Automated persona feature selection

A non-transitory computer readable storage medium storing processor-executable computer program instructions that, when executed, cause a computer processor to perform a method, the method comprising:

• receiving histories of a plurality of entities, each respective history comprising a plurality of features, each entity assigned to at least one cluster from a plurality of clusters;
• for each of the plurality of features corresponding to the plurality of entities, determining a corresponding general prevalence coefficient, each general prevalence coefficient representing a prevalence of entities in the plurality of entities having the respective feature in their history;
• for each cluster, generating a respective feature list comprising features in the histories of the entities assigned to the respective cluster, each feature having a corresponding in-cluster coefficient representing prevalence of entities in the respective cluster having the respective feature in their history;
• selecting distinguishing features from the feature lists for each respective cluster by selecting features wherein a difference between each selected feature's corresponding in-cluster coefficient and the selected feature's corresponding general prevalence coefficient is greater than a coefficient threshold;
• for each selected distinguishing feature corresponding to each respective cluster, determining a variance of the respective selected distinguishing feature's coefficients across the clusters;
• generating a discriminating feature list for each respective cluster by selecting distinguishing features of the respective cluster with a respective variance greater than a variance threshold value;
• choosing at least one cluster by comparing features of a history of a specified entity to features of the respective discriminating feature lists of the clusters;
• customizing content according to the at least one chosen cluster;
• and
• sending the customized content to the specified entity.

A method, comprising:

• receiving histories of a plurality of entities, each respective history comprising a plurality of features, each entity assigned to at least one cluster from a plurality of clusters;
• for each of the plurality of features corresponding to the plurality of entities, determining a corresponding general prevalence coefficient, each general prevalence coefficient representing a prevalence of entities in the plurality of entities having the respective feature in their history;
• for each cluster, generating a respective feature list comprising features in the histories of the entities assigned to the respective cluster, each feature having a corresponding in-cluster coefficient representing prevalence of entities in the respective cluster having the respective feature in their history;
• selecting distinguishing features from the feature lists for each respective cluster by selecting features wherein a difference between each selected feature's corresponding in-cluster coefficient and corresponding general prevalence coefficient is greater than a coefficient threshold;
• for each selected distinguishing feature corresponding to each respective cluster, determining a variance of the respective selected distinguishing feature's coefficients across the clusters;
• generating a discriminating feature list for each respective cluster by selecting distinguishing features of the respective cluster with a respective variance greater than a variance threshold value;
• choosing at least one cluster by comparing features of a history of a specified entity to features of the respective discriminating feature lists of the clusters;
• customizing content according to the at least one chosen cluster;
• and
• sending the customized content to the specified entity.