Unlocking Viewer Insights in Linear Television: A Machine Learning Approach

Amidst the digital transformation, traditional linear TV faces major challenges, including fragmented viewership, fixed schedule, and inaccurate targeting. Therefore, this paper proposes a novel Machine Learning framework to understand the audience’s demographics from their viewing behaviour. By employing state-of-the-art classification models on an extensive TV first-party dataset, we achieved an average 88.6% accuracy in correctly identifying each household demographics. Our result offers promising outcomes for refining strategies within linear TV to improve viewer engagement, content programming, and market insights.