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, one can make more informed content decisions by utilizing demand data for TV shows. Let's consider the TV series "Ke Apon Ke Por" and how its demand data can be used to make more informed decisions.
Content Valuation:
Content valuation is important for any TV executive, and "Ke Apon Ke Por" provides an excellent example of how demand data can be used for content valuation. The demand data for this show reveals that it has excellent demand in India. In the last 30 days, its demand was 2.3 times the average show in India, indicating that it is a highly-valued show. By calculating the show's dollar value contribution to the platform through Parrot Analytics' content valuation system, one can determine how much to spend on the show and its potential dollar value contribution.
Programming Decisions:
Programming decisions require an understanding of viewers' preferences and how content can be optimized for success. By looking at the demand data of "Ke Apon Ke Por," one can understand the show's most popular genre and which elements are most liked by the show's fans. Its high demand in India reveals that the show has resonated well with audiences in this region. The show's spike in demand in late March 2024 also suggests that certain events in the show may have captivated audiences. This might inform programming decisions, such as airing more episodes that satisfy the audiences' interest.
Distribution Decisions:
Having information about a show's international demand relative to its home market can be vital for making informed distribution decisions. "Ke Apon Ke Por" has poor travelability, meaning it has low international demand relative to its home market in India. This may inform decisions such as avoiding spending too much on exporting the show for theatrical release. At the same time, its worldwide audience demand and performance across all platforms indicate that there may be potential opportunities for the show globally.
Acquisition Decisions:
Acquisition decisions require identifying which types of content exhibit opportunities to produce or acquire. While "Ke Apon Ke Por" has good demand in India, it has poor franchisability and reach. This may inform acquisition decisions, such as avoiding investing in spin-offs or expanding the scope of the storyline. However, it is worth noting that the show's demand data reveals increased popularity, which may warrant acquiring the show for future distribution.
Subscriber Acquisition and Retention:
For an OTT platform or SVOD platform, acquiring and retaining subscribers is vital. The demand data for "Ke Apon Ke Por" can help with subscriber acquisition and retention. By looking at the show's most popular markets, one can optimize the distribution strategy for the show. The show's high demand in India is an example of a show that can attract new subscribers to an SVOD or OTT platform. At the same time, the show's drop in rank during March 2024 may suggest that certain decisions that led to the decline needed to be reviewed to retain and possibly attract new subscribers.
In conclusion, utilizing demand data for TV shows like "Ke Apon Ke Por" can inform strategic content, distribution, acquisition, and programming decisions for entertainment executives. Demand data can assist with determining how much to spend on a show, identifying which elements and genres are most popular, understanding the international demand relative to its home market, and enhancing subscriber acquisition and retention strategy.
This is Ke Apon Ke Por'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 Ke Apon Ke Por travels internationally.
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Our TV audience measurement dataset is truly global. Discover how popular Star Jalsha‘s Ke Apon Ke Por 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 India, 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 Star Jalsha 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 Ke Apon Ke Por, for example, can be assessed for a market e.g. India, which can then be benchmarked against genre averages (e.g. Romantic Drama), using our globally standardized Demand Expressions® metric.
This page has been revised on April 22, 2024, 2:03 p.m. PST using global demand datasets from Parrot Analytics for Ke Apon Ke Por in India. We provide our partners with smart SVOD know-how to help them drive better and more informed content sales strategies. Contact us today to find out more.