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.
As an entertainment executive working in the TV industry, you are always looking for ways to make more informed decisions in your content strategy. One way to achieve this is through the use of demand data. Demand data gives you valuable insights into how TV shows and genres are performing in specific markets and globally. Based on the analytics for the TV show "Chicago Fire" in Spain during March 2024, we can provide some examples of how demand data can be applied.
Firstly, content valuation uses demand data to answer questions such as: "How much is my series worth?" and "What is the baseline value of a title?" The analysis shows that "Chicago Fire" achieved 12.9 times the audience demand of the average show in Spain during March 2024. This suggests that it has strong value in the Spanish market, which can be used as a baseline for content valuation. Additionally, demand for the show is increasing, which could be an indicator of future value growth.
Demand data also powers content valuation by informing scalable concept testing. For instance, when assessing what kind of content to produce or acquire, you can rely on demand data to identify opportunities in different genres and markets. The analysis shows that "Chicago Fire" is in the 99.7th percentile for the drama genre in Spain, outranking 99.7% of all drama titles. This data could be used to inform the decision to invest in Spanish drama content or acquisition.
Moreover, demand helps with programming decisions, specifically in terms of subscriber retention and acquisition. For example, if you want to keep your current audience happy, you need to know which titles to buy. Based on the analysis of "Chicago Fire," popular shows that fans also like include "Chicago Med", "NCIS", and "The Rookie". This data can be useful for identifying the best titles to retain subscribers. However, if you want to grow your audience, you need to acquire titles that attract new viewers. Therefore, by assessing which titles perform well in Spain for different genres, you can use the data to make decisions that will appeal to a broader audience.
Additionally, demand can inform distribution decisions, allowing you to determine the right platforms to target for specific content. For instance, the data shows that "Chicago Fire" has outstanding demand in several countries, including the United States, the United Kingdom, Canada, Australia, Brazil, and France. By examining these countries' performance, entertainment executives can gauge how the TV series is doing outside of its original market and plan strategies to expand to specific markets.
Finally, demand data provides insights that allow you to make more informed acquisition decisions. For example, knowing how much a TV show is worth to other streaming platforms will allow executives to make informed decisions about whether to acquire new content or not. The demand data for "Chicago Fire" can be used to show its value to other platforms, highlighting its outstanding demand in Spain and other countries.
In conclusion, demand data can provide entertainment executives with valuable insights into how TV shows and genres are performing in specific markets and globally, allowing them to make more informed content, acquisition, distribution, and programming decisions. Based on the analysis of "Chicago Fire," executives can rely on demand data to guide them in their content strategy, ensuring they remain competitive and appealing to their target audience.
Chicago Fire's travelability to Spain in the last 30 days is 39%, which means that the audience demand for Chicago Fire is 39% of the demand in its country of origin, United States. Click through to another market to discover how well Chicago Fire travels internationally.
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Our TV audience measurement dataset is truly global. Discover how popular NBC‘s Chicago Fire 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 Spain, 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 NBC 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 Chicago Fire, for example, can be assessed for a market e.g. Spain, which can then be benchmarked against genre averages (e.g. Action Drama), using our globally standardized Demand Expressions® metric.
This page has been altered on April 22, 2024, 2:01 p.m. PST using global tv demand datasets from Parrot Analytics for Chicago Fire in Spain. We provide our partners with far-reaching content expertise to help them drive better and more informed content acquisition strategies. Contact us today to find out more.