To outsiders, it may seem as though Netflix simply chooses to create content that it thinks “subscribers might like”. The truth is more complex. Take for example the popular American political drama web television series ‘House of Cards, the first original online-only web television series to receive 33 Primetime Emmy Award nominations. While some may assume the show was green-lit solely because it had a great plot with great actors, in reality, the decision was strongly supported by data provided by Netflix customer base’s viewing habits.
This data driven approach is proving its merit. In the traditional approach for content acquisition, in which shows are pitched primarily on the production teams’ insights, only 1 in 3 shows are popular enough with audiences for a second season to be produced. On the other hand, by using the big data approach, 4 in 5 shows are popular enough for a second season production. When digital data is leveraged, critical patterns of consumer intent, behavior, and preferences can be uncovered. By utilizing this data, content providers can identify or create projects that will have a higher likelihood of engaging their target audience. Given the investment required for content and the highly competitive nature of the entertainment industry, the ability to increase the hit rate of a platform’s content library through data analytics will become a significant competitive advantage. Native online players such as Netflix already understand this.
As data becomes a more common way of guiding content investments, the amount of information content providers will need to collect to maintain their competitive advantage will far exceed their processing capabilities. The amount of information available in the digital ecosystem is expected to grow from 130 exabytes today to 40,000 exabytes by 2020, which is impossible for humans to analyze.
How then can content providers plough through the huge amount of data to analyze and identify the type of content they should acquire or create?
Content providers should begin testing artificial intelligence (AI) to sort through the massive data pools and drive out content investment insights with minimal human oversight.
AI is based on algorithms that can learn from data without relying on rules-based programming. A machine learning program starts by analyzing high volumes of data to learn and can then use its learnings to produce valuable insights.
For instance, AI systems could analyze plot themes, characters, degree of humour in combination with time spent viewing, percentage of viewers who watch the next episode, and even unstructured data like social media commentary to recommend key elements of a plot twist that have proven successful.
Disney has already begun testing AI on children’s story books to produce new story lines that will resonate with its young audiences.
In preparation for implementing this technology, content providers will need to understand the AI developer landscape to identify who to work with as well as develop new internal processes to integrate AI learning into their workstreams.
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