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 a TV executive, analyzing audience demand data can provide insights that would enable you to make informed decisions regarding your content valuation. Demand data can be used for content acquisition, distribution, programming, and marketing decisions. In this article, we will use the show "Good Girls" as an example to demonstrate how demand data can be leveraged to make strategic decisions.
The demand data for Good Girls shows that the show achieved 7.5 times the audience demand of the average show in the United States for the last 30 days, which is a good performance. Audience demand for the show has been decreasing, however, compared to its demand of 7.7 times the average over the last 90 days.
Looking at the ranking of the show, we see that it peaked at #1019 in the United States and moved upwards by 125 spots during February 2024. The audience demand for the show over the past month has increased by 3.5%, with a significant spike in the middle of the month.
To gain a deeper understanding of the audience's preferences for "Good Girls", an affinity analysis was conducted. Fans of the show were found to also enjoy "Last Man Standing", "The Rookie", "Dr. Pimple Popper", "The Blacklist", "The Russell Howard Hour", "This Is Us", "Foundation", "Rick And Morty", and "The Bachelorette".
Furthermore, looking at the demand data for "Good Girls" on a global scale, we see that it outperforms the demand for the average TV show in each of the top 10 global markets where it is most in-demand. The show's number one market globally is the United States, followed by South Africa. Its performance in other markets such as Germany, Canada, Spain, Netherlands, Mexico, United Kingdom, and Kenya, was also good or okay.
Using content valuation based on audience demand data, executives can determine the value of a TV series to any platform in any region and how much to spend on content. With "Good Girls" as an example, content valuation answers questions such as: how much is the show worth to another streaming platform or linear network, how can its exclusivity be assessed, how well does the show perform as an acquisition-driving or churn-reducing title, and what is the baseline value of the title?
The momentum, travelability, longevity, franchisability, and reach of a show can also be assessed using demand data. In the case of "Good Girls," we see that its longevity is outstanding, while its momentum, travelability, and franchisability are okay or good. Its reach is noteworthy, with an outstanding number of people expressing demand for the show.
Demand data is also useful in decisions concerning supply and demand, optimal release strategy and pricing, subscriber retention, acquisition, and discovery and recommendations. TV executives can use demand data to answer a broad range of questions such as: what type of content exhibits a chance to acquire or produce, which titles should be acquired to grow or keep the current audience happy or grow the audience, what are the most popular genres in the local market, what is the best release strategy, how to find new opportunity markets for content distribution, and what is the best way to optimize a marketing campaign ahead of the premiere?
In conclusion, understanding audience demand data can give TV executives a significant edge in developing content that is popular with their targeted audience. Demand data enables them to make more informed content, acquisition, distribution, programming, and marketing decisions. By analyzing demand data for "Good Girls," we have shown how content valuation can be determined, and how demand data can be used to assess a show's momentum, travelability, longevity, franchisability, and reach.
This is Good Girls'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 Good Girls travels internationally.
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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 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 Good Girls, for example, can be assessed for a market e.g. United States, which can then be benchmarked against genre averages (e.g. Crime Drama), using our globally standardized Demand Expressions® metric.
This page has been reconstructed on March 28, 2024, 11:13 p.m. PST using global television demand data from Parrot Analytics for Good Girls in the United States. We provide our partners with smart SVOD workflows to help them drive better and more informed content licensing strategies. Contact us today to find out more.