We are seeking expertise to develop an AI-powered content recommendation engine that enhances user engagement by delivering highly personalized streaming experiences. This project aims to leverage large language models and state-of-the-art AI technologies to predict user preferences and curate content dynamically.
Our target users are streaming platform subscribers who seek personalized and engaging content experiences. They range from casual viewers to dedicated binge-watchers and include diverse demographic groups with varying content preferences.
With an overwhelming amount of content available, users often struggle to find shows and movies they genuinely enjoy, leading to user dissatisfaction and increased churn rates. Solving this issue is critical to retaining our competitive edge and enhancing user experience.
The market is ready to pay for solutions that enhance user engagement due to the direct impact on revenue through increased subscriptions and reduced churn rates, driven by competitive pressure to offer superior content discovery options.
If not addressed, we'll face lost revenue from subscriber churn, decreased user engagement, and risk falling behind competitors who offer better personalization features.
Current alternatives include static recommendation engines and manual curation, which lack the dynamic and personalized approach that sophisticated AI solutions can offer.
Our AI-driven recommendation engine will provide real-time personalization by leveraging cutting-edge AI technologies, distinguishing us from competitors relying on traditional recommendation methods.
Our go-to-market strategy includes leveraging social media channels and targeted digital marketing campaigns to highlight the personalized streaming experience. We will also use partnerships with content providers to promote our unique recommendation features.