Quick Commerce - Lithuania

Lithuania

Highlights

The Quick Commerce market in Lithuania is projected to reach a revenue of US$14.38m in 2024.
It is expected to show an annual growth rate (CAGR 2024-2029) of 6.79%, resulting in a projected market volume of US$19.97m by 2029.
By 2029, the number of users in the Quick Commerce market is expected to amount to 258.2k users.
The user penetration rate, which is currently at 7.0% in 2024, is expected to increase to 10.0% by 2029.
The average revenue per user (ARPU) is projected to be US$75.87.
In 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 at 21.4%.
The Quick Commerce market in Lithuania is thriving, with local delivery platforms offering a wide range of products and services for fast and convenient delivery to customers.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Quick Commerce, also known as q-commerce, is a rapidly growing sector in Lithuania. With the rise of e-commerce and the increasing demand for fast delivery, q-commerce has become an attractive option for consumers who want their goods delivered quickly and conveniently.

Customer preferences: Lithuanian consumers are increasingly turning to q-commerce for their shopping needs. The convenience of being able to order products online and have them delivered quickly is a major draw for consumers. Additionally, the COVID-19 pandemic has accelerated the shift towards e-commerce and q-commerce as more people are staying at home and avoiding physical stores.

Trends in the market: One trend in the Lithuanian q-commerce market is the rise of local players. While international companies such as Glovo and Bolt Food have a presence in Lithuania, local companies such as Pigu and Barbora are also gaining market share. These companies have a better understanding of the local market and can tailor their services to meet the needs of Lithuanian consumers. Another trend is the expansion of q-commerce beyond food delivery. While food delivery remains the dominant segment, q-commerce is also being used for the delivery of other products such as groceries, household items, and even prescription medication.

Local special circumstances: Lithuania has a relatively small population of just under 3 million people. This means that q-commerce companies need to be strategic in their operations and marketing to reach a large enough customer base. Additionally, the country's geography presents challenges for delivery, as some areas may be more difficult to access than others.

Underlying macroeconomic factors: Lithuania has a strong and growing economy, with a GDP growth rate of 3.9% in 2019. This growth has led to increased consumer spending and a greater demand for convenience. Additionally, Lithuania has a high internet penetration rate of over 70%, which has made e-commerce and q-commerce more accessible to consumers. The government has also taken steps to support the growth of the digital economy, including investing in infrastructure and promoting innovation.

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.