METHOD AND SYSTEM FOR PREDICTING CONSUMER BEHAVIOR

1 . Method of predicting consumer response to given content, including the steps of

collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer behavior and the dataset containing at least twice the number of entries to provide statistical validity;

constructing a classification tree structure using the dataset, wherein

the dataset is subdivided into learning and validation datasets of substantially equal size;

the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split; and

each successive split of the learning dataset is performed only if such split produces child nodes statistically different from one another; and

an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset;

receiving a data item related to a new consumer, including values for the segmentation variables;

computing the likely response of the new consumer to the content, employing the classification tree data structure.

2 . The method of claim 1 , wherein the segmentation variables include data relating to internet navigation history of the consumer.

3 . The method of claim 1 , wherein the segmentation variables include information related to categories of websites visited by the consumer.

4 . The method of claim 1 , wherein the subdivision of the dataset is made on the basis of a variable independent of the segmentation variables or the consumer response.

5 . The method of claim 1 , further including the step of calculating the value of the consumer response to the provider of the content.

6 . The method of claim 1 , wherein the process is repeated for a plurality of content items, producing a library of classification data structures.

7 . Method of predicting consumer response to given content presented in connection with viewing a website on the internet, including the steps of

collecting a dataset of consumer response to the content, each data item including values for a selected set of segmentation variables related to past consumer internet behavior, the dataset containing at least twice the number of entries to provide statistical validity;

constructing a classification tree structure using the dataset, wherein

the dataset is subdivided into learning and validation datasets of substantially equal size;

the criterion for each successive split is the lowest entropy of segmentation variables not employed to the point of such split; and

each successive split of the learning dataset is performed only if such split produces child nodes statistically different from one another; and

an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset;

receiving a data item related to a new internet consumer, including values for the segmentation variables;

computing the likely response of the new consumer to the content, employing the classification tree data structure.

8 . The method of claim 7 , wherein the segmentation variables include data relating to internet navigation history of the consumer.

9 . The method of claim 7 , wherein the segmentation variables include information related to categories of websites visited by the consumer.

10 . The method of claim 7 , wherein the subdivision of the dataset is made on the basis of a variable independent of the segmentation variables or the consumer response.

11 . The method of claim 7 , further including the step of calculating the value of the consumer response to the provider of the content.

12 . The method of claim 7 , wherein the process is repeated for a plurality of content items, producing a library of classification data structures.

13 . A classification tree data structure useful for predicting consumer response to given content, wherein the tree structure is constructed by a process including the steps of

subdividing the dataset into learning and validation datasets of substantially equal size;

determining each successive split based on the lowest entropy of segmentation variables not employed to the point of such split; and

performing successive split of the learning dataset only if

such split produces child nodes statistically different from one another; and

an identical split of the validation data set produces child nodes statistically similar to child nodes produced on the learning dataset.

14 . The classification tree structure of claim 13 , wherein the segmentation variables include data relating to internet navigation history of the consumer.

15 . The classification tree structure of claim 13 , wherein the segmentation variables include information related to categories of websites visited by the consumer.

16 . The classification tree structure of claim 13 , wherein the subdivision of the dataset is made on the basis of a variable independent of the segmentation variables or the consumer response.

17 . The classification tree structure of claim 13 , further including the step of calculating the value of the consumer response to the provider of the content.

18 . Method of predicting consumer response to given content, including the steps of

assembling a library of binary tree tools, including the steps of

building a consumer response dataset, including the steps of

exposing consumers to selected content;

collecting each consumer response, measured as a value of a response variable;

collecting consumer segmentation characteristics, measured as values of each of a set of consumer segmentation variables;

continuing the collection until the dataset consists of at least twice the number of data items required for a statistically valid sample;

dividing the dataset into a learning set and a validation set, based on a variable independent of either the response variable or any segmentation variable, the datasets being substantially equal in size and each being sufficiently large to provide statistical reliability;

constructing a binary tree by successively splitting nodes, each splitting step including the steps of

employing the learning dataset to obtain a proposed split, including

splitting the node hypothetically, based on each value of each segmentation variable;

calculating the entropy of each hypothetical split;

choosing the split having the minimum entropy as the proposed split;

performing a statistical test on the resulting nodes to determine whether they differ statistically;

collapsing the proposed split in the event no difference is found;

validating the proposed split, including

replicating the proposed split on the validation dataset;

performing a statistical test on the resulting nodes to determine whether they are statistically similar to like nodes of the proposed split;

collapsing the proposed split in the event that no similarity is found;

continuing the tree construction process, with each successive split employing only those segmentation variables not employed in an adopted split;

receiving data concerning an individual consumer, including values for the set of segmentation variables;

determining the most appropriate content to present to the consumer, including the steps of

obtaining a value for the consumer dataset for each binary tree tool in the library; and

selecting the content associated with the binary tree tool producing the highest response value.