Quick Commerce - Romania

Romania

Highlights

The Quick Commerce market in Romania is projected to reach a revenue of US$43.75m by 2024.
This is expected to show an annual growth rate (CAGR 2024-2029) of 6.25%, resulting in a projected market volume of US$59.24m by 2029.
In Romania's Quick Commerce market, the number of users is expected to reach 1.3m users by 2029.
The user penetration rate is projected to be 4.9% in 2024 and is expected to increase to 6.8% by 2029.
The average revenue per user (ARPU) is expected to be US$45.45.
In global comparison, China is projected to generate the highest revenue in the Quick Commerce market, reaching US$80.84bn in 2024.
China also has the highest user penetration rate in the Quick Commerce market, projected to be 21.4%.
Romania's quick commerce market is rapidly expanding, with a growing number of online platforms offering convenient and fast delivery services to meet the increasing demand of time-conscious consumers.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Quick Commerce, or Q-commerce, is a rapidly growing sector in the retail industry. In Romania, this market is no exception to the trend. With the rise of e-commerce, consumers are looking for faster and more convenient ways to shop, and Q-commerce provides just that.

Customer preferences: Romanian consumers are increasingly looking for convenience and speed in their shopping experiences. With the rise of urbanization and busy lifestyles, consumers are seeking out ways to save time. Q-commerce offers same-day or even one-hour delivery, making it an attractive option for those who don't have time to go to physical stores. Additionally, with the COVID-19 pandemic, consumers are turning to online shopping even more, making Q-commerce an even more popular option.

Trends in the market: The Q-commerce market in Romania has seen significant growth in recent years, with both local and international companies entering the market. One trend that has emerged is the rise of dark stores, which are small warehouses designed specifically for fulfilling online orders. This allows companies to offer faster delivery times and a wider range of products. Another trend is the use of technology, such as mobile apps and chatbots, to make ordering and delivery more efficient and convenient for consumers.

Local special circumstances: Romania has a growing middle class, which is driving the demand for Q-commerce. Additionally, the country has a high percentage of young people, who are more likely to shop online and appreciate the convenience of Q-commerce. However, there are also challenges to the market, such as a lack of infrastructure in some areas and a preference for cash payments over digital payments.

Underlying macroeconomic factors: Romania has a growing economy, with a GDP growth rate that outpaces the EU average. This provides a favorable environment for Q-commerce companies to invest and expand. Additionally, the country has a relatively low unemployment rate, which means consumers have more disposable income to spend on online shopping. However, there are also challenges, such as high inflation and a relatively low level of digitalization compared to other EU countries.

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.