The Smart Home market Smart Appliances includes connected versions of all kinds of household appliances. This includes large appliances (fridges, washing machines, ovens etc.) as well as small appliances (microwaves, coffee machines, vacuum and mowing robots etc.), provided they are connected to the internet. An indirect connection via a local network is also possible, as long as the remote access and control of the respective appliances via the connection is given.
Directly or indirectly internet-controllable household appliances
Large appliances such as fridges, washing machines, dish washers, ovens
Small appliances such as coffee machines, vacuum and mowing robots, microwaves
Any other smart home device (partially also referred to as “appliances”)
Any non-connected household appliances
B2B/C2C sales of any kind (e.g. to hotels or office buildings)
The segment Smart Appliances includes connected versions of all kinds of household appliances. In most cases, smart appliances are incremental innovations which add new features to existing products rather than creating totally new devices. The high amount of revenues are generated mainly by relatively high product prices. Many customers as a first step purchase lower-priced small appliances such as smart coffee machines or vacuum robots, whereas people who already own products from other segments are more likely to purchase large, higher-priced appliances like fridges. We expect devices to be adopted rather moderately in Europe and North America, but more quickly in Asia.
The data encompasses B2C enterprises. Figures are based on the sales of smart home products, excluding taxes.
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.
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.
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.