US Recorded Music Revenues by Format

US Recorded Music Revenues by Format in 2019 (Inflation Adjusted)

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information about the US recorded music industry in 2019 Week 10. It includes details on the various formats of recorded music, such as CDs, vinyl records, digital downloads, and more. The dataset also provides data on the respective years in which these records were made, allowing for accurate historical comparison and analysis.

Key metrics provided include the number of units sold for each format, as well as corresponding revenue generated from their sales. In addition to the raw revenue figures, this dataset offers an extra column that presents inflation-adjusted revenue values. These adjusted figures take into account changes in purchasing power over time and enable a fair comparison of different years' revenues.

Overall, this dataset offers valuable insights into the US recorded music industry's performance in terms of format popularity and economic gains throughout a specific week in 2019. Researchers, analysts, and music professionals can utilize this comprehensive dataset to explore trends within specific formats while considering both absolute revenue and inflation-adjusted figures

How to use the dataset

Introduction:

• Understanding the Columns: a) Format: This column categorizes the format of the recorded music, such as CD, vinyl, digital download, etc. b) Year: This column represents the year in which the data was recorded. c) Units: The number of units sold for a particular format of recorded music. d) Revenue: The revenue generated from sales for a specific format. e) Revenue (Inflation Adjusted): The column that shows revenue adjusted for inflation.

• Analyzing Formats: By exploring and analyzing the Format column in this dataset, you can gain insights into changing consumer preferences over time. You can identify which formats have gained popularity or declined over different years or periods.

• Understanding Revenue Generation: To understand revenue patterns in relation to various formats and years, analyze both Revenue and Revenue (Inflation Adjusted) columns separately. Comparing these two columns will help you assess changes due to inflation accurately.

• Exploring Units Sold: The column Units provides insight into how many units were sold for each format within a specific year or period. Analyzing this data helps understand consumer demand across various formats.

• Calculating Inflation-Adjusted Revenue: Utilize the Revenue (Inflation Adjusted) column when analyzing long-term trends or comparisons across different periods without worrying about how inflation affects purchasing power over time.

• Comparing Multiple Years or Periods: This dataset includes information specifically for 2019 Week 10. However, you can use this dataset in conjunction with other datasets covering different years to compare revenue, units sold, and format performance across multiple years.

• Creating Visualizations: Visualizations such as line charts or bar graphs can help represent patterns and trends more comprehensively. Consider creating visualizations based on formats over multiple years or comparing revenue generated by different formats.

• Deriving Insights: Make use of the information provided to identify trends, understand customer preferences, and make informed decisions related to marketing strategies or product offerings in the music industry.

Conclusion:

Research Ideas

• Analyzing the impact of different music formats on revenue: This dataset provides information on the revenue and units sold for different recorded music formats such as CDs, vinyl, and digital downloads. By analyzing this data, one can identify which format generates the highest revenue and understand how consumer preferences have shifted over time.
• Tracking changes in purchasing power over time: The dataset includes both revenue and inflation-adjusted revenue figures, allowing for a comparison of how purchasing power has changed over the years. This can be useful in understanding trends in consumer spending habits or evaluating the success of marketing campaigns.
• Assessing market performance by year: With data on both units sold and revenue by year, this dataset can be used to assess the overall performance of the US recorded music industry over time. By comparing different years, one can identify periods of growth or decline and gain insights into factors driving these changes, such as technological advancements or shifts in consumer behavior

Acknowledgements

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MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market.
Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

• Genre Networks: Success-based genre collaboration networks
• Genre Mapping: Genre mapping from Spotify genres to super-genres
• Artist Networks: Success-based artist collaboration networks
• Artists: Some artist data
• Hit Songs: Hit Song data and features
• Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20,
 title = {Detecting Collaboration Profiles in Success-based Music Genre Networks},
 author = {Gabriel P. Oliveira and 
      Mariana O. Silva and 
      Danilo B. Seufitelli and 
      Anisio Lacerda and
      Mirella M. Moro},
 booktitle = {21st International Society for Music Information Retrieval Conference}
 pages = {726--732},
 year = {2020}
}

In 2024, physical music sales generated *** billion U.S. dollars, whereas digital music sales made *** billion worldwide. The majority of global music revenue now comes from streaming, and accounted for more than ** billion U.S. dollars in total industry revenue in 2024.

This table contains 6 series, with data for years 1998 - 2003 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Musical category (6 items: Popular and rock music;Classical music;Jazz and blues;Country and folk music; ...).

