An Assessment of the Impact of AI-Powered Recommendation Engines in Online Streaming Services: A Case Study of Digital Media Platforms in Katsina State
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- NGN 5000
Background of the Study
The digital media and entertainment industry has experienced significant growth due to the proliferation of internet access and the rise of on-demand streaming platforms. According to Oladipo and Sulaimon (2024), online streaming services are increasingly adopting Artificial Intelligence (AI) to enhance user experience and engagement. AI-powered recommendation engines, which analyze users' viewing history and preferences, are central to this transformation. These systems enable platforms like Netflix, YouTube, and Spotify to suggest content tailored to individual tastes, increasing user satisfaction and engagement.
For digital media platforms in Katsina State, the integration of AI in recommendation engines could offer substantial advantages, particularly in a market where personalized content delivery is still in the nascent stages. AI can assist in retaining users by ensuring they have access to content that aligns with their preferences and viewing patterns. However, despite the potential benefits, the adoption of AI-powered recommendation systems in Katsina State's digital media platforms is still underexplored. This study aims to assess how AI-powered recommendation engines impact user engagement and business performance in these platforms.
Statement of the Problem
While AI-driven recommendation engines have been widely implemented in global streaming services, many digital media platforms in Katsina State have yet to adopt these technologies. As a result, these platforms often struggle to personalize user experiences and enhance customer retention. Without personalized content suggestions, platforms may lose users to competitors with more advanced, user-focused recommendation systems. According to Yahaya and Bukar (2024), the lack of AI integration could limit the growth and profitability of digital media platforms in Katsina State.
This study seeks to evaluate the impact of AI-powered recommendation engines on customer engagement, retention, and business performance in digital media platforms in Katsina State.
Objectives of the Study
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To assess the adoption and implementation of AI-powered recommendation engines by digital media platforms in Katsina State.
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To evaluate the impact of these AI-powered engines on user engagement and customer retention.
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To explore the business performance implications of adopting AI-powered recommendation systems for digital media platforms in Katsina State.
Research Questions
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To what extent have AI-powered recommendation engines been adopted by digital media platforms in Katsina State?
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How do AI-powered recommendation engines affect user engagement and customer retention in these platforms?
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What is the impact of AI-powered recommendation engines on the business performance of digital media platforms in Katsina State?
Research Hypotheses
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AI-powered recommendation engines are not significantly adopted by digital media platforms in Katsina State.
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AI-powered recommendation engines do not significantly enhance user engagement or customer retention in these platforms.
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The adoption of AI-powered recommendation engines does not significantly improve business performance for digital media platforms in Katsina State.
Scope and Limitations of the Study
This study focuses on digital media platforms in Katsina State, assessing the implementation of AI-powered recommendation engines and their impact. The study may face limitations in terms of data availability from these platforms and potential reluctance from firms to share performance metrics due to confidentiality concerns.
Definitions of Terms
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AI-Powered Recommendation Engines: Algorithms that utilize artificial intelligence to analyze user data and recommend content based on past behavior and preferences.
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User Engagement: The level of interaction and involvement a user has with the content and platform, such as time spent watching or browsing.
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Customer Retention: The ability of a platform to keep its users over time, preventing them from switching to competitors.
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