Tag Archives: digg

Content-centric Communities

Bernard Lunn, at Read/WriteWeb wrote an interesting article about the failure of Eons.

Eons is a social networking website aimed at the over-fifty crowd, headed by the founder of job-site Monster.com. After raising $32M, Eons is now cutting it’s workforce in half – not exactly a measure of success.

In his article, Lunn touches a point I’ve been making for a long time: people gather around content, not around demographic variables (see “The Advertiser’s Dilemma”, “Rethinking Ratings” and “Why Google Should Buy YouTube” for my previous articles on content-centric ratings analysis).

Lunn think the problem lies in Eons’ strategy to connect people around age, a traditional demographic variable, and not around content or common interests. He’s hit the nail squarely on the head:

“…people want to connect around content, not around age. Connecting around content is what Blogs do. You connect on something that interests you. (…) As you get older, you get a more varied set of interests and human relationships across all ages.”

Age/Sex/Location is not a social network

Demographic variables allow advertisers and their clients to easily target their products to artificial segments of the population that probably have very little else in common, other than age/sex/location. In a small-town-world these variables may have been good enough to create desirable advertising targets, but we now live in a connected world where people of all ages and genders interact and share common interests on a scale seldom seen before.

And while you can still use demographic variables to target your product, you’d be missing a much more interesting target, one capable of creating die-hard fans and viral awareness of your product, by ignoring content-centric connections.

As for social networks, look at the successful ones and the “glue” that keeps them together:

Building a social network around content will not magically make it successful, just like putting wings on a box won’t make it fly; but those wings sure help once you put the rest of the airplane together.

The Content-centric Connectivity Chart

The following chart is an example of how people of different ages, genders and cultural backgrounds gather around common interests (caveat: networks are not drawn to scale, connections do not attempt to imply actual traffic for these sites, and age/gender/race were limited by the avatar icons I could find on the net).

Content-Centric communities chart

The Content-centric Connectivity chart highlights two key ideas:

  • Successful networks are built around content, not around demographics.
  • There’s a huge opportunity for anyone who learns how to target their products around content-centric communities.

Conclusion

There will always be products that need to be targeted around demographic variables (e.g., feminine products, some toys, acne-medication, denture products), but the opportunities and tools for expanding your product’s appeal have never been this good.

Rethinking Ratings

Summary: An analysis of current television ratings methods, why they’re inappropriate for the timeless internet and digital video recorder era, and suggestions for improving them.

Traditional television ratings reports let TV executives and analysts study the behavior of a particular show or series, displaying the number of viewers each show had, broken down by demographic targets. This allows the television industry to determine which show won a particular time slot (e.g., Friday 9pm to 10pm), how it performed among a particular demographic (e.g., Males 18-34yrs) and how it has evolved (in the case of serials) over time (e.g., Are there more or less people watching it).

But what happens when viewers can watch any show at any time? When viewers don’t have to choose one show over another on a rival network? What happens when you can’t tell for sure who your viewers are?

The internet and TiVos give the viewer unprecedented freedom over when, where and what to watch. Soon it won’t be possible to tell for sure how many people are watching any given show, using traditional ratings tools such as AGB/Nielsen‘s Peoplemeters. Programming executives won’t have to worry about what the rival networks are showing at the same time as their new hit show. And ratings analysts won’t be able to track a new series’ behavior by simply looking at how each episode did on its air date.

Two hit shows going head-to-head on rival networks? Not a problem: watch one and record the other for later viewing (or get it from the Net). Missed last week’s premiere episode? No problem there either: watch it online, download it off bit torrent or pay for it on iTunes. Some of this you can easily track, but some you can’t.

