Abstract
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.
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.
| Original language | English |
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| Title of host publication | Perspectives in Business Informatics Research |
| Subtitle of host publication | 23rd International Conference on Business Informatics Research, BIR 2024, Prague, Czech Republic, September 11–13, 2024, Proceedings |
| Editors | Václav Řepa, Raimundas Matulevičius, Emanuele Laurenzi |
| Publisher | Springer |
| Pages | 53-67 |
| Volume | 529 |
| ISBN (Electronic) | 978-3-031-71333-0 |
| ISBN (Print) | 978-3-031-71332-3 |
| DOIs | |
| Publication status | Published - 11 Sept 2024 |
Publication series
| Name | Lecture Notes in Business Information Processing |
|---|---|
| ISSN (Print) | 1865-1348 |
| ISSN (Electronic) | 1865-1356 |