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Quick Commerce - Germany

Germany

The projected revenue in the Quick Commerce market in Germany is expected to reach €0.94bn in 2024.
This revenue is anticipated to show an annual growth rate (CAGR 2024-2029) of 9.94%, resulting in a projected market volume of €1.51bn by 2029.
Moreover, the number of users in the Quick Commerce market in Germany is projected to amount to 5.0m users by 2029.
The user penetration in this market is expected to be 4.0% in 2024 and is projected to increase to 6.0% by 2029.
Additionally, the average revenue per user (ARPU) is estimated to be €280.00.
In global comparison, China is projected to generate the highest revenue in the Quick Commerce market, amounting to €74,960.00m in 2024.
Furthermore, China is expected to have the highest user penetration rate in the Quick Commerce market with a projected rate of 21.4%.
Germany's quick commerce market is rapidly expanding, with major players like Delivery Hero and Gorillas dominating the sector.

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Analyst Opinion

Quick Commerce, also known as q-commerce, is a rapidly growing trend in the retail industry that allows customers to order goods online and have them delivered within a short period of time. Germany is one of the countries that has seen significant growth in this market in recent years.

Customer preferences: One of the main reasons for the growth of the q-commerce market in Germany is the changing preferences of customers. With the increasing use of smartphones and the internet, customers are looking for more convenient and faster ways to shop. Q-commerce offers a solution to this by providing a fast and easy way to purchase goods online and have them delivered within a short period of time.

Trends in the market: One of the major trends in the q-commerce market in Germany is the increasing competition among companies. Many companies are now entering the market, offering a wide range of products and services to customers. This has led to a significant increase in the number of q-commerce platforms, which has made it easier for customers to find what they are looking for.Another trend in the market is the increasing use of technology. Companies are now using advanced technologies such as artificial intelligence and machine learning to improve their services and provide a better customer experience. This has led to a significant improvement in the speed and accuracy of deliveries, which has made q-commerce more attractive to customers.

Local special circumstances: Germany has a highly developed logistics infrastructure, which has made it easier for companies to offer fast and efficient delivery services. This has been a major factor in the growth of the q-commerce market in the country. Additionally, the country has a large population of tech-savvy consumers who are comfortable using technology to shop online.

Underlying macroeconomic factors: The q-commerce market in Germany is also influenced by macroeconomic factors such as the growth of the e-commerce industry and the increasing use of smartphones and the internet. As more people become comfortable with shopping online, the demand for q-commerce services is likely to continue to grow. Additionally, the increasing use of technology and the growth of the logistics industry are likely to drive further innovation and growth in the q-commerce market in Germany.

Methodology

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.