ZN1BERMED1A, a startup dedicated to creating programming tools for the entertainment industry, has unveiled MusicDatak, an algorithm-based music research tool for brands and radio stations. MusicDatak is an automated process that detects hits, verifies information, organizes and prioritizes hits based on how happy or tired a specific audience is at a specific location. MusicDatak targets radio stations, retail brands and music curators.
The music profile on Radio Disney is feel-good hits and is specifically curated for the target audience. The collaboration with French startup MusicDatak ensures a genuinely insight-driven repertoire. MusicDatak analyses the most streamed music across all major digital platforms in Sweden, in real time, including for example TikTok, Spotify, Apple Music, Google Music, YouTube and Shazam.
The music selection is based in part on research conducted by French start-up Musicdatak, which analyses which music is popular on Tik Tok, Spotify, Apple Music, Google Music, Youtube and Shazaam.
Working for many national groups that own multiple brands, I saw that, for budgetary reasons, they were relying less on research and more on benchmarking with competitors. This resulted in the stations not differentiating themselves and not paying attention to many of their listeners, particularly the early adopters or trendsetters. MusikDatak detects hits and verifies the information. It also organizes and prioritizes the popular tracks based on how much a specific audience in a specific location is listening.
MusicDatak presents itself as "an automated process that detects hits, verifies the information, organizes and prioritizes the hits according to the degree of satisfaction of a specific audience at a specific location". A tool based on algorithms and intended for radio stations that want to take care of and improve their music programming.
The company describes MusicDatak as an automated solution that detects hits, verifies information, organizes and prioritizes hits according to the degree of audience satisfaction or fatigue in a precise location. It uses data sources from music platforms, radio airplay and social media music charts. The tool aims to help radio stations make better-informed decisions about their music categories in GSelector and MusicMaster scheduling systems.