Big Data Analytics in the Entertainment Industry: Audience Behavior Analysis, Content Recommendation, and Revenue Maximization

Authors

Keywords:

Big data analytics, Entertainment industry, Audience behavior analysis, Content recommendation systems, Revenue maximization

Abstract

This research contributes to the understanding of the significant role of big data analytics in transforming the entertainment industry. In this study, we investigate the impact of big data analytics on the entertainment industry, focusing on three key aspects: audience behavior analysis, content recommendation, and revenue maximization. To understand audience behavior, entertainment companies leverage big data analytics to collect and analyze vast amounts of data from various sources, including social media platforms, streaming services, ticket sales, and website traffic. By analyzing viewer preferences, engagement metrics, and geographic information, companies gain valuable insights into audience behavior. These insights help in creating content that resonates with the target audience, optimizing future content creation, and tailoring marketing strategies based on geographical preferences. Furthermore, big data analytics plays a vital role in powering content recommendation systems. Through collaborative filtering and content-based filtering techniques, entertainment platforms personalize content recommendations based on user behavior, preferences, and historical data. This enhances user satisfaction and increases the likelihood of discovering relevant and appealing content. Hybrid approaches that combine collaborative and content-based filtering techniques are also explored to achieve more accurate and diverse recommendations. Moreover, big data analytics enables entertainment companies to optimize revenue generation strategies. By analyzing historical data, market trends, and consumer behavior, companies can implement dynamic pricing strategies, adjusting ticket prices, subscription fees, or content pricing based on demand and viewer preferences. Additionally, targeted advertising based on user data enhances advertising revenue by delivering personalized advertisements. Furthermore, analyzing market data and consumer behavior patterns helps optimize licensing agreements and content distribution strategies, maximizing revenue opportunities.

Downloads

Published

01-06-2023

How to Cite

Ahmed, A., & Abdulkareem, A. M. (2023). Big Data Analytics in the Entertainment Industry: Audience Behavior Analysis, Content Recommendation, and Revenue Maximization. Reviews of Contemporary Business Analytics, 6(1), 88–102. Retrieved from https://researchberg.com/index.php/rcba/article/view/142