System and methods for calibrating user and consumer data

A non-transitory computer-readable medium encoded with instructions that, when executed by a processor, perform a method in a computing system for calibrating a subject data set of behavior data based on information from a reference data set of behavior data, each data set containing a plurality of participants and associated transactional data, the method comprising:

• partitioning the reference data set into a plurality of reference data partitions, using a data partitioning scheme, no two reference data partitions sharing a participant in common;
• partitioning the subject data set of behavior data into a plurality of subject data partitions using the data partitioning scheme, wherein:
• each of the plurality of subject data partitions is based on a viewer characteristic that corresponds to a characteristic associated with a corresponding reference data partition;
• and
• no two subject data partitions of the plurality of subject data partitions share a participant in common;
• calculating weights associated with each of the plurality of subject data partitions to adjust a distribution of the plurality of subject data partitions based upon a distribution of the plurality of reference data partitions;
• calculating a statistic for each of the plurality of subject data partitions;
• and
• preparing adjusted calculated statistics by applying the calculated weight for each subject data partition to the calculated statistic for each subject data partition, the applied weights producing calibrated estimates of the statistics for the plurality of subject data partitions.

A method in a computing system for calibrating a subject data set of behavior data based on information from a reference data set of behavior data, each data set containing a plurality of participants, the method comprising:

• partitioning the reference data set into a plurality of reference data partitions, using a data partitioning scheme;
• partitioning the subject data set of behavior data into a plurality of subject data partitions using the data partitioning scheme, wherein:
• each of the plurality of subject data partitions has one or more variables that are in common with the one or more variables associated with the corresponding reference data partition;
• calculating weights associated with each of the plurality of subject data partitions to adjust a distribution of the plurality of subject data partitions based upon a distribution of the plurality of reference data partitions;
• calculating a statistic for each of the plurality of subject data partitions;
• and
• preparing adjusted calculated statistics by applying the calculated weight for each subject data partition to the calculated statistic for each subject data partition, the applied weights producing calibrated estimates of the statistics for the plurality of subject data partitions.