You’ve probably seen a lot of handwringing in the press from YouTube about the Google-subsidized division isn’t profitable. Aside from the fact that YouTube has experienced yet another self-created public relations disaster with Zoë Keating that will no doubt serve up the company’s SXSW panels like chopped beef, why would YouTube be telling the world how unprofitable they are? Kind of an odd thing for Google to opt for the “poor me” treatment which no one–and I mean no one–believes, right? Why would Google be putting out that story?
Maybe as an excuse for why their royalty rates are so low? But is it even true? If you look at YouTube as a video service, maybe Google can cook the books to show whatever they want. Big advertisers don’t typically sign up for a YouTube-only campaign. The media buy is across all Google platforms. Google can allocate that money to each platform how they like, so they can cause YouTube to look unprofitable. Not saying this is in fact happening, but easily it could be.
But maybe it’s because we are not looking at YouTube as the business that Google is really in: data mining and user profiling. Data mining revenue is not included in YouTube’s performance results and it’s also not included in artist royalties. As Jeff Gould observes in an illuminating post:
Beginning in 2003 and continuing to the present, Google has filed a range of patents describing the use of various kinds of user profiles to improve both ad response rates and — perhaps even more significantly — the quality of search results….Knowing your customer is the key to successful selling. While you might imagine that Upper Crust or Young Digerati audiences are inherently more desirable than Rustic Elders or Hard Scrabble, this is not always the case. What may be true for BMW or Apple is not necessarily true for KFC or Coke.
How does Google’s online profiling work? At its core are…patented PHIL clustering and concept extraction methods…. A user (or group of users) can be described by various kinds of clusters. The simplest kind are clusters of terms used in documents created or viewed by the user. Another kind derives from the URLs of documents the user has viewed or perhaps forwarded to others by email or social media. A third kind — the most comprehensive — consists of the concept or category clusters extracted by the PHIL algorithm from documents the user has viewed (web pages, inbound emails) or created (outbound emails, social media posts)….
Retaining and satisfying search users is without doubt Google’s most important business objective, because search continues to account for the lion’s share of its revenues and virtually all of its profit. [And remember, YouTube itself is one of the top 5 search engines.] Consequently, data mining methods that optimize the relevance of search results for users are of great strategic value to Google….Google has operationalized the techniques developed by its researchers for correlating real-world user attributes with features extracted from their online actions. The power of this method is obvious.
For example, Google can now tell its advertisers very precisely that “about 14% of users on the Internet are Moms with children in the household”. But more than that, it can tell them exactly which users are Moms with kids.
Think YouTube helps with identifying those Mom’s and kids?
So if YouTube is viewed as a data mining honeypot, now is it profitable?