Quick Commerce - Spain

Spain

Highlights

The Quick Commerce market in Spain is estimated to achieve a revenue of US$762.70m by 2024.
This projection indicates an annual growth rate (CAGR 2024-2029) of 8.04%, leading to a market volume of US$1.12bn by 2029.
Furthermore, it is expected that the number of users in the Quick Commerce market will reach 7.6m users by 2029.
In terms of user penetration, in Spain is anticipated to have a penetration rate of 10.9% in 2024, which is predicted to rise to 16.0% by 2029.
Additionally, the average revenue per user (ARPU) in Spain is estimated to be US$147.10.
When comparing global figures, China is expected to generate the highest revenue in the Quick Commerce market, reaching US$80.84bn by 2024.
Moreover, China is projected to have the highest user penetration rate in the Quick Commerce market, with an expected rate of 21.4%.
Spain's quick commerce market is experiencing a surge in demand as consumers seek convenient and fast delivery options.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Spain, a country known for its rich culture, beautiful beaches, and delicious cuisine, is also experiencing a surge in the Quick Commerce market.

Customer preferences: Spanish consumers are increasingly turning to Quick Commerce for their daily needs. The convenience of having groceries and other necessities delivered to their doorstep in a matter of minutes is a major draw. Additionally, the COVID-19 pandemic has accelerated the adoption of Quick Commerce as consumers seek contactless delivery options.

Trends in the market: The Quick Commerce market in Spain is becoming increasingly competitive, with both local and international players vying for market share. One trend that is emerging is the expansion of Quick Commerce services beyond just groceries and household items. Many Quick Commerce companies are now offering a wider range of products, including electronics, clothing, and beauty products.Another trend is the use of technology to improve the delivery experience. Some Quick Commerce companies are experimenting with drones and robots to deliver packages, while others are using predictive analytics to optimize delivery routes and reduce wait times.

Local special circumstances: Spain's unique geography and urban landscape present both challenges and opportunities for the Quick Commerce market. The country's dense urban centers and narrow streets can make delivery difficult, but the prevalence of small, family-owned shops and markets presents an opportunity for Quick Commerce companies to partner with these businesses and offer their products for delivery.

Underlying macroeconomic factors: Spain's economy has been slowly recovering from the 2008 financial crisis, but the COVID-19 pandemic has dealt a significant blow to the country's economy. However, the Quick Commerce market has been one bright spot, with many companies experiencing significant growth in 2020. As the economy continues to recover, it is likely that the Quick Commerce market will continue to thrive as consumers prioritize convenience and contactless delivery options.

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.