sync with data
case study:
sync with data focuses on analyzing the relationship between specific music genres and TV show genres to identify the most commonly used songs in sync licensing.
the goal is to create a dashboard that provides insights for sync licensing coordinators and music supervisors to make data-driven decisions on music placement
introduction
as my senior capstone project, i chose to combine my passion for data and my experience in sync licensing to create this project! i analyzed the relationship between music genres and TV show genres in sync licensing. by leveraging data analytics, the project identifies patterns in successful music placements and develops an algorithm to help music supervisors and artists make more informed decisions about sync opportunities. the ultimate goal is to create a recommendation platform that streamlines the music selection process for media productions while helping artists gain strategic exposure. this is a project that is still a work in progress, but this is what i've been up to!
research question
the question i want to answer is: what specific genres of music fit with specific genres of TV shows, and what are the most used songs in sync licensing?
importance of the project
- market trends: helps artists and labels understand music usage trends
- artist success: provides artists with better opportunities for successful placements
project details
research phase
- 1. gathered a list from this billboard article to start my search on the most popular used songs in the most popular TV shows.
- 2. compiled that into one big spreadsheet
- 3. collected the streaming numbers from luminate, organizing that into another big spreadsheet
data analysis phase
- 1. data visualization: created easy visualization graphs through tableau to better visualize the trends of each of the songs
- 2. trend analysis: analyzed streaming success of songs after sync sync placements to quantify the impact
- 3. heat map creation: developed a heat map showing the distribution of streaming on-demand audio across different genres
- 4. algorithm development: used the heat map data to create a correlation algorithm through python that identifies relationships between music and TV show genres
challenges faced
- data acquisition:initially struggled to find comprehensive data but solved this by using luminate
- data organization: overcame the challenge of organizing large datasets through excel techniques
- historical data limitations: noted limitations in streaming data for songs from the 70s and 80s
- algorithm development: successfully created a correlation algorithm despite initial complexity
next steps
- research analysis: present findings and their implications on the music and entertainment industries
- prototyping: design and test the platform with users to identify areas for improvement
- development: potentially build a fully functional website to serve as a go-to resource for sync licensing professionals
conclusion
although this is still an "in the works" project, i am excited to continue working on it and hope to present it to the world one day! i've definitely been challenged in working with all this data, but it's been a great learning experience that has helped me grow. by identifying patterns in successful sync placements and developing algorithmic recommendations, the project creates value for multiple stakeholders while potentially improving the quality and efficiency of music selection for media productions. the approach combines technical data analysis with creative industry knowledge, resulting in practical applications that could significantly impact how music is selected for TV shows. as the platform develops further, i hope it has the potential to become an essential tool for music supervisors and a strategic resource for artists seeking sync opportunities.
