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'LG Electronics' Launches Dedicated 'CJ ENM K-Dance' Channel on North American FAST Platform

Launched Oct. 27 on LG Channels in the U.S. and Canada, with a lineup focused on key franchises including 'Street Woman Fighter'

CJ ENM's 'K-Dance' channel on LG Channels [Provided by CJ ENM. No resale or database use]
CJ ENM's 'K-Dance' channel on LG Channels [Provided by CJ ENM. No resale or database use]

Securing a foothold on the global FAST platform and expanding the reach of K-content

Global entertainment company 'CJ ENM' is opening up a new avenue for K-content, targeting North American viewers. On the 27th, 'CJ ENM' announced the official launch of 'CJ ENM K-Dance,' a dedicated K-dance channel in the United States and Canada on 'LG Electronics' ad-supported free streaming TV service, 'FAST,' via LG Channels.

The newly launched 'CJ ENM K-Dance' channel takes an in-depth look at the K-pop choreography and dancers captivating audiences around the world. Its diverse lineup of premium content, available free around the clock, includes Mnet's mega-hit dance competition shows 'Street Woman Fighter' and 'Street Man Fighter'; 'Somebody,' a variety show combining dance and romance; and legendary performances from the 'MAMA Awards,' the world's largest K-pop awards show.

The 'FAST' service, which is growing rapidly in North America, is gaining attention as a next-generation media platform because it lets viewers enjoy high-quality content by watching ads, without paying a subscription fee. Through its partnership with 'LG Electronics,' which has built an extensive smart TV user base across North America, 'CJ ENM' aims to significantly improve access for local viewers and further strengthen the global fandom for K-dance.

Industry experts say the launch of the dedicated channel is more than a simple content export, calling it an effective example of IP (intellectual property) use aligned with global media trends. 'CJ ENM' is expected to continue expanding its tailored K-content lineup for the North American market by analyzing local viewers' data and viewing patterns.

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