HLL-based TV panel audience extrapolation compatible with online audience measurement from Logs

Shen-Fu Tsai
Jim Koehler
Google LLC(2021)


This paper proposes an extension to the actionable reach modeling for cross-media audiences, whereTV audiences are measured via extrapolation from a panel or partial set-top-box data. In essence, a set of TV Virtual People are exclusively associated with, or represented by, a TV panelist q via an HR sketch s, which is an extension to an HLL sketch. We extrapolate q’s TV activity to this set of Virtual People, thus s serves as an input to the system and can be deduplicated with the digital part of the audience via a simple sketch merge.The main contribution of this paper is an efficient method that takes as input Q panelists and P, the union of Virtual People they represent, and assigns an HR sketch to each panelist. The efficiency-accuracy trade-off is controlled by a depth parameterD, and to help decide D in practical systems we provide an upper bound to the performance loss due to a finite depth D. The size of the deep sketch is roughly proportional to the depth. For example, for an error of no more than 1%, we can set D to 9.