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How Demand Data on My Hero Academia Can Inform Entertainment Executives' Decision Making Process

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Parrot Analytics Insights - June 2023

Are you an entertainment executive working at an OTT platform, SVOD platform, TV series financing company, entertainment talent agency, TV production company, studio, content distributor, linear network or pay TV network? Wondering how to make more informed content decisions, acquisition decisions, distribution decisions, or programming decisions? In this article, we will show you how demand data analysis on a popular TV series, "My Hero Academia," can be leveraged to help entertainment executives like you make informed business decisions.

Content valuation uses demand data to determine the dollar value of a title or a library for any platform in any region. In this case, the demand for "My Hero Academia" in the United States over the last 30 days is exceptional. Its demand is 42.4 times higher than the average TV show in the US, meaning it has a high-dollar value contribution to any platform in the United States. Only 0.2% of all TV shows have this level of demand, implying that this TV series is a strategic asset for any platform. Knowing the baseline value of a title is of utmost importance in content valuation, and "My Hero Academia" has a high baseline value in the United States.

Demand data can inform scalable concept testing for executives. The data shows that "My Hero Academia" has a comedy genre ranking of the 99.9th percentile in the United States. Executives can learn which elements of an existing show fans enjoy the most and optimize future productions for success. Furthermore, if executives are planning to acquire or produce a similar show, they can check the demand data for genres that exhibit the most opportunity and touchpoints of interest for potential subscribers.

If executives are deciding where to distribute their content, demand data can help. Suppose you have a show with a higher demand in the United States than in its country of origin, like "My Hero Academia," where the demand for it is 186% of the demand in Japan. In that case, executives can leverage the data and look for other regions where their content can create similar interest.

Engaging subscribers is one of the most significant challenges that executives face. Knowing the right titles that appeal to their viewers can help improve subscriber retention and acquisition. For example, fans of "My Hero Academia" also like popular shows like "Attack on Titan," "Rick and Morty," and "The Witcher." By understanding these content preferences, executives can suggest a marketing strategy, target advertisements to specific audiences, and direct viewers to relevant titles that will encourage higher audience retention.

Demand data can also inform executives on optimal release strategies and pricing. In the case of "My Hero Academia," although the demand has decreased in the past 90 days (48.1 times the average), it has increased by 4.0% in June 2023. Demand spiked at the end of June 2023, indicating the release strategy employed may have worked well. The Parrot Pulse for the show indicates exceptional momentum, which means the pace of growth is fast. Understanding how effectively past marketing campaigns promoted a show can help shape future strategies, such as the optimal pricing for exclusive shows.

Lastly, demand data can inform executives on the best platforms to target for their content. In the case of "My Hero Academia," executives know that its global performance is exceptional across six dimensions and that this show is in demand across the world. Social media campaigns promoting this show in these regions may help attract interest from different platforms across various regions.

As shown, demand data can provide valuable insights to assist entertainment executives in making informed decisions. By understanding audience preferences, determining content valuation, and leveraging insights on distributed content, executives can make better content decisions, acquisition decisions, distribution decisions, and programming decisions.

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