Abstract: Methods and apparatus for household level segmentation are disclosed. An example method to classify consumers in clusters includes receiving population data indicative of a population of consumers and receiving a plurality of profiles, at least one profile to evaluate partitioning of the population of consumers. The example method also includes selecting at least one of the plurality of profiles based on a count limit value in accordance with a classification tree dimension split.

Abstract: Methods and apparatus for household level segmentation are disclosed. An example method to classify consumers in clusters includes receiving population data indicative of a population of consumers and receiving a plurality of profiles, at least one profile to evaluate partitioning of the population of consumers. The example method also includes selecting at least one of the plurality of profiles based on a count limit value in accordance with a classification tree dimension split.

Abstract: In one aspect, a system and method is provided for developing small geographic area population, household, business and demographic count estimates and projections using a MAF. The systems and methods described herein use mailing addresses and corresponding address related records, in conjunction with the DPV validation and RDI coding functionality, as well as ZIP+4 type coding, in order to build a MAF with unique DPV validated addresses. Each address in the MAF is delivery point coded (DPC), mail delivery validated, has a residential/business address code, and has a USPS ZIP+4 type, FIPS code, latitude and longitude and selected demographic data. The MAF is then tabulated directly or used in conjunction with current county level or Census Bureau estimates to generate estimates of the census block records or census block group records and/or areas of any size or shape.

Abstract: Methods and apparatus for household level segmentation are disclosed. An example method to classify consumers in clusters includes receiving population data indicative of a population of consumers and receiving a plurality of profiles, at least one profile to evaluate partitioning of the population of consumers. The example method also includes selecting at least one of the plurality of profiles based on a count limit value in accordance with a classification tree dimension split.

Abstract: A method and system for classifying consumers in clusters of similar behavioral and demographic characteristics. Demographic and behavioral data of consumers is used to generate a plurality of classification trees. The plurality of classification trees are searched for a classification tree resulting in an optimal set of clusters. The optimal set of clusters has clusters that are substantially homogeneous in demographic and behavioral characteristics.

Abstract: A method and system for classifying consumers in clusters of similar behavioral and demographic characteristics. Demographic and behavioral data of consumers is used to generate a plurality of classification trees. The plurality of classification trees are searched for a classification tree resulting in an optimal set of clusters. The optimal set of clusters has clusters that are substantially homogeneous in demographic and behavioral characteristics.