Used for market research and product sampling, new retail outlet location, direct mail campaigns, and overall marketing planning, particularly for consumer marketing. Geodemographic databases have their limitations: it is not always the case that people living in the same location have the same lifestyles or buying tendencies. Often the information on the databases is outdated after two or three years and is not constantly refreshed; the information is not in-depth enough to form more sophisticated profiles. Geodemographic databases can link together data from the National Census with other information, for example for credit ratings or investor registers, to enable identification of different types of households. Using cluster analysis, the geography can be broken down into neighbourhoods of around 150 households. The basic assumption is that two people living in the same neighbourhood are more likely to be similar than two people chosen at random. In the UK there are programs such as ACORN (A Classification of Residential Neighbourhoods) that divides up the entire UK population in terms of the type of housing in which they live. For each of these areas, a wide range of demographic information is generated. The system allows assessment of product usage patterns, dependent upon the research conducted within national surveys. There are 54 separate groupings including for example:
These geodemographic categories are also classified and segmented as: Thriving, Expanding, Rising, Settling, Aspiring and Striving. See also market research.
Another database is CCN's MOSAIC. This has started to build classifications within Europe. This system also analyses information from various sources including the census, which is used to give housing, socio-economic, household, and age data; the electoral roll, to give household composition and population movement data; postcode address files to give information on post-1991 housing and special address types such as farms and flats, as well as credit search information and bad debt risk.
Another is PINPOINT. Pinpoint Identified Neighbourhoods utilizes information from disparate sources and overlays this with Ordnance Survey data to target individuals.
FiNPiN, Financial PIN, uses data not only from the census, but data from the Financial Research Survey which comprises 30,000 respondents. It was intended to segment the financial services buyers' market. Information concentrates upon details of financial holdings and usage patterns, so that FiNPiN neighbourhoods are able to describe financial activity as well as demographics. Data sources include: the number of company directors, the level and value of share ownership, the demand for various types of financial service, such as household insurance, home ownership, mortgage holding, and county court judgements.