The fragmentation created by multiple viewing devices, data sources and measurement systems continues to be an obstacle to fully employing the sophisticated audience-targeting capabilities possible through advanced TV platforms.
Case in point: Smart TV and set-top-box (STB) data each have advantages and disadvantages for audience targeting and attribution purposes. As a result, the practice of combining the two types of privacy-compliant data to try to build large-scale, granular datasets capable of measuring all forms of TV viewing has been on the rise.
Now, the Coalition for Innovative Media Measurement (CIMM), in partnership with Pre-Meditated Media and Janus Strategy & Insights, is launching a study to develop best practices for this emerging capability.
“Our goal is to provide the industry with an understanding of how to leverage smart TV and STB data together, not only helping to improve data quality within a household, but create a blueprint for more nationally representative, deterministic TV viewing datasets that can be used for cross-platform planning, activation and measurement,” sums up Howard Shimmel, president, Janus Strategy & Insights.
Smart TV data can provide a broad footprint of data spread geographically across the country, and help refine the edit rules used for STB data, while STB data provides a fuller picture of TV usage for most TV sets in a household.
“STB data are currently being made available to some media measurement vendors, and also to audience-based planning and buying platforms,” but “none of the vendors analyzing such data have a nationally representative footprint,” points out Jane Clarke, CIMM’s managing director and CEO. “This study aims to examine how smart TV data can complement STB data to increase the value of the combined datasets.”
“Smart TVs can report viewing data in near-real time, but the ACR (automated content recognition) data collected don’t represent all TV sets in the average home,” adds Gerard Broussard, principal, Pre-Meditated Media.
“STB data are sourced from a much larger household footprint than smart TVs,” but “calibrating the STB signals to viewing metrics and matching program names adds more time to the reporting process,” he says. “We hope to find how, perhaps by combining the data, there might be a better solution for releasing more accurate tuning data on a timely basis.”
Even in combination, the two datasets lack data on broadcast network viewing in over-the-air (OTA) households — those without broadband access or pay-TV subscriptions. But the combination comes closer to providing data on most U.S. households, which can then be further calibrated to provide the OTA viewing via more traditional panel methodology, according to the study partners. Panels (i.e. Nielsen methodology) are also still required to understand who and how many people are watching a TV set.
Reflecting this, the study also aims to add transparency and increase industry confidence in using new data-plus-panel TV measurement approaches.
The study will be conducted in two phases.
Phase 1 is a review of ACR and STB data providers, through interviews with providers and aggregators, to identify next steps. Factors examined will include sample size, data captured and reported, data processing rules, and availability.
Phase 2 will consist of interviewing companies integrating ACR and STB datasets to review existing methods, learn about best practices and potential obstacles, and identify specific design recommendations for creating an integrated reporting system.
A report on the results will be released later this year.