Month: July 2008

Newspapers: Don’t Give Up Yet!

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Dan Gilmore:

Newspapers have at least two more huge opportunities.

First is to open the archives, with permalinks on every story in the database. Newspapers hold more of their communities’ histories and all other media put together, yet they hoard it behind a paywall that produces pathetic revenues and keeps people in the communities from using it — as they would all the time — as part of their current lives. The revenues would go up with targeted search and keyword-specific ads on those pages, I’m absolutely convinced. But an equally important result would be to strengthen local ties.

Second, expand the conversation with the community in the one place where it’s already taking place: the editorial pages. Invert them. Make the printed pages the best-of and guide to a conversation the community can and should be having with itself. The paper can’t set the agenda, at least not by itself (nor should it), but it can highlight what people care about and help the community have a conversation that is civil and useful.

What Gilmore is suggesting is to stop one step earlier in the editorial process.  Great editors are excellent at searching and coalescing the voice of the people.  Stop at the search, and promote the voices themselves; you have a local competitive advantage that is hard to top.

[via Doc Searls; Photo: Matt Callow]

Response: Is social media becoming a vast wasteland?

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Shel Israel penned a great article with an unfortunate quote from writer and blogger Ashok Banker about his retreat from social media,

There shouldn’t be writers and fans. We’re all writers on such platforms and should be all equal. The moment there are writers and ‘names,’ it’s a failure of the system. I’m sorry but after seeing the way most bloggers shamelessly abuse the medium to promote themselves and their work instead of genuinely writing something worthwhile, I realized that blogging and microblogging have also become tools to crass commercialism.

Blogging, like any other technology, is a just a tool.  This one in particular has done wonders in lowering the barrier so that anyone with Internet access has a persistent soapbox.  And yes, that anyone also includes crass commercialists.

I for one, hope Mr. Banker will continue to blog and engage through social media.  He has already gone well beyond how most authors engage with their audience today, and it would be a shame to fall to a false defeat.

Visualize Your Network with Fidg’t

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figd't screenshot
figd’t screenshot

There are more and more great tools getting developed for visualizing our social networks. One of the more beautiful ones I have come across is Fidg’t.  While not quite a SN visualization tool, it does operate on data from SN’s.

Fidg’t is an interactive display that looks at your tags in Flickr and LastFM, and shows the relationships visually.  There’s even a movie of the tool in action.

Available for Mac, PC, and Linux.

http://www.fidgt.com/visualize

Wrestling with FriendSpam?

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Every day 2009: 41% Email
Every day 2009: 41% Email

Please Facebook: give us filtering!

There has been a lot of talk lately about the increasing and sometimes overwhelming amount of data we are exposed to daily.  [Google: information-overload]

What happens when it’s from your friends?

I have several hundred friends across the social networks I use, and even with the short updates SN’s usually enforce, these can add up.  Combine that with updates internal to the SN (e.g. Bob just became friends with Sally), and I get enough information that if I were to process it all, it would really interfere with my day.  However, my inability to process it all reduces and limits the value I receive from SN’s.

There are companies looking into this, including Socialmedian.  If they figure it out before Facebook, Myspace, and friends do, expect Socialmedian to steal some serious thunder.

[2009 Email Photo: Will Lion; Friends photo Tavallai]

Sales Teams need Social Networks

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Effective use of social networks (SN’s) is closer to sales than it is to marketing.  You want to build momentum in the network, and marketing alone will not provide that.

There’s a lot more to SN’s than better demographics, and given the abysmal value advertisers are are placing on Facebook (suggested $0.32 CPM vs. $1.15 for average online CPM in 2007 as per CPM Advisors LLC), demographics just aren’t cutting it.

Sealing the Deal
Sealing the Deal

The alternative SN’s should looking to is helping companies sell to their networks.  With all of the embedded relationship information, any salesman would love to get their hands on that data for companies they are selling to.

As SN’s age and continue to fill in, this becomes a reasonable opportunity (LinkedIn is already there).  In the meantime, SN’s have to provie value to retails scale vendors.  Since the per-sale return from using a sales team is likely to be negative, they need to place their bets on individuals likely to get others to buy too.

In other industries, we’ll use pharmaceuticals as our example, market research teams will do extensive survey work to determine the most influential figures in decision making relevant to their products.

Even after the enormous expense of conducting these 6 month or more research projects, and taking into account all of the known problems with determining influence with surveys, pharmaceutical companies often dedicate a specialized sales team to act on the data.  One company analyzed in a current paper showed approximately a 20% increase in revenue from this collaboration between marketing and sales.

Unfortunately, survey based methodologies become prohibitively expensive when moving from a 1,000 to 10,000 doctor network to a 10 or 100 million customer retail market.

The good news is, SN data is better and more accurate than surveys, and the data already exists.  You have the actual relationship matrix, rather than skewed survey information.  That alone provides quite a punch to sales.  Marry that with frequency of communication data, and you’ve got a goldmine for sales.

