Diesel Labs

What this data set is:

A ranked list of 200 top musicians by cross-platform engagement with and without video views. Daily engagement volumes are provided per artist for the week of recorded engagement and can be trended.

Data Detail: The music product contains a subset of notable and chart-topping artists receiving significant levels of audience attention during the engagement period. Diesel Labs’s Music library is updated on a regular basis.

Engagements are defined and collected based on specified Diesel Labs methodology for this particular analysis. If you require a custom methodology, please reach out to the Diesel Labs team.

Methodology: Diesel Labs is a data curation company that specializes in deriving insights from the major social and video platforms to help researchers and analysts understand what topics are the most important and why. This report includes data from Instagram, Twitter (X), YouTube, and Wikipedia, across all relevant engagement types (video views, page views, likes, shares and comments).

Application guidance / ideas: This report can be used in conjunction with other forms of data (streams, weeks on top charts, sales numbers) to provide a holistic look at how an artist’s music is capturing audience attention. For example, it may be expected that a top-selling artist is highly engaged with, but there may be more opportunity to run campaigns and reach new listeners if that artist falls to the bottom of the social engagement rankings list. Understanding how artists stack up in terms of engagement can also assist in understanding the music landscape and what may be driving or pulling attention from one artist to another. This can be supported by qualitative conversation summaries (reach out to Diesel for more) About Diesel Labs: Diesel Labs measures audience engagement with content across all major social and video platforms - curating, organizing and analyzing the vast unstructured data and readying it for seamless integration into business intelligence tools, reporting and even generative AI models.