1. Household Furnishing and Decoration
This dataset includes furniture and household textiles like carpets, flooring, drapes, mattresses, bedding, linen, and bathroom textiles and drapery.
2. Household Products – Electrical
This dataset includes large electrical appliances like refrigerators and freezers, cooking appliances, washing machines, dryers and dishwashers and small electrical appliances such as toasters, coffee makers, blenders and mixers, vacuum cleaners, irons, sewing and knitting machines, lamps and lighting.
3. Household Products – Non-Electrical
This dataset includes cookware, cutlery, flatware, glass, porcelain, ceramics, plastic items, home accessories, etc.
4. House and Garden Tools
This dataset includes:
- Gardening items like plants, seeds, fertilizer and soil.
- Garden tools such as hoses, spades, shovels, rakes, and forks.
- The use and repair of motorized equipment such as garden lumber and fencing, gardening tools, garden supplies, drills, saws, and lawn mowers.
- Ladder and steps.
- Indoor fittings (tiles, wood, plastics) including flagstones, custom wood cuttings, moldings, prefabricated panels, parquetry, plastics, and paneling.
- Appliances, machines, tools, and accessories such as ladders, wheelbarrows, drills, high-pressure cleaners, hammers, work clothes, and ironware/household hardware.
5. Household Maintenance
This dataset includes:
- Cleaning and maintenance products such as soaps, washing powders, brooms, dusters, tea towels, floor cloths, sponges etc. It also includes polishes, creams and other shoe-cleaning articles.
- Paper products such as filters, tablecloths and table napkins, kitchen paper, vacuum cleaner bags and cardboard tableware, including aluminum foil and plastic bin liners.
- Household articles like screws, nuts, matches, candles, lamp wicks, clothes pegs, clothes hangers, pins, safety pins, sewing needles etc.
- Domestic services supplied by paid staff employed in the private service such as butlers, cooks, maids, drivers, gardeners, governesses, secretaries, tutors and au pairs.
- External services like baby-sitting and housework and household services such as window cleaning, disinfecting, fumigation and pest extermination.
- Dry-cleaning and laundering and the dyeing of household linen, household textiles and carpets.
- The rental of furniture, furnishings, carpets, household equipment and household linen.
Spotzi uses the Consumer Expenditure Survey programs of census bureaus and commercial partners. These surveys provide data on expenditures, income, and the demographic characteristics of consumers.
A model links the outcome of consumer spending surveys to households with similar socioeconomic characteristics. The result is a consumer spending dataset divided into 5 main categories and 17 subcategories. The level of detail varies per country and ranges from the census level to the postal code level.
Per spending category, Spotzi calculates the average spending of all people living in a certain country. This average is set to 100. If people within a certain area spend more on a certain product the index will be higher than 100. For instance, if the index is 200, the people in this postal code area spend twice as much on that product.
Delivery of this data
The data can be obtained for specific areas of your choice, such as your company’s catchment areas or external sales regions. Spotzi adds the consumer styles dataset to our postal code maps so they can be used in our Mapbuilder or third-party GIS software.
Regional code (e.g., regional identifier, postal code, etc.)
Regional designator (e.g., municipality, postal code, street)
Inhabitants and households
The number of inhabitants and households within a given region, provided in absolute and per mill values.
Purchasing power for a specific product line in millions
This dataset provides the amount of disposable income available for a specific product line (in millions) among the population of a given region.
Purchasing power for a specific product line in per mill values
This dataset indicates how the purchasing power for a specific product line (per mill) in a given region compares to the nationwide purchasing power for this product line. The sum of all per mill values equals 1000.
Purchasing power for a specific product line per inhabitant
This dataset provides the average annual expenditure per person in a given region for a specific product line. Values are listed in euros.
Purchasing power for a specific product line as an index per inhabitant
This dataset reveals the index value per inhabitant: a figure based on the national average of 100.0 per inhabitant. Thus, an index value of 110.0 means that the inhabitants of the region in question spend 10% more of their net income on the product line in question than the national average. By the same token, an index value of 90.0 means that the purchasing power for the region and product line in question is 10% less than the national average.
Maps for Marketing
Spotzi has census data from multiple countries fully mapped out. Each census area has an extensive set of features available. We offer an extensive range of data to instantly select the right audience for your campaign.
Unlock the potential of our purchasing power data of 17 food and non-food product lines. This is an ideal dataset for enriching your customer database or for mapping out new potential customers.
Discover trends in housing prices and act at the right time. Buy or sell land or properties based on insights created with our data.
Spotzi has postal code boundary maps of more than 30 countries in its DataShop. For example, you can easily segment your potential customers based on a postal code area and combine this with our consumer styles dataset.