This table contains 6 series, with data for years 1998 - 2003 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Musical category (6 items: Popular and rock music;Classical music;Jazz and blues;Country and folk music; ...).

This table contains 76 series, with data for years 2005 - 2011 (not all combinations necessarily have data for all years), and was last released on 2015-08-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada; Ontario; Quebec; Atlantic provinces ...), North American Industry Classification System (NAICS) (4 items: Record production and integrated record production/distribution; Sound recording studios; Other sound recording industries; Music publishers ...), Summary statistics (4 items: Operating revenue; Operating expenses; Operating profit margin; Salaries; wages and benefits ...).

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of record production and integrated record production/distribution (NAICS 512210 & 512220), music publishers (NAICS 512230), sound recording studios (NAICS 512240), and other sound recording industries (NAICS 512290), annual, for five years of data.

Analysis of ‘Lata Mangeshkar's Songs on Spotify’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rprkh15/lata-mangeshkar-songs-on-spotify on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Lata Mangeshkar was an Indian playback singer and occasional music composer. She is widely considered to have been one of the greatest and most influential singers in India. Her contribution to the Indian music industry in a career spanning seven decades gained her honorific titles such as the Nightingale of India, Voice of the Millennium and Queen of Melody.

Content

• Id: Unique value for each song
• Name: Name of the song
• Album: Name of the album
• Release Date: Release Date of the song
• Length (ms): Length of the song in milli-seconds
• Acousticness: This value describes how acoustic a song is. A score of 1.0 means the song is most likely to be an acoustic one.
• Danceability: Danceability is measured using a mixture of song features such as beat strength, tempo stability, and overall tempo. The value returned determines the ease with which a person could dance to a song over the course of the whole song.
• Energy: Energy is the sense of forward motion in music, whatever keeps the listener engaged and listening. In loud music, musical energy is easy to identify. We notice the energy more as the drums get busier and play louder, and as the singer sings higher.
• Instrumentalness: This value represents the amount of vocals in the song. The closer it is to 1.0, the more instrumental the song is.
• Valence: Describes the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
• Liveness: This value describes the probability that the song was recorded with a live audience. A value above 0.8 provides strong likelihood that the track is live
• Loudness: Loudness is a way to measure audio levels.
• Speechiness: Speechiness detects the presence of spoken words in a track. If the speechiness of a song is above 0.66, it is probably made of spoken words, a score between 0.33 and 0.66 is a song that may contain both music and words, and a score below 0.33 means the song does not have any speech.
• Tempo: Tempo is how fast or slow a piece of music is performed. Tempo generally is measured as the number of beats per minute, where the beat is the basic measure of time in music.
• Time Signature: The time signature indicates how many counts are in each measure and which type of note will receive one count. The top number is commonly 2, 3, 4, or 6. The bottom number is either 4 or 8.
• Popularity: Describes how popular the song is.

--- Original source retains full ownership of the source dataset ---

Techsalerator’s Location Sentiment Data for Vanuatu

Techsalerator’s Location Sentiment Data for Vanuatu provides a detailed collection of data, offering crucial insights for businesses, researchers, and technology developers. This dataset delivers a comprehensive analysis of public sentiment and environmental conditions across different regions of Vanuatu, helping to understand local opinions, behaviors, and perceptions.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

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• Sentiment by Demographics (Age, Gender, Socio-Economic Status)
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• Public Sentiment toward Key Industries (Tourism, Agriculture, Technology, etc.)
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Techsalerator’s dataset is an invaluable resource for businesses, governments, and researchers seeking to understand public sentiment in Vanuatu. It provides actionable insights for decision-making, policy development, and market strategies.

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of record production and integrated record production/distribution (NAICS 512210 & 512220), music publishers (NAICS 512230), sound recording studios (NAICS 512240), and other sound recording industries (NAICS 512290), annual, for five years of data.

Determining the causal factors influencing college students’ willingness to consume digital music in China is crucial given the rapid integration of digital technology with the traditional music industry. Chengdu, as a rapidly developing city in China with a thriving youth culture and a significant presence of higher education institutions, provides an ideal setting to explore the factors influencing college students’ willingness to consume digital music. Using a mixed-method approach and a sample of 431 college students from various universities in Chengdu, this research examines the impact of perceived value, behavioral attitude, subjective norms, user participation, user stickiness, and psychological needs on digital music consumption intention. The empirical results show that these factors jointly affect consumption intention, with user stickiness and psychological needs serving as mediators. Specifically, perceived value, behavioral attitude, subjective norms, and user participation have a significant positive impact on consumption intention. These findings provide valuable insights for digital music platforms and the music industry to develop targeted marketing strategies and services tailored to the demands and behaviors of college students in Chengdu.

