AGGREGATION-BASED METHODS FOR DETECTION AND CORRECTION OF TELEVISION VIEWERSHIP ABERRATIONS
A method cleans television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In various aspects, the group of set top boxes may be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, the method can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, that is based on a weekly or an approximately monthly frequency. The aberrant viewership can be detected by calculating a minimum expected number of viewers for a day, and comparing it to the actual number of households that reported viewers for that day.
1 . A processor-based method for cleaning television viewing data by detecting and correcting a viewership aberration, the method comprising:
selecting a period of time to analyze;
selecting a subject plurality of set top boxes corresponding to a first television service provider;
selecting a reference plurality of set top boxes corresponding to at least a second television service provider;
aggregating the viewing data across the subject plurality of set top boxes to create a subject viewing data pattern for the selected period of time;
aggregating the viewing data across the reference plurality of set top boxes to create a reference viewing data pattern for the selected period of time;
calculating a normalization factor according to at least the relationship of a size associated with the first television service provider compared to a size associated with the second television service provider;
applying the normalization factor to the viewing data patterns to make them comparable when the television service providers have different customer footprints;
determining, by a processor, if any part of the subject viewing data pattern deviates beyond a threshold from the reference viewing data pattern; and
if a deviation was determined, flagging the deviation for factoring into the calculation of a corrected viewership estimate.