Tag Archives: youtube

Google: social analytics is a key differentiator

According to a recent article in Advertising Age, Google’s social strategy does not involve building social networks to compete with Facebook but rather it is focusing on using social data to build better applications:

“As an example of the current strategy, [Eric] Schmidt talked about getting more information from YouTube users in order to offer more targeted video.”

YouTube already has a fairly robust recommendation engine:

but from Schmidt’s comments, development around this area of exposing social analytics is where they see real business value. This is backed up by moves such as YouTube’s purchase of movie recommendation site Fflick.

How can analytics be used to derive value?

For instance, predictive analytics solutions (like IBM SPSS) can traverse a large inventory of content and make associations based on a visitor’s past behavior and the behavior of their friends in the network. Match this with sentiment analysis, which can be used to look at the conversation around a video to determine whether it is loved or loathed (or somewhere in-between), and suddenly you have a more immersive viewing experience.

This doesn’t just apply to Google and video. Foursquare is apparently taking this approach to differentiate itself as Facebook encroaches into its space with its Places offering.

Whilst analytics can offer differentiation in a hotly-contested area, the issue of privacy has to be addressed. The interfaces can get so good at offering recommendations that they border on being plain creepy. Couple this with the growing paranoia around the extent to which our digital lives are tracked, and suddenly these interfaces appear more Big Brother rather than benevolent Jeeves. One way to address this issue is to be as transparent as possible when exposing social analytics.

So if Eric Schmidt’s comment can be taken at face value, I’d suggest it’s in the context of a growing trend in looking to maximize the value in existing networks rather than racing to build new ones. Social analytics, when handled deftly, can unlock this latent value in social data.

Do you agree?

The YouTube recommendation engine: a lesson in transparent analytics

Recommendation engines are all the rage. Whether it is in the realm of social commerce (see IBM Coremetrics Intelligent Offer) or in location-based social applications like Foursquare.

As the attention span of the browsing population shrinks below that of your average goldfish, so the need to create razor-sharp, perfectly honed navigation systems increases. There’s a demand on publishers to use whatever information they have to provide a more contextualized browsing experience.

That’s all well and good, but have you ever looked at a recommendation and wondered what on earth was the system thinking when it picked it? You’ve spent months on the site exploring hardcore thrash metal so why on earth are you being offered a book on floral knitting patterns?

I just went onto Youtube and noticed that they have actually got pretty transparent with their recommendation engine:

Notice the ‘because you watched’.

As we use more analytics systems to build interfaces, being explicit about how decisions are made becomes increasingly important: 

Show what determined the recommendation: This answers the question of why on earth am I seeing this recommendation? In this instance YouTube bases this on what you have watched previously, but this could just as well relate to what others with similar interests have liked (the Amazon approach).

Allow you to interact with the recommendation: YouTube allows you to remove recommendations from the list that you don’t think are appropriate. One thing it doesn’t do is spell out whether that feedback is factored into future recommendations. Some systems (such as Pandora and Netflix) use a thumbs-up/down or rating system with the implicit understanding that this information will be fed into the calculations of future recommendations. As James Taylor, the Decision Management expert pointed out to me some years ago, recommendation engines have their limit. If I booked a once-in-the-lifetime trip to Bermuda last year, there’s no point in showing me vacations to Antigua six months later. Allowing me to vote this kind of recommendation down can help systems disentangle one-shot whims from longer term patterns of behavior.      

The question of privacy: Being transparent about analytics systems and and how exactly visitors are being tracked can go a long way to allay the growing public fears around the growing mountain of data produced by the internet in general and social networking sites in particular. Indeed, here in California there has been considerable press around a bill to increase the privacy of social networks. Justin Brookman, director of the Project on Consumer Privacy at the Center for Democracy and Technology has said, “I think the idea of telling people what is going on and giving them control over their information from the beginning is a good idea for social networks and others places as well”. Privacy advocates are asking publishers to be more open about how data is being used.

As user interfaces become more reliant on analytics tools to offer a more personalized experience, there are significant advantages to displaying upfront exactly why we are being shown the recommendations we see.

What would YouTube want with a recommendation engine?

Techcrunch recently reported that Google (as the owner of YouTube) is looking at the purchase of Twitter-based movie recommendation site Fflick. Judging by the Fflick site today, this is more than just idle rumor:


What are the implications for YouTube?

On the one hand it signals a more concerted effort from the beefy video sharing site to play nicer with the other social networks in the playground (or at least hold hands with Twitter whilst working out a relationship with arch-rival Facebook). It could mean we see the kind of functionality in YouTube present on other video networks like Livestream: a display of all the Twitter backchannel related to a piece of content. For instance:


See the running commentary down the right? This is more ‘chat’ than ‘comments’ with tight integration with Facebook/Twitter. 

On the other hand, it also opens up the possibility of YouTube to start mining user data to offer recommendations. How useful is this on a video site? Just look at the Netflix story. The popular US video rental service has made a big deal of its ability to guess what movie you want to add to your rental wishlist. It bases its recommendations on what you’ve seen in the past, how you rated it, what others like you have seen (and a bunch of other variables even including the day of the week on which you’re viewing the site!) Netflix prizes this technology enough to have made it a central part of the site navigation and even paid a team from AT&T $1 M for coming up with a winnning algorithm in 2009.

