If you work in the entertainment industry, demand data is a tool that can be utilized to make more informed decisions about content valuation, acquisition, distribution, and programming. Let's use "Lessons in Chemistry," a TV series that aired for the first time in 2023, as an example to illustrate how demand data can help with such decisions as a content producer or distributor.
One way "Lessons in Chemistry" can be evaluated using demand data is content valuation. Demand data can be used to answer questions such as, "How much is my series worth?" Parrot Analytics content valuation system draws on demand data and can reveal the dollar value of a content library to a platform over time. Content valuation can be applied to an individual series or an entire library, so you can start estimating the value of content on any streaming service. For "Lessons in Chemistry," we know that the show currently has a demand that is 10.4 times the average show in Germany over the last 30 days, indicating that it is a highly valuable asset for any platform that acquires the rights to distribute it in the region.
Another use of demand data is to inform content acquisition decisions. One common acquisition question that can be answered by demand data is, "How much global appeal does this title have in comparison with other titles in the genre, and how should funds be allocated to maximize future content investments?" "Lessons in Chemistry" has performed exceedingly well across the globe, outperforming the average TV show by at least 14.3 times the average demand in each of its top 10 global markets, including the United States, Canada, Australia, China, UK, Brazil, and Netherlands. As a content executive, these key insights can be used to invest in acquiring shows with similar demand metrics within a specific genre or adapt similar content formulas to existing shows.
A common use of demand data that can benefit content distributors, producers, and programmers is understanding viewers and their preferences, enabling the development of more relevant and engaging content. Questions such as "What genres are resonating most with my current audience?" can be answered through demand analysis. In reference to "Lessons in Chemistry" demand surge in Germany in February 2024, executives may want to learn more about the factors - such as the platform, pre-release marketing, or word of mouth - that result in such spikes in demand or adjust the scheduling or release strategy to capitalize on such surges in demand.
For content programmers or distributors, by answering questions such as "What type(s) of content exhibit opportunity to acquire or produce?" demand data can facilitate identifying and selecting the right content for specific audiences. For instance, fans of "Lessons in Chemistry" also like other popular shows such as "For All Mankind," "Fargo," "The Gilded Age," and "Foundation." By analyzing the demand data of these content and identifying the common characteristics that attract audiences, executives can build watch lists and optimize content discovery and recommendations to increase user engagement.
Finally, understanding demand data can also help executives understand the shows' global performance, potential for global expansion, and franchise expansion possibilities. "Lessons in Chemistry" has outstanding travelability and franchisability, indicating that the viewers' global appeal as well as IP value for potential spin-offs or franchises. By quantifying global audience demand data, executives can choose which platforms to target for distribution, plan and adjust marketing campaigns for the upcoming season, and assess the potential for spin-offs or franchises before incurring significant production expenditures.
In summary, demand data is a crucial tool for any entertainment executive working in content production, distribution, or acquisition, helping them make more informed and data-driven decisions concerning audiences, content, growth, and ROI. "Lessons in Chemistry," as productive wealth of demand data, serves as an excellent example of how demand data can be applied to inform decisions in the entertainment industry.
