The Demand Distribution curve illustrates how a TV show’s popularity compares to the demand benchmark, which is a measure of the average demand across all titles. The curve is divided into performance buckets, ranging from “Below Average” to “Exceptional”. A show falls into one of these performance buckets depending on how many times more or less demand it has compared to the demand benchmark.
The show’s performance is market-specific, e.g. the same show can be in the “Average” range in the United States and in the “Good” range in France.
As an example, if a show has 9 times more demand than the demand benchmark, it falls in the “Outstanding” performance range; only 2.7% of all shows in the market reach this high level of demand.
Are you an entertainment executive looking for ways to make more informed content decisions, acquisition decisions, or distribution decisions? Parrot Analytics' demand data provides a solution that can help you. Let's explore a case study that involves the show "Blindspotting" and discover how you can use demand data to help inform your decision-making process.
Do you want to assess the quality of the content? Well, the demand for a show is an excellent indicator. "Blindspotting" has a demand that is 2.6 times the audience demand of the average show in the United States for the last 30 days. The show belongs to a group of only 8.6% of all TV shows that achieve this level of demand. The data indicates a positive performance of the show. However, it is necessary to keep an eye on the recent demand trend that shows a decreasing demand for this show.
Content valuation is a critical driver of the entertainment industry. The Parrot Analytics content valuation system can use the demand data to determine the value of a show. You can answer questions such as how much a title or library is worth to any streaming platform and how to estimate the cannibalization of audiences if a title is available on multiple services. Furthermore, you can calculate the ROI of specific titles or assess a TV series' ability to drive subscriptions, mitigate churn and improve viewer engagement. For "Blindspotting." you can use the demand data to determine its content value and enrich your content acquisition decisions.
Similarly, you can optimize content for different markets. Suppose you are keen on global expansion, in that case, the demand data for "Blindspotting" can help you understand the show's travelability, the extent to which it can resonate globally, and create a strategy to maximize global reach. In the past 30 days in the United States, audience demand for the show has decreased by 3.3%. Still, demand spiked in late October 2023, indicating promising times ahead. You can look at the other top ten markets where the show is most in-demand to fine-tune your strategy for content distribution.
The demand data can also inform expert programming decisions. "Blindspotting" ranks convincingly at the 88.7th percentile in the drama genre in October 2023, indicating that it has higher demand than 88.7% of all drama titles in the United States. You can use this information to optimally program the show to multiple platforms.
Finally, you can analyze the fans' preference by using the show's affinity data. You can gain insights like which elements of existing content do fans like the most, what shows or genres are resonating most with your current audience, and what partnership with talent or brands could expand the audience. Additionally, you can get data for future decisions on programming, producing, or partnering with pinpoint accuracy. Parrot Analytics identified ten other shows that fans of "Blindspotting" like, including "Ruthless," "MasterChef (US)," and "Foundation." The insights from the affinity analysis can inform potential partnerships for franchise spin-offs.
In conclusion, Parrot Analytics' demand data provides a valuable tool for entertainment executives to make more informed content decisions, acquisition decisions, or distribution decisions. With the help of the case study of "Blindspotting," it becomes clear how you can utilize Parrot Analytics demand data to develop, program, price, and distribute content in a more informed manner.
This is Blindspotting's home market and therefore its travelability is 100%; the travelability of a TV show's market of origin is always 100%. Click through to another market to discover how well Blindspotting travels internationally.
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Our TV audience measurement dataset is truly global. Discover how popular STARZ‘s Blindspotting is in any of the following selected markets. Need data for other markets? Find out more about DEMAND360LITE.
Parrot Analytics is the leading global content demand analytics company for the multi-platform business of television. With the world’s largest TV audience demand dataset, the company currently tracks more than 1.5B daily expressions of demand in 100 languages, not only in the United States, but also in 100+ other markets around the world to reveal the television content that consumers watch (viewership) and give their attention to the most.
Parrot Analytics' television demand data highlights the global TV content monetization opportunities for STARZ and thousands of TV studios, linear networks, broadcasters, pay TV providers and OTT / SVOD platforms.
Unlike TV ratings, our DemandRank TV rating system ensures that important demand signals are weighted more heavily than others: The more consumer effort required; the more importance is attached to each signal. Once all the signals are weighted and combined, the audience demand for Blindspotting, for example, can be assessed for a market e.g. United States, which can then be benchmarked against genre averages (e.g. Comedy Drama), using our globally standardized Demand Expressions® metric.
This page has been reconstructed on March 29, 2024, 8:34 a.m. PST using television demand content from Parrot Analytics for Blindspotting in the United States. We provide our partners with deep entertainment industry analyses to help them drive better and more informed content sales strategies. Contact us today to find out more.