
Nielsen Claritas Consumer Buying Power database incorporates geography-based estimates of potential annual consumer expenditures for more than 350 household expenditure items. Consumer Buying Power data allows clients to identify the potential demand of consumers for products and services by geographic area regardless of where the consumers make their purchase.
Available as a licensable data option in Nielsen Claritas' software applications, the Consumer Buying Power database is an invaluable tool for target marketing. Examples of applications for this type of consumer expenditure data include:
Consumer Buying Power includes total household expenditures for over 350 specific buying power product categories and 73 summary categories in current-year and 5-year projections.
Additionally, Nielsen Claritas developed a cross-reference that leverages the Consumer Buying Power database to estimate potential consumer expenditures by store types for 41 distinct retail store types. Breakdowns of average household expenditures are also available for 53 Yellow Page headings.
The 73 product and service summary categories include:
Retail Store Types
Product and service categories are also aggregated into broader groupings of forty-one retail store types based on the type of retail store(s) in which specific products would likely be purchased. These groupings include:
Yellow Page Headings
Line items are aggregated into 53 headings based on where products/services would most likely be found in the Yellow Pages section of your local phone directory. Consumer Buying Power groupings include headings such as:
The Consumer Buying Power database is created using statistical models estimated from the Bureau of Labor Statistics' Consumer Expenditure Surveys (CEX). Annual income and expenditures are collected from the Interview and Diary surveys in varying detail, classified by income, age, consumer unit size, and other demographic characteristics of consumer units. Nielsen Claritas has an archive of the CEX data over multiple years, utilizing the latest five years to update the Consumer Buying Power data model coefficients each year.
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