Pet Health Monitoring through Micro-Influencer-Boosted CTR: A Machine Learning and NLP Approach

Abstract

This research presents an innovative approach to enhance pet health monitoring by leveraging micro-influencer marketing and click-through rate (CTR) optimization through advanced machine learning and natural language processing (NLP) techniques. With the increasing importance of pets in households and the growing demand for personalized pet healthcare solutions, this study aims to bridge the gap between pet owners and relevant health information. Our methodology involves the identification and collaboration with micro-influencers in the pet care domain, who have a significant online presence and can effectively disseminate health-related content to pet owners. We employ machine learning algorithms to identify suitable micro-influencers and develop NLP models to analyze and categorize their content for relevance and accuracy in pet health monitoring. By strategically incorporating 3D printing and EdTech (Educational Technology) components, we aim to create engaging and informative materials that micro-influencers can use to educate pet owners on health monitoring practices. The ultimate goal is to increase CTR on such content, leading to better-informed pet owners and improved pet health outcomes. This research contributes to the fields of pet health monitoring, micro-influencer marketing, and digital health communication by demonstrating the potential of machine learning and NLP techniques to bridge the gap between pet owners and valuable health information. The findings have implications not only for pet health but also for broader applications in healthcare communication and digital marketing strategies.