This study focuses on how to find similarities amongst individuals using social media based on their behavioral characteristics. Finding such similarities across myriad social networks has beneficial uses: making users aware of other users with similar interests, finding users who comment on the same blogs, and enhancing already existing recommender systems (e.g., Pandora’s partnership with Facebook). Would be interesting to see a real estate application using these theories.

• Facebook marketing “partners” (e.g., shopping sites, news sites, etc) have seen huge jumps in referral traffic after implementing Facebook’s “social plug-ins”
• Despite the success Facebook marketing partners may experience, security issues have emerged with the implementation of these social plug-ins
• Facebook’s brand image is rising with adults 18-34 but dropping with adults 35+

Brands appear to benefit by tightly integrating Facebook into their customer outreach efforts. For example, this MediaWeek article (thanks @ReggieRPR for the heads-up) reports that Starbuck’s Facebook page is valued at $20 million. Nevertheless, the CNET article points out interesting issues that could impact Facebook’s marketer outreach efforts. The core of the issue is the inherent tension between publicity vs privacy; that is, just because someone makes something public does not mean they necessarily want it publicized. Danah Boyd in her keynote address at the 2010 SXSW Interactive made this latter point, as well as the following observations:

• Technologists’ have a mantra that “privacy is dead”, but this is not true
• People still care about privacy and the “public by default” “private through effort” dichotomy represents an inherent tension for individuals wanting to navigate online social worlds (Danah was referencing the fact that in many social networks users’ personally identifiable information and activities conducted through these social networks are rendered “public” by default and that users have to proactively change their privacy settings to make such information and activities less public or wholly private)
• Marketers should remember that just because you can “see” someone does not mean they want to be “seen” by you
• A Pew study showed that most adult social network users are privacy conscious (see related Pew study here showing that younger adults seem to be exerting even more control over their digital reputations)
• Product developers need to think through publicity-vs-privacy-vs-control issues if they want to develop and launch successful products that tap the inherent benefits of the online social world

It will be interesting to see whether consumers will or will not readily use Facebook’s social plug-ins as privacy issues continue to gain mainstream media attention. What are your views?

Photo: alancleaver_2000

When users make privacy choices, a system could make educated guesses about which friends fall into trusted and untrusted categories.

…and…

Consider a politician or company that wants to broadcast a message through the network such that it only passes through trusted friends.

From a marketing perspective, it’s important to understand this as way to drive customer loyalty because as social networks continue to grow, predictive systems that deliver more relevant information in meaningful ways will drive overall customer loyalty. This could be a huge value add for such social network marketing/branding services like Facebook pages.

Related post: Peering Under the Hood at Facebook

Here’s a good history of SEO since 1999, which is valuable to understand how things have changed over the last 10 years. Change is a constant with the Internet and SEO…what “worked” yesterday may not “work” today. Thus, focus on passionate, relevant, and niche content as a way to ground your SEO efforts on a solid foundation. My opinion: relevant, niche content will remain king for SEO.

Social networks:

According to comScore, Twitter gained the most visitor traction in March 2009 (9,313,000 unique visitors), a 131% growth over February 2009 (4,033,000 unique visitors).

Social media coolness:

UC Berkeley’s Opinion Space allows you to visualize your opinions relative to others. This article gives a good overview of the process.

Here’s a nifty resource on topics related to setting up Key Performance Indicators for your webiste. KPIs allow you to measure the success and effectiveness of your website.

Mashable has a Social Media Hub series and has compiled a list of the social media scene in New York City.

The first article discusses how the Facebook data team uses statistical analysis to make informed product development decisions (the article also touches on Google’s use of data modeling and statistics).

Facebook’s Data Team used R in 2007 to answer two questions about new users: (i) which data points predict whether a user will stay? and (ii) if they stay, which data points predict how active they’ll be after three months?

For the first question, Itamar’s team used recursive partitioning (via the rpart package) to infer that just two data points are significantly predictive of whether a user remains on Facebook: (i) having more than one session as a new user, and (ii) entering basic profile information.

For the second question, they fit the data to a logistic model using a least angle regression approach (via the lars package), and found that activity at three months was predicted by variables related to three classes of behavior: (i) how often a user was reached out to by others, (ii) frequency of third party application use, and (iii) what Itamar termed “receptiveness” — related to how forthcoming a user was on the site.

The second article, posted by the Facebook data team in response to this Economist article, gives a very insightful description as to how the Facebook data team uses statistical analysis to answer an important question:

We were asked a simple question: is Facebook increasing the size of people’s personal networks? This is a particularly difficult question to answer, so as a first attempt we looked into the types of relationships people do maintain, and the relative size of these groups.

What the Facebook data team found was that a user’s passive network is 2 to 2.5 times larger than their active network (i.e., a reciprocal network where there is an active two-way communication happening), and that a passive network is just as important as a reciprocal network in building buzz.

The stark contrast between reciprocal and passive networks shows the effect of technologies such as News Feed. If these people were required to talk on the phone to each other, we might see something like the reciprocal network, where everyone is connected to a small number of individuals. Moving to an environment where everyone is passively engaged with each other, some event, such as a new baby or engagement can propagate very quickly through this highly connected network.

I’ll take a leap and say that these findings helped drive some of the reasoning behind the updated profile home page and business page “lifestreaming” functionality. Facebook’s focus on having people set up a profile–and updating this profile–and immediately engage with other people, coupled with an emphasis on increasing a user’s penetration within their passive network, is critical to Facebook’s continued growth. [Update: for an excellent three series analysis of the new Facebook pages go here, here, and here]. We can see an example of this passive network effect below where a Facebook user posted a short note that his twins are soon to be featured on CSI, the news spread quickly and opened up several channels of commentary:

Here’s an additional link to some interesting insights by Facebook’s former head of data and analytics, Jeff Hammerbacher, into Facebook’s approach to data analytics and lessons learned (these are fairly long videos, but really really fun to watch). Hammerbacher discusses how they analyze terabytes of data in near-real time to allow their various business units to make more informed decisions. My key take-away from the videos is that a graphical display of data that allows users to also “hack” the data to gain deeper insights yields great product development and customer relationship management gains.