CIMM Launches TV Attribution Best Practices Study

The Coalition for Innovative Media Measurement (CIMM) has announced a new TV attribution best practices study. 

The research, currently in early stages, is part of an ongoing partnership with the 4A’s Media Measurement Task Force. 

It will investigate how attribution providers’ use of different modeling techniques and data sources, including both STB versus smart-TV ACR data, affect attribution results. 

Specifically, the research will investigate the drivers behind differences in attribution results from six national, linear television campaigns that aired in 2019, to compare TV ad occurrence data sources, TV exposure data sources and delivery across multiple syndicated data providers. 

Factors studied will include what contributes to differences in the data that detect TV ad occurrences, how ad occurrences are currently missed or overcounted, how viewership estimates vary between the different “footprints” and technical limitations of ACR and STB data, and how the differences impact model lift estimates and marketers’ decisions. 

CIMM and the 4A’s will work with Alphonso, Ampersand, Comscore, Hive, iSpot, Kantar, Nielsen, Samba, TVSquared, 605 and other companies. 

Following the study, Janus Strategy and Insights and Sequent Partners will analyze the data. CIMM will release a report based on their resulting best practices recommendations for attribution model TV data inputs. 

“As more granular TV data is becoming available for attribution, we have entirely new methods and practices that need to be both vetted by the television industry and better understood by end users,” said CIMM Managing Director and CEO Jane Clarke (shown). “This study will be a great first step for the industry to gain insight into the impact of the data on attribution results and begin to create best practices and greater industry confidence.” 

No completion date has been set for the study. CIMM will present initial results to members during its annual Summit on February 6.

Next story loading loading..