Targeting TV Viewers More Effectively Using K-Means Clustering

ER. SHREYAS MAHIMKAR; PROF.(DR.) ARPIT JAIN; ER. OM GOEL

Targeting TV Viewers More Effectively Using K-Means Clustering

Authors: ER. SHREYAS MAHIMKAR, PROF.(DR.) ARPIT JAIN, ER. OM GOEL

Unique Paper ID: 167451
Volume: 9
Issue: 7
PageNo: 973-984

Keywords: Audience Targeting TV Advertising K-Means Clustering Viewership Segmentation Machine Learning Data Preprocessing Feature Selection Viewer Preferences Content Personalization Demographic Information Viewing Habits Silhouette Score Elbow Method Segment Analysis Media Analytics

Abstract:
Effective audience targeting is crucial for optimizing TV advertising strategies and maximizing viewer engagement. Traditional methods of audience segmentation often fall short in capturing the complex, multidimensional nature of viewer preferences. This research paper explores the use of K-Means clustering, a widely adopted unsupervised machine learning algorithm, to enhance TV viewership targeting. By employing K-Means clustering, we aim to refine audience segmentation, enabling TV networks and advertisers to better tailor their content and advertising strategies to distinct viewer segments. In this study, we utilized a comprehensive dataset comprising TV viewership data, including demographic information, viewing habits, and program preferences. The K-Means algorithm was applied to segment viewers into distinct clusters based on their viewing patterns. The process involved several key steps: data preprocessing, feature selection, and model training. Data preprocessing included handling missing values, normalizing data, and selecting relevant features to ensure the clustering results are meaningful and actionable. The K-Means clustering algorithm was configured with varying numbers of clusters to identify the optimal segmentation that provides the most insightful and actionable results. The silhouette score and elbow method were used to determine the optimal number of clusters, ensuring that the segmentation reflects meaningful distinctions between viewer groups. Our analysis revealed several distinct viewer segments, each characterized by unique viewing behaviors and preferences. For instance, one segment showed a high affinity for sports and news programming, while another group preferred drama and entertainment content. These insights allow for more targeted advertising campaigns and content recommendations, enhancing viewer engagement and satisfaction. The results of this study demonstrate that K-Means clustering provides a robust framework for improving TV viewership targeting. By segmenting viewers more effectively, TV networks and advertisers can better align their strategies with viewer preferences, leading to more personalized content and optimized advertising spend. The research highlights the potential of K-Means clustering to transform traditional audience segmentation methods and offers a foundation for future research in applying advanced machine learning techniques to media analytics.

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ISSN: 2349-6002
Volume: 9
Issue: 7
PageNo: 973-984

Targeting TV Viewers More Effectively Using K-Means Clustering

Available: https://ijirt.org/Article?manuscript=164982

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