Historically, writers and marketers mostly focused on creating content and story lines based on traditional research, focus groups, and the adaption of proven story lines, which has proven to be risky, costly and, oftentimes, not reflecting the likes and dislikes of viewers resulting in short-lived distribution and/or limited distribution.
Goals and Objectives
Enables the iterating of content and story lines based on data about viewers’ likes and dislikes, matching specific content with the most appropriate audiences, channels, and monetization models — and then monitor performance and make adjustments based on real-time data about who is watching and the program’s relevance to advertisers
AI and machine learning, AI technology results, predictive analytics, social, and relational databases
Use Case Summary
Iterative content creation enables the use of historical and real-time data to create, develop, and integrate content and story lines (in real time), which best reflects the likes and dislikes of viewers and the relevance to advertisers.