Despite the flurry of YouTube Multi-Channel Network M&A over the past 12 months, MCN skepticism continues to abound. Nearly every week, a client or journalist expresses incredulity at the either the price paid for an MCN or the implied value of YouTube’s user generated content. “Rupert’s interest in Time Warner makes sense: scale, a leading content library, HBO as a Netflix competitor. Starz is a high quality, cash-generating asset,” an executive at a leading US network emailed me last month (republished with permission), “And Disney is doubling down on IP: LucasArts, Marvel, live sports.... But I don’t see how Maker fits in.”
These responses stem from an underlying truth: the MCN/UGC (User Generated Content) model is fundamentally at odds with Hollywood’s own modus operandi:
- Production costs are extremely low, typically $500-$1,000/minute, compared to $100,000+ for TV and up to $1,500,000 for film
- There is limited oversight from non-creatives: little-to-no studio/network “notes”, audience testing, production audits etc.
- Content tends to be thought up, planned, produced and released in a matter of hours or couple of days – not months or years
- Not only do MCNs largely eschew Hollywood’s rigid windowing strategy, but the vast majority of consumption (and value) is front loaded
- Audiences are primarily built around people, not IP or brand (or even the former mixed with the latter)
- Hyper-specialized audiences are also an asset, not a reflection of programming failure
- Consumption is distributed across thousands of niche videos and storytellers, not “hits” (let alone blockbusters)
- MCNs appear to act as more of an ad exchange or technology provider than a content company
For those working deep inside Hollywood today, the MCN model may not be anathema, but it is confounding. How can content be reliably “good”? How can it ever be profitable? How can it scale? While certainly worthy of exploration, these questions overlook the critical – and often unique – advantages of the platform. But before getting into abstractions, we need to first consider their scale.