Household level segmentation method and system
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.
1. A segmentation system for classifying consumers in clusters, comprising:
means for generating a plurality of classification trees each including both behavioral and demographic consumers, segmenting variables for classifying a given consumer population, each of said classification trees including a plurality of decision nodes and a plurality of terminal nodes, and producing a consumer cluster set having a plurality of consumer clusters represented by the terminal nodes, each decision node indicating a certain portion of the consumer population and splitting the certain portion of the consumer population into at least two other nodes in response to one of the consumer segmenting variables; and
means for searching said consumer cluster sets for an optimal consumer cluster set that optimizes a measure of the behavioral and demographic data,
wherein consumers in each consumer cluster of said plurality of consumer clusters in the optimal consumer cluster set have similar behavioral and demographic characteristics to each other and different behavioral or demographic characteristics from consumers in all other consumer clusters of said plurality of consumer clusters in the optimal consumer cluster set,
wherein the means for searching includes:
means for storing profile definitions data for defining evaluation profiles to evaluate partitioning of the consumer population;
means for determining counts for each of the decision nodes of each of the classification trees, the counts including a right split count, a left split count, and a total count for each of the decision nodes;
means for storing profile data in the form of summaries of the counts;
means for storing segment definitions data including variables used to define segments; and
means for comparing performance of the classification trees based on stored profile definitions data, profile data, and segment definitions data to determine the classification tree producing the optimal consumer cluster set,
whereby the consumer clusters in the optimal consumer cluster set are used to focus marketing on groups of consumers.
2. The segmentation system according to claim 1 , wherein said classification trees use Zhang's methodology.
3. The segmentation system according to claim 1 , wherein said means for searching uses a partitioning program.
4. The segmentation system according to claim 1 , further comprising:
means for generating summary data, said summary data being a summarization of data contained in said means for generating; and
means for storing said summary data.
5. The segmentation system according to claim 1 , wherein said means for storing profile definitions data comprises a database.
6. The segmentation system according to claim 1 , wherein said means for storing profile data comprises an electronic file.
7. The segmentation system according to claim 1 , wherein said means for storing segment definitions data comprises a dbase file.
8. The segmentation system according to claim 1 , wherein said means for generating comprises a dbase table.