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Case study

The advanced television meter: More precise and faster audience behaviour data and insights into advert effectiveness

Daniel Tjondronegoro
Applied Marketing Analytics: The Peer-Reviewed Journal, 3 (1), 42-52 (2017)
https://doi.org/10.69554/LUCE1999

Abstract

This paper describes how new advanced television, video and radio measurement technology enables faster and more precise brand and advert effectiveness research and attribution. This is done through the use of new advanced proprietary automatic content recognition (ACR) technology applications that capture true audience exposure and impact. The paper describes the market opportunities for advertisers and broadcasters ready to adopt the data solutions from this technology. It explains why a new approach to ACR technology is required for passive — all-day — measurement through the use of smartphones. Finally, it describes some experiences through a case study of a top Australian television advertiser, including how actual exposure data were incorporated into one of Australia’s most sophisticated bespoke brand and advertising trackers.

Keywords: television; radio; advertising effectiveness analytics; passive viewing behaviour metering app; automatic content recognition; audio fingerprinting; real-time audience data

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Author's Biography

Daniel Tjondronegoro is co-founder of Beatgrid Media, a technology startup with new proprietary automatic content recognition technology. Beatgrid’s technology performs all-day content exposure measurement through the smartphone microphone without impacting privacy, data plans or daily battery life. It enables real-time measurement of television and radio audiences across-platforms, helping advertisers and broadcasters increase television and radio advertising effectiveness and return on investment.

Citation

Tjondronegoro, Daniel (2017, April 1). The advanced television meter: More precise and faster audience behaviour data and insights into advert effectiveness. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 3, Issue 1. https://doi.org/10.69554/LUCE1999.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 3 / Issue 1
© Henry Stewart
Publications LLP

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