Many organizations utilize demographic, and geographically-based demographic information, for a wide variety of purposes. For example, the United States conducts a constitutionally mandated, geographically-based census survey every ten years for purposes of readjusting governmental and political representation districts. Demographic information available from the census includes age, gender, ethnicity, race, income, education, etc. The census is conducted within each state plus the District of Columbia, with information provided at a comparatively fine-grained level, with the U.S. geographically divided into over 3,000 counties, which are further geographically subdivided into over 65,000 census tracts, which are further geographically subdivided into over 208,000 census block groups, and which are further geographically subdivided into over 8,200,000 census blocks.
Based on the notion that demographics are often skewed by geographies, such as wealthy neighborhoods and ethnic neighborhoods, “geo-demographics” have often been utilized to target or preferentially communicate with certain groups, such as for political and marketing communications. In other circumstances, governmental and business decisions are based on such geo-demographics, such as decisions for locating a new post office, a new dealership, a new shopping center or store, and so on. In addition, for certain industries, such as the broadcast and print media, their usage is specifically skewed by such geo-demographic “segments”, as such media may only have applicability for certain demographic groups within certain defined geographies.
As a consequence, geo-demographic “clustering” has evolved as a way to try to identify and describe groups of individuals based upon their demographic characteristics. Certain regions are then defined by the corresponding demographic clusters contained in that region. While certainly better than “blind” targeting or analysis by at least providing some differentiation (or discriminatory) capability, such clustering techniques do not provide other significant information, such as behavioral and attitudinal information. (“Discriminatory”, as used herein, should be utilized in a statistical sense, to provide differentiation on the basis of certain common characteristics, such as differentiating people based upon their age groups or educational levels.)
In addition, such clustering techniques tend to be either under inclusive or over inclusive. For example, such clustering is under inclusive, as small but significant groups of people in certain regions may be omitted from analysis altogether, as not falling within the major cluster groups for those regions. As a consequence, such clustering systems lose population nuances and minority influences, which may, in fact, be significant. For example, current clustering techniques from a wide variety of companies would group Hispanic individuals of Dominican Republic origin into a cluster residing in large cities; actual analysis would reveal, however, that many of these cities having such a “large city cluster” do not, in fact, have any significant population of such Hispanic individuals of Dominican Republic origin.
The over inclusiveness of such clustering groups is also well-known. Groups of individuals with very different behaviors and attitudes, for example, may be grouped together. For example, a young or middle-aged statistician with a doctoral level education, who happens to live in Florida, may, by virtue of his residence, be included within a cluster of retired individuals. Because of such over inclusiveness, nuances and minority information of certain regions and neighborhoods are lost in these forms of cluster analysis.
As a consequence, a need remains for other forms of analysis, which not only provide geo-demographic information, but which also provide important behavioral (e.g., what people say and do) and attitudinal (e.g., beliefs, outlooks, psychographics) information. Such an analytical system should have a firm foundation in empirical research, such as through the use of statistically significant or relevant surveys from a nationally represented sample of individuals.
Such systems and methods should be fine-grained, and should accurately preserve behavioral nuances and minority information. Such methods and systems should have the capability to model all regions and their subregions, even those that have an insufficient sample size within nationally representative surveys.