Systems and methods of personifying viewership data

A system comprising:

• at least one processor;
• at least one memory comprising instructions, which when executed by the at least one processor, cause the at least one processor to:
• receive training data including tuning data, household member data, and initial person level viewership distribution data;
• aggregate the tuning data from one or more user devices associated with a household to generate an observed household viewership distribution;
• calculate, via a prediction model, an implied household viewership distribution based on the initial person level viewership distribution data associated with one or more members of the household;
• compare the implied household viewership distribution to the observed household viewership distribution;
• adjust, based on the comparison of the implied household viewership distribution to the observed household viewership distribution, the prediction model and/or the initial person level viewership distribution data such that the implied household viewership distribution more closely aligns with the observed household viewership distribution;
• calculate an updated implied household viewership distribution based on the adjusted prediction model and/or the adjusted person level viewership distribution data;
• compare the updated implied household viewership distribution to the observed household viewership distribution;
• adjust, based on the comparison of the updated implied household viewership distribution to the observed household viewership distribution, the adjusted person level viewership distribution data such that the updated implied household viewership distribution more closely aligns with the observed household viewership distribution;
• and
• generate a report with the readjusted person level viewership distribution data.

A non-transitory computer-readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to:

• receive training data including tuning data, household member data, and initial person level viewership distribution data;
• aggregate the tuning data from one or more user devices associated with a household to generate an observed household viewership distribution;
• calculate, via a prediction model, an implied household viewership distribution based on the initial person level viewership distribution data associated with one or more members of the household;
• compare the implied household viewership distribution to the observed household viewership distribution;
• adjust, based on the comparison of the implied household viewership distribution to the observed household viewership distribution, the prediction model and/or the initial person level viewership distribution data such that the implied household viewership distribution more closely aligns with the observed household viewership distribution;
• calculate an updated implied household viewership distribution based on the adjusted prediction model and/or the adjusted person level viewership distribution data;
• compare the updated implied household viewership distribution to the observed household viewership distribution;
• adjust, based on the comparison of the updated implied household viewership distribution to the observed household viewership distribution, the adjusted person level viewership distribution data such that the updated implied household viewership distribution more closely aligns with the observed household viewership distribution;
• and
• generate a report with the readjusted person level viewership distribution data.