Estimates suggest that Apple Music had 95 million subscribers worldwide in June 2024, up by 2 million from the previous year. Launched in 2015 by U.S. tech giant Apple, Apple Music is the second largest music streaming service worldwide, competing with market leader Spotify. Spotify remains market leader While Apple Music is a popular music streaming platform, accounting for 12.6 percent of subscribers worldwide, the 2008 founded streaming service Spotify remains the market leader with a subscriber share of nearly 32 percent. Financially this meant that the Swedish company generated a global revenue of 3.7 billion euros through its Premium accounts in the fourth quarter of 2024 alone.Music streaming overall increasesOverall, music streaming has experienced significant growth over the last decade. Even if the annual growth rate is gradually declining, it still stood at over 7 percent in 2024, becoming the music industry’s main revenue driver and reaching a revenue of 20 billion U.S. dollars worldwide in 2024.

In 2024, it cost an average of ****** U.S. dollars to see an artist live in concert. This marks a substantial increase from 2015, when a gig ticket cost just over ** dollars. However, as these prices are averages, they do not necessarily reflect additional costs, which can vary wildly according to venue, seating type and the artist themselves. For some of the world’s biggest stars, concertgoers across the world can expect to may significantly more than the global average. Another factor which can influence ticket prices is the seller. Online ticket resale marketplaces such as ViaGoGo have often come under fire for advertising tickets at vastly inflated prices, and a proliferation of unofficial sellers means that buyers are often charged more than the tickets were worth in the first place. The leading music promoter worldwide in 2024 was Live Nation, which sold a total of over ** million tickets worldwide. Other large music promoters include Eventim Live and AEG Presents. Live music: an expensive but addictive hobbyDespite rising ticket prices, consumers with a taste for seeing their favorite artists play live appear relatively unfazed by cost. In 2024, the number of music tour tickets sold worldwide amounted to **** million, representing a slight decrease of **** percent compared to 2023, when **** million tickets were sold. These fluctuations have been ongoing since 2011 and appear to be a normal aspect of the industry. Naturally, the number of tickets sold depends on a variety of factors, including which artists or bands are playing that year, the type of tour an entertainer is offering (e.g. a greatest hits tour versus an album tour) and the location of the gig itself. Many artists have achieved fame and popularity on a global level, with their tours generating hundreds of millions of dollars. In 2024, the most successful music tour worldwide based on gross revenue was Taylor Swift's Eras Tour, which grossed over **** billion U.S. dollars, making it the first tour to achieve this milestone twice.

On October 22, 2024, 'APT.' by ROSÉ and Bruno Mars was the most-streamed track on Spotify with 14.6 million streams worldwide, followed by 'Die With A Smile" by Lady Gaga and Bruno Mars, reaching over 11 million Spotify streams on Spotify that day. Billie Eilish's 'BIRDS OF A FEATHER' came third with just over 7.6 million streams. How do music artists get so many streams on Spotify? Firstly, Spotify is one of the most successful and popular music streaming services in the United States, and as of the first half of 2018 had the biggest share of music streaming subscribers in the world. With Spotify’s vast audience, featuring on the platform is a good start for emerging and popular artists hoping to make an impact. Secondly, there is no exact science to ‘going viral’. From the famous egg photo on Instagram posted in early 2019 to wildly successful music video ‘Gangnam Style’ released back in 2012, viral content comes in all shapes and sizes. Purposeful viral marketing is one way in which something could go viral, and is one of the reasons why some songs have so many streams in a short space of time. This type of marketing involves a tactical approach and pre-planning in an attempt to push the content into the public eye and encourage it to spread as quickly as possible. However, many artists who go viral do not expect to. Accessible, catchy content created by an already popular artist is already poised to do well, i.e. the latest song or album from U.S. singer Drake. This is an example of incidental viral marketing, when content spreads by itself partially as a result of an established and engaged audience. Indeed, Spotify’s most-streamed tracks generally originate from a well-known figure with a large following. But for smaller or entirely unknown content creators, going viral or experiencing their 15 minutes of fame can simply be a case of posting the right thing at the right time.

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.