I’ve heard prognosticators prognosticating that in the future we’ll each have a couple of social networks to which we belong.  Through those social networks we’ll create stronger relationships across geographies, schools of thought, disciplines, and companies.  Members in our networks will influence our buying decisions, hiring decisions, and introduce us to new ways of thinking.   It’s already coming true or is true for many of us: from early experiences with Napster, Friendster, Myspace, Facebook, Bebo, to current experiences with LinkedIn- social networks and the social media are penetrating our lives, our time, and our consciousness more than ever before. 

On June 1st 2007 a unique social network, called LinkSV, launched.  It stands for Link Silicon Valley.  LinkSV is about connecting with people who build and fund companies in Silicon Valley.  I think LinkSV foretells a lot of what the future of social networks are evolving into:

• Highly verticalized (and long tail).  LinkSV’s focus is for VC’s and others who want to know how, why, and where the capital flows.
• Potentially Private.  While LinkSV is now public, at one time you needed an invite.  Or for example, Orkut or private MySpace pages.
• Monetized.  $20 a month and I know who’s looking at my profile on LinkedIn, or for $50/month, I have access to all the data on LinkSV to generate a thousand reports.  Both sites offer other price points too.
• Geographically-specific.  Silicon Valley only.
• Metadata-ized.  LinkSV site has lots of metadata - from company profiles with detailed attributes, such as backers, capital raised, and corporate governance.
• Visualized.  Check it out:

The LinkSViewer, shown above, is based on GroupScope’stechnology. Very cool.  Here I can map influence, relationships, and organizational structure between and within companies and the people that build them. 

This type of social network visualization got me thinking about mapping the relationships of objects in web analytics.    

As we move from page views to event trackingto understand “web 2.0,” I’m wondering how the core construct of the “path” (also known as the clickstream) will evolve.  High-end analytics tools provide clickstream visualizations and other ways to visualize “path.” But the visualizations tend to be limited to pages during a visit.

Could basic concepts from scholarly thinking on Social Network Analysis (SNA) apply to ”doing” more rich web analysis?   SNA is based on nodes and tiesto those nodes.  With web analytics 2.0:

• The site’s taxonomy has nodes and the path is the tie. 
• The “event” is a node and the click is the tie. 

Paths and their subsequent visualizations in web analytics 2.0 go beyond the page to include:

• The taxonomy path.  The path that emerges from identifying how a visitor interacts with nodes in the taxonomy.
• The event path.  The sequence of events that a user clicks to provide context for engagement.   Events in the path may include major events, such as the page view(s) , and minor events subordinate to the page view, such as play/pan/zoom.

This type of node-based pathing when combined with other “web analytics” data provides richer information about:

• How a site is actually used.  As web sites use more widgets or AJAX methods, we all know the raw page view path or count isn’t as relevant or useful as it once was.  While page view pathing is still useful (remember a page view is a type of event), other types of pathing demonstrate how a visit or visitor interacts on a page (the event) and how that page is categorized (the taxonomy).
• What content types are most popular.  Paths across the most requested events and taxonomy nodes inform product development about frequently used widgets or modules on the site.  Editors can identify content maximizes their content agenda.
• Context for why people clicked.  We look at heatmapping tools to see “where people clicked” as they go through a site.  In Web 2.0, event pathing can help determine “why people clicked.”  Events provide context to clarify visitor intent. The page view path tells us X visit viewed Y page.  The event path says Z event occurred on Y page in X visit.  For example, if a car manufacturer’s site has a gallery with a zoom feature for visually examining the car and reading product details, the current page view path only tells you how many visitors viewed the page.  While the event path tells you how many people engaged in the “zoom” event and completed the “read” event.  If fielded with metadata about where the “zoom” was focused and what was “read,” it is conceivable that one could conclude why the visitor focused (i.e. to view a dashboard, to look at the wheels, to view more information about…).  Thus, by providing context for clarifying visitor intent, the event path can be used to automatically target key behaviors (for an upsell or cross-sell opportunity). 
• Overall user experience.  The event path helps the analyst understand the surface of the website.  The page view path helps the analyst understand the structure of the website.  The taxonomy path helps the analyst understand the skeleton and semantics of the website.

As web analytics moves from being page view dependent, to page view independent, I’m looking forward to how vendor tools evolve that reconcile and provide new methods for creating, defining, visualizing, and reporting relationships between objects, such as new ways of pathing. 

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