[Photo by: Beth and Christian]

Social Networks and Sales

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This Guy Sells the Big Money!
This Guy Sells the Big Money!

From eponymous Social Network data alone, I can tell you who has, for any group, the most influence, who the leaders are, and who you need to convince in order to turn the opinion of the group as a whole. The question is are you going to be a trusted adviser, or a hard sell?

This ability to analyze a network often causes a knee-jerk reaction of unease by people new to the field, myself included when I first started. But, after considered thought and discussion, there are no new ethical questions here, just the same old difficult ones. First, a discussion of sales.

We all have friends whose opinions we trust above others about certain product classes. My brother-in-law is an incredible and studied amateur photographer (not that I can ever get him to update his gallery), and to him I turn for all things photographic. Another friend is an insatiable and articulate consumer of modern fiction, and whom feeds me many great book recommendations. For electronic gadgets, I turn to yet another. I trust their judgment and opinions; if you can convince them that your product is great, you have gone a long way toward convincing me. Further, switching to the general, we look to our gurus for information and ideas about the new. If you as a manufacturer/service/producer bring new ideas to my gurus, you are helping them seek out new information, which they tend to do naturally.

So, as a Social Network provider, or as a consumer of social network data for sales and advertising, you have a choice: treat networks as just another advertising platform, and be treated by the participants as just another advertiser; or provide value into the network, and reap the rewards.

[Photo by bonkedproducer]

The Never Ending Quest for Data

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Luc Legay's Social Network
Radial Representation of a Social Network

Finding good data in this field is difficult, even most of the academic literature references relatively small networks of less than 100 or so individuals. I suggest that the academic research is just starting to take off now (although the field is very far from new), because of availability of large real world datasets available in the social networking sites.

Nathan Eagle (Reality Mining at MIT) was kind enough to share 330,000 hours of proximity and cell phone communications data he and the team collected from volunteers over the course of the project. To say I am quite excited about digging into it, would be an dramatic understatement.

For other large data sets, Duncan Watts is spending his sabbatical over at Yahoo!, and I can only hope there are other people looking really hard at the data available there, Facebook, Hi5, Google, and many more. Research into people’s behavior, especially in a commercial setting is not only a great thing for the unprecedented data, but at least equally as important, this also brings to front the ethical implications.

[Image: Luc Legay‘s Facebook network]

Friendship: #1 factor in whom we spend time with

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Mobiles & Communication
Mobiles & Communication

Like all good science, analyzing social networks sometimes works out to proving things we always thought were true. Sometimes, we never even had any idea how right we were. For example, we really do spend more time with people we like.

A few really bright folks from MIT and the Kennedy School, have a paper pending publishing:

[analyzing] 330,000 hours of continuous behavioral data logged by the mobile phones of 94 subjects, and compar[ing] these observations with self reported relational data.

Three significant conclusions:

  1. Self-reported data shows a mildly positive relationship with observed data, but is exceptionally noisy.
  2. Friendship outside of work is the best indicator of who spends time with whom at work.
  3. Physical proximity is a good indicator, and predictor, of friendship (and not-friendship).

So, what do these conclusions suggest for practitioners?

Observed vs Reported Data: Surveys are great for all the reasons surrounding explicit participation, but the bias effects are significant. Find a way to marry active participation with empirical exploration and analysis of social networks.

Friendship and After-hours: Don’t under estimate the power of emotion on business decisions. Since we’re more likely to agree with data that confirms any already held thoughts, let’s be realistic and recognize the impact that, viewed through friendship, has on communication in our firms.

Proximity and Friendship: While I was unable to tease out any correlation/causation relationship from this paper, if we consider friendship as a proxy both for trust and ease of ability to work with (through shared history, goals, culture, etc.), there are some solid implications on the upper limit to the value of outsourcing.

[Photo by Ed Yourdon]

Great Work, Lousy Title

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13th Century Social Network of Deeds in France

Good news, from Roland Piquepaille over at ZDNet…

According to Nature News, a team of French researchers has used medieval documents to create the oldest detailed social network ever constructed. The mathematicians and computer scientists looked through thousands of records of land transactions dating back as far as 1260 in a Southwest part of France.

Makes me wonder why I did not come across it earlier. Oh, right, because they titled the paper Batch kernel SOM and related Laplacian methods for social network analysis. Shame on you French Scientists, don’t hide the good stuff.

Playing with Circos

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Martin Krzywinski at the Genome Sciences Centre of the BC Cancer Agency, created software called Circos designed to help elucidate the interaction of genes, and has used it to create some truly beautiful graphs.

The software is pretty complex, and I have only figured out how to use his simple on-line version, which limits the number of inputs.  But, even so, here is an image I created using Circos.  The image represents the number of emails exchanged by the top 10 most connected participants with each other, from an active large email list.

Relative % of each other's time
Relative % of each other's time