Smart Home - Guyana

Guyana

The Smart Home market in Guyana is anticipated to witness a significant revenue growth, projected to reach €3,451.0k in 2024.
This growth rate is expected to continue at a compound annual growth rate (CAGR) of 13.12% between 2024 and 2028, resulting in a projected market volume of €5,651.0k by 2028.
In Guyana's Smart Home market, the number of active households is forecasted to reach [users_currentlayer_yearend] by 2028.
This indicates a steady increase in household penetration from 6.1% in 2024 to an estimated 10.3% by 2028.
Currently, the average revenue per installed Smart Home market in Guyana stands at €230.40.
This metric reflects the revenue generated from each installed Smart Home market system in the country.
When comparing the global market, it is worth noting that United States leads in revenue generation, with an impressive €35,470.0m in 2024.
This demonstrates the dominance of the United States in the Smart Home market on a global scale.
Despite being a developing country, Guyana is experiencing a growing trend of smart home adoption, driven by increased internet connectivity and a desire for modern convenience.

Key regions: United States, United Kingdom, Germany, Europe, Norway

Marché

Région

Comparaison de régions

Monnaie

Methodology

Data coverage:

The data encompasses B2C enterprises. Figures are based on the sales of smart home products, excluding taxes.

Modeling approach:

Market sizes are determined through a bottom-up approach, building on a specific rationale for each market segment. As a basis for evaluating markets, we use the Statista Global Consumer Survey, market data from independent databases and third-party sources, and Statista interviews with market experts. In addition, we use relevant key market indicators and data from country-specific associations, such as household internet penetration and consumer spending for households. 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 are well suited for forecasting digital products and services due to the non-linear growth of technology adoption. The main drivers are GDP/capita, level of digitization, and consumer attitudes toward smart home integration.

Additional notes:

The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. The market is updated twice a year in case market dynamics change.