Matchmaking - Eastern Europe

Eastern Europe

Highlights

Revenue in the Matchmaking market is projected to reach US$42.01m in 2024.
Revenue is expected to show an annual growth rate (CAGR 2024-2029) of 2.56%, resulting in a projected market volume of US$47.66m by 2029.
In the Matchmaking market, the number of users is expected to amount to 1.5m users by 2029.
User penetration will be 0.6% in 2024 and is expected to hit 0.7% by 2029.
The average revenue per user (ARPU) is expected to amount to US$31.93.
In global comparison, most revenue will be generated China (US$1.21bn in 2024).
With a projected rate of 3.8%, the user penetration in the Matchmaking market is highest South Korea.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

The Casual Dating market in Eastern Europe has been experiencing significant growth in recent years. Customer preferences in the region have shifted towards more casual and non-committal relationships. This can be attributed to various factors such as changing societal norms, increased urbanization, and the influence of Western culture. Eastern European consumers are increasingly seeking out casual dating platforms and apps that cater to their specific needs and preferences. Trends in the market indicate a growing demand for online platforms and apps that facilitate casual dating. These platforms provide individuals with a convenient and discreet way to meet and connect with potential partners. The ease of use and accessibility of these platforms have contributed to their popularity in Eastern Europe. Additionally, the use of mobile devices has further fueled the growth of the casual dating market, as individuals can now connect with others anytime and anywhere. Local special circumstances in Eastern Europe also play a role in the development of the casual dating market. The region has a diverse cultural landscape, with different countries having their own unique dating customs and traditions. This diversity creates opportunities for niche casual dating platforms that cater to specific cultural preferences. For example, some platforms may focus on connecting individuals from similar ethnic backgrounds or religious beliefs. Underlying macroeconomic factors also contribute to the growth of the casual dating market in Eastern Europe. Economic development and increasing disposable incomes have led to a rise in leisure activities and spending on entertainment. As a result, individuals are more willing to invest in casual dating experiences and services. Additionally, the growth of the gig economy and flexible work arrangements have provided individuals with more free time and flexibility to engage in casual dating. Overall, the Casual Dating market in Eastern Europe is experiencing growth due to changing customer preferences, the rise of online platforms and apps, local special circumstances, and underlying macroeconomic factors. As the market continues to evolve, it is expected that new trends and developments will emerge to meet the evolving needs and preferences of Eastern European consumers.

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 segment. 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.