Quick Commerce - EAEU

EAEU

Highlights

The Quick Commerce market in the EAEU is expected to reach a projected revenue of US$1.18bn by 2024.
This revenue is forecasted to grow at an annual rate of 7.43% from 2024 to 2029, resulting in a projected market volume of US$1.69bn by 2029.
By 2029, the number of users in the Quick Commerce market is expected to reach 13.1m users.
The user penetration rate, which currently stands at 5.1% in 2024, is projected to increase to 7.2% by 2029.
The average revenue per user (ARPU) in the Quick Commerce market is estimated to be US$128.00.
In terms of global comparison, China is anticipated to generate the highest revenue in the Quick Commerce market, with US$80.84bn in 2024.
Additionally, China is expected to have the highest user penetration rate in the Quick Commerce market, projected to be 21.4%.
EAEU's Quick Commerce market is experiencing a surge in demand due to the convenience and efficiency it offers to consumers in the region.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Quick Commerce market in EAEU has been growing rapidly in recent years, with several factors contributing to its development.

Customer preferences: Consumers in EAEU have shown a growing preference for convenience and speed when it comes to shopping. Quick Commerce platforms have been able to meet these demands by providing fast and efficient delivery of goods, often within just a few hours of ordering. Additionally, many consumers in the region are increasingly using mobile devices for shopping, making Quick Commerce platforms a natural fit for their needs.

Trends in the market: One trend in the Quick Commerce market in EAEU is the increasing number of players entering the market. Both domestic and international companies have been expanding their operations in the region, leading to greater competition and innovation. Another trend is the expansion of Quick Commerce platforms beyond major cities, as companies seek to tap into the growing demand for fast delivery in smaller towns and rural areas.

Local special circumstances: The Quick Commerce market in EAEU is unique in that it spans several countries with different cultures, languages, and regulatory environments. Companies operating in the region must navigate these differences in order to succeed. Additionally, infrastructure challenges, such as poor road conditions and limited logistics networks, can pose obstacles to efficient delivery.

Underlying macroeconomic factors: The growing Quick Commerce market in EAEU is also being driven by broader macroeconomic factors. For example, the region has a large and growing middle class that is increasingly interested in e-commerce and online shopping. Additionally, the COVID-19 pandemic has accelerated the shift towards online shopping and home delivery, further fueling the growth of the Quick Commerce market in EAEU. Finally, government support for small and medium-sized enterprises has helped to create a favorable environment for Quick Commerce startups to thrive.

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.