Analysts will need to track each episode over time and then track the series as a whole. A VERY SIMPLIFIED graphic might look something like this, with a running total for each episode over the time of the series:

VERY simple ratings graphic

Any viewer can watch any episode from its air date to the end of the series (and beyond). This allows viewers to catch-up after the series has started or to catch any episode they may have missed. Of course, the whole concept of missing an episode disappears in the TiVo/Internet model. But in addition to tracking how many times a particular episode was watched or downloaded, you should also be tracking what’s happening with the rest of the show’s internet presence. Are viewers reading the characters’ blogs? Are they discussing the show in the forums? Are they setting up fan websites? Linking to the Myspace profiles? Uploading mashups of show clips? Not only must you track the show’s behavior over time and over several distribution methods, but you must also track and measure the user experience surrounding the show.

And finally, how do you solve the demographic problem: if you don’t know who your viewers are, how do you target them? The answer is both simple and complex. I believe that traditional demographic targets are on the way out. Social networks and special interest groups are the new targets… and these are much easier to track via the Internet than the old ones. You may not be able to tell whether a particular viewer is male or female, young or old, wealthy or not, but you can tell what news s/he reads, what games s/he plays and which people s/he hangs out with (to a certain degree, of course). One minor detail… you can’t (or shouldn’t) add apples and oranges. Traditional television ratings data categorizes viewers by demographic targets such as age, sex, location and income (because someone takes the time to visit each household in the sample and verify this information). And whereas traditional ratings analysis has always relied on a sample set of data subjects, internet traffic and behavior analysis has always examined the whole dataset. Eventually it shouldn’t be too hard to homogenize both sets of data, either by linking traditional television viewers to their online behaviors, or simply by expanding their interviews to include enough data to categorize them.

Currently, Google and YouTube limit their video data to a traditional web-traffic analysis mindset: most viewed, most recent, most subscribed. Coming from an Internet world, they fail to see the need (or maybe even the possibility) of better, more detailed reports (Yes, it could also be that they keep these reports hidden from the outside world).

As for me, I’d love to know how the most watched videos on YouTube evolved over time. Have they peaked? Are they growing? Who watches them? How about a Google Finance like chart, linking views to blog/news mentions? Which video has been linked-to the most (this one is actually on YouTube)? Which videos have been dugg and how many diggs did they get? Actually… I’d just love to work there and get it done myself!

Revision3 finally goes live

Revision3After several days of server errors, it seems Revision3 is finally live, sporting a brand new and very cool look. Gone is the “Sign Up” page… which is interesting, but more on that later. According to their own About page,

Revision3 is the first media company that gets it, born from the Internet, on-demand generation. Unlike aggregators, mash-ups, clients and web sites, Revision3 is an actual TV network for the web, creating, producing its own original entertainment and content.

They also mention that their “content is designed for a new audience. This audience, like television, expects dependability and quality, but unlike television, wants a more gritty, edgy, highly-targeted and in-depth form of entertainment,” and offer to make their shows available on as many platforms and through as many distribution methods as possible.

So far, so good… even the content is entertaining (Ctrl-Alt-Chicken, Diggnation, NotMTV, thebroken, etc…). The quality of the videos is very good and they are available, as promised, in a variety of formats (quicktime, wmv, xvid, theora).

Founded in part by some of the Digg boys, Revision3 is sure to make a lot of noise in the internet television scene. The lack of a sign up page (which was available, though not working, in the previous iteration) really caught my attention. While Revision3 is certainly no YouTube (content is generated by Revision3, not uploaded by users), I was expecting a certain level of personalization, viral marketing and social networking – particularly because of Revision3’s ties to Digg. I’d certainly like to be able to bookmark my favorite shows, share my playlists, see what everyone else thinks about any particular show, see a show’s ratings, and share my favorite shows with my friends. But, hard as I looked, I could not even find any mention of signing up as a regular user. I’d love to know the reasons behind this.

For more on a user-centric, internet TV experience, read Google Media: How Google will change the way you experience music, television and media in general. On that article, I explained what I envisioned Google doing on the internet television front, complete with an interface mock-up of what the user experience could be like. The same concept could apply to Revision3 as well.

Tech wise, Revision3 seems to be running on CherryPy (“a pythonic, object-oriented HTTP framework”) and their Flash video containers are done by BitGravity.