificial intelligence and machine learning to optimize delivery routes and improve efficiency.

Local special circumstances: Belgium’s small size and high population density make it an ideal market for Quick Commerce companies. Additionally, the country’s well-developed transportation infrastructure and high internet penetration rate make it easy for Quick Commerce companies to reach customers across the country. However, the country’s complex regulatory environment can pose challenges for Quick Commerce companies, particularly in terms of labor laws.

Underlying macroeconomic factors: Belgium’s strong economy and high standard of living have contributed to the growth of the Quick Commerce market. Additionally, the country’s position as a major hub for international trade and logistics makes it an attractive market for Quick Commerce companies looking to expand their global reach. However, the country’s high taxes and strict labor laws can make it challenging for Quick Commerce companies to operate profitably.

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.