How to use data to make a hit TV show | Sebastian Wernicke
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The introduction of Roy Price, a senior executive at Amazon Studios, responsible for selecting TV shows for production based on data analysis.
Roy Price and the TV Show Selection Process
- Roy Price's role involves choosing original content for Amazon Studios, aiming to select exceptional shows in a competitive market.
- Shows with ratings of nine or higher are considered top-tier, such as "Breaking Bad" and "Game of Thrones," while lower-rated shows like "Toddlers and Tiaras" represent the opposite end of the spectrum.
- Price focuses on avoiding average TV shows by ensuring selections lean towards high-quality content that excites viewers.
Data Analysis in TV Show Selection
The pressure on Roy Price to engineer success leads to the implementation of a competition-based selection process using viewer data.
Competition-Based Selection Process
- Price conducts a competition where eight show candidates produce pilot episodes available for free online viewing.
- Viewer behavior is closely monitored during these free viewings to gather data points for decision-making.
Comparison: Amazon vs. Netflix Approach
Contrasting approaches between Amazon's Roy Price and Netflix's Ted Sarandos in utilizing data analysis for show selection.
Divergent Strategies
- Netflix leverages existing viewer data to understand preferences and successfully licenses "House of Cards," achieving high ratings.
- In contrast, Amazon's approach results in an average show like "Alpha House," falling short of expectations set by Netflix's strategy.
Challenges in Data Analysis Decision-Making
Reflecting on the limitations and complexities of relying solely on data analysis for decision-making beyond entertainment industry applications.
Limitations of Data Analysis
- Despite vast amounts of collected data, successful outcomes are not guaranteed due to unforeseen factors influencing decision-making processes.
Decision-Making with Data
The speaker discusses the shift from evaluating TV shows to assessing individuals, highlighting the risks associated with data-driven decision-making.
Evaluating People vs. TV Shows
- Decision-making now involves judging people rather than TV shows.
- Contrasts the consequences of a bad TV show versus a flawed individual assessment.
- Acknowledges that data analysis doesn't always yield optimal outcomes despite abundant data availability.
Challenges in Data Analysis
The speaker explores instances where even proficient companies like Google face failures in data analysis, emphasizing the unpredictable nature of data-driven predictions.
Limitations of Data Analysis
- Google's successful flu outbreak prediction through data analysis is highlighted.
- Despite failures, data increasingly influences real-life decisions in various sectors like medicine and law enforcement.
Successful Decision-Making with Data
The speaker outlines a pattern for effective decision-making using data, emphasizing the importance of analyzing and synthesizing information correctly.
Pattern for Successful Decision-Making
- Decisions involve dissecting complex problems into components for thorough analysis.
- Data aids in understanding problem components but isn't sufficient for drawing conclusions; human judgment plays a crucial role.
Data and Brain Integration
The speaker discusses the synergy between data and human intelligence in decision-making processes, citing Netflix's success as an example.
Utilizing Data and Human Judgment
- Emphasizes the brain's ability to synthesize information effectively compared to relying solely on data.
- Netflix's strategic blend of data insights and human judgment led to successful decisions like producing "House of Cards."
Balancing Data and Decision-Making
The speaker cautions against letting data solely dictate decisions, advocating for a balanced approach that incorporates human judgment alongside analytical insights.
Role of Data in Decision-Making
- While valuable, excessive reliance on data can hinder exceptional results by overshadowing human intuition.