YouTube has a much bigger collection of content, a wealth of behavioural data through its huge viewing figures. It generally knows less about its visitors than Netflix does as the site doesn’t require you to login to engage. Potentially, that’s where the Twitter piece comes in to play: you give up some of this information about yourself each time you tweet. Fflick provides the service to tie the tweet back to the video. Fflick also provides the service to pick through your Tweets and use these to determine what content you might like to see next.

This kind of application of predictive analytics is hot right now in the social media space. Foursquare is believed to be using predictive analytics to keep Facebook at bay in the location-based-services sector.

Social media is making us increasingly impatient and we are starting to demand more from our interfaces. Add to that the growing market for hand-held devices that offer precious little space for content, let alone navigation, and you have a compelling case for services using whatever technology they can to pinpoint what you probably want to do next, and serve that up. If they don’t engage, the next video-sharing site is only a short URL away.

More on the Fflick acquisition

Social media game at IBM Impact event

IBM is currently hosting its annual SOA event at the Venetian in Las Vegas. In order to promote the event and to encourage collaboration between attendees and those who couldn’t make it this year, IBM have launched a number of social media iniatives.

One promotion that particularly caught my eye is the social media game, whereby anyone who actively shares content around the event is entered in a competition to win free entrance to next year’s conference. So whether you blog, post videos on Youtube, pictures on Flickr or engage in any one of the following social media tools, you have the chance to win.

Activity Points per Activity More Information
Register at SOAsocial or the Impact Social Network* 10* points (for both, earn 20) SOAsocial.com
Impact Social Network
Tweet about Impact 1 point www.Twitter.com (include hashtag #ibmimpact to tweet)
Blog about Impact 5 points Any blog post related to Impact will count, as long as the tweet includes a link to the post and the hashtag #ibmimpact
Pic posted to Twitter (using Twitpic, Flickr™, etc.) 2 points Any picture related to Impact will count, as long as the tweet includes a link to the picture and the hashtag #ibmimpact
Post a 12 second video about Impact 2 points 12seconds.tv
Post YouTube Video about Impact 5 points YouTube.com
Attend a Tweetup at Impact 5 points See details above.

RSVP for Monday

RSVP for Tuesday
RSVP for Wednesday

Grab a ‘scavenger prize’ 10 points Be sure to follow @smartSOA on Twitter for clues to scavenger prizes

A leaderboard around the event lets participants know where they stand and the winners will be announced on the last day of the conference.

This looks like a great idea to encourage participation in social networks and I look forward to following up with the team after the event to gauge the success of this initiative. For more on this and to see how else IBM is using social media the Impact event, check out the Impact Communities page.

(In the interests of disclosure, I should point out that I am an IBM employee)

Making sure your YouTube videos rank

The Underground Confessions blog recently covered the thorny subject of driving more traffic to your YouTube video content. They suggest the term YouTube Ranking Optimization (YRO) as a description for this field, which I’m sure is set to grow – especially as more and more companies now take the plunge into using YouTube as a way of distributing video content (it’s something like the 5th most visited site on the planet).

So, how do you ensure that your video ranks highly? It’s pretty close to what you do to optimize web content (or a blog for that matter). Basic items they use:

  • The title of your video
  • The description of your video
  • The tags that you assign to your video

By offering this basic advice, the post has attracted a great deal of comments by those asking questions or offering their own experience of YouTube optimization.

One particular comment stands out, together with Jeff’s response:

Chris says:
Hey Jeff thanks for the post.
I just checked your profile on Youtube and saw your videos that reviewed the Casio Exilim under the search term ‘Casio Exilim ex-z1080?.
I saw that you were kind of split testing the results.
And the newer version of the same video put up 1 month ago is ranking higher than the one that was put up 6 months ago – YET the 6month old video is actually rated 3 stars compared to the 1 month old video.
The only other difference is that the newer video has more comments than the older.
It’d be interesting in the test results.
1. Do newer videos get more preference than older?
2. Are videos ranked according to the number of comments?
3. Do the contents of the other videos in your profile (tags, titles and descriptions) as well as your profile name, play a role in the ranking of your video amongst others for the same/similar keyword?
It’d be interesting to find the test results. It could possibly be a combination of all of the above.
Maybe finding that out will help you put out your ooined term ‘YRO’ in the internet marketing realm. Anyway, thanks for the heads up.
Jeff Johnson says: Here are the answers to your questions:
1. No, newer videos do not necessarily rank higher than old ones. It has to do with many, many factors including incoming links, comments, tags, the number of sites that host it outside of youtube, the quality of those sites, etc.
2. Yes, commenting helps but is not the only thing that matters.
3. Yes, the only way the engines know what is in the video is by what you tell them is in it by use of your incoming link text, the title tags, your description, and any of the words found on the pages surrounding it.
That pretty much means you should optimize the pages that your videos on in the same way you would for a regular page.

I was interested to hear that the quantity and quality of external sites hosting the video plays a part in the ranking algorithm.

I don’t have any concrete evidence for this, but one thing that does appear to happen is that channels with a lot of content tend to outrank lesser channels (much like the way, as a vast generalization, Google favors sites with more content rather than less).

If anyone does have more definitive answers, please let me know.

Read the post from Underground Confessions