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Optimizing Big Data + Panel Measurement Through Calibration
David Kurzynski – SVP, Data Science, Nielsen
Kyle Poppie – VP, Data Science, Nielsen
It is challenging to measure the smaller audiences of local TV and measurement challenges include false zero audience metrics and instability. Kyle Poppie (Nielsen) reviewed the evolution of local TV measurement, and this presentation demonstrated how Nielsen’s approach enables accurate measurement. Calibrating big data to a probabilistic panel controls for biases in the big data population that cannot be accounted for by weighting alone. The panel provides accurate and unbiased measurement at aggregate levels while big data provides greater coverage of granular behavior. An example demonstrated how the calibration of panel data and big data resulted in a more accurate weighted audience size. David Kurzynski (Nielsen) presented a case study that applied calibration to live data from a secondary station in New York. The improved result included fewer zero ratings and smoother trends. Key takeaways:- The goal of calibration is to achieve local TV measurement that provides accuracy and stability for audience levels and audience flows.
- Both big data and panel data are critical as inputs to calibration to achieve these goals. Audience levels are informed by both big data and panel homes, and audience flows are influenced by big data.
- Relative and total errors decrease as a result of calibration compared to panel-only currency.