PLAYLIST MEDIA

Playlist, already a top iOS app, is uniting people, music, and messaging. Their fully licensed, ad-supported music streaming platform generates 10 times the user engagement of Pandora. On mobile, people spend more time listening to music than any other application and the message most frequently. Playlist combines music and messaging for unprecedented engagement. On Playlist, people listen together at the exact same time and chat while they listen, follow one other, collaborate on playlists, and... share their favorite songs. Playlist pioneered social music. Playlist is a proven platform for artists and influencers to connect live with fans, hold virtual concerts, and release new music to millions of listeners at the same time. Our community describes Playlist as Spotify plus Instagram and now Tinder, given our introduction of Music Match which connects people with similar music tastes. Three years in the making, Playlist’s patent-pending technology includes synchronized streaming, machine learning-based social recommendations, collaborative playlists, large-scale group chat, behavior-based music programming, and Music Match algorithms. The company is now working on blockchain. Founders, Karen Katz, and Steve Petersen are Stanford engineers and serial entrepreneurs with 2 IPOs, a $300M exit, 36 patents, and over 10 years’ experience each in digital music. They’ve raised over $60M in VC funding across multiple start-ups and built ad-supported, consumer-facing platforms with tens of millions of users, including one of the fastest-growing streaming music services in U.S. history. Their experience also includes developing the software and chip inside the iPod, which revolutionized digital music and mobile listening. As operators, they’ve recruited and managed tech teams of over 75 engineers and generated nearly $100M in digital ad revenues. As innovators, they’ve worked at Bell Labs, architected platforms for social networking, location-based services, cloud-based media; and applied advanced analytics, machine learning, and big data for recommendations.More