Targeting refers to the process of identifying characteristics of a segment so that relevant content may be matched to it and delivered at a time when the segment is most open to the message. The idea is the right content to the right visitor at the right time (optimally in real time). 

For example, you may visit a site, and see some type of ad unit calling out at you to “meet singles in <insert_your_city>.” When browsing real estate you may see ad units for realtors and mortgage companies.  After entering a keyword such as “car prices” and clickingthrough the SERP, you may see an ad for a local car dealer.   That’s targeting in a nutshell.  It’s simple: 

1. Visitor X has these attributes. 
2. We have content that we think will appeal to Vistor X’s attributes. 
3. Let’s show that content. 

While targeting has helped to increase ad clickthrough rates, it’s far from an ideal science.  Current methods for targeting have inefficiencies.  What if Visitor X just bought a new car after his recent marriage?  Unless the targeting engine is made aware of the visitor’s current state, the targeting may be off and not yield desired results. 

Even with limitations around “current awareness” targeting is perceived in the Internet industry as a crucial activity for maximizing the effectiveness of advertising and content.  Targeting is the next stage after A/B and multivariate testing.  Once you determine the preference of segments based on testing, you identify content to target. 

In new media, targeting is something associated with paid search campaigning, ad serving, and content optimization.  It’s not uncommon for targeting activities to be based on:

• Category and sub-category.  Conceptual constructs like “categories” of topics on a media web site or products on an ecommerce site can be targeted to include certain types of ads or messages.  The notion of a “zone” fits in here as well.  The idea is that if visitors are browsing in your category for “hardware floors” you could offer them an ad or content specific to “flooring installation services.” 
• Geography.  Country, region, city, state, DMA are all targetable constructs.  You may choose target people surfing from 02141 (Cambridge, MA) an ad for pre-sale Red Sox tix or content about Mike Lowell’s recent contract.
• Browsing environment such as the connection speed, type of browser, operating system, user software, domain, and ISP.  An ad network serves an ad for Verizon DSL to a modem-based surfer by detecting the visitor’s browsing environment.
• Time.  The idea of only showing content during specific periods of time is called “parting.”  Common types include day-parting and season-parting.  For example, a B2B site only choosing to show ads for a particular manufacturers product during business hours – the site’s busiest time of day – would be an example of day parting.
• Keyword.  There are many different types of keyword targeting.  Google does fantastic things with targeting ads based on the keywords in queries.  Content Management Systems can target content based on on-site search keywords or referring keywords.  “Keywords” may be associated as metadata with site sections or pages, similar to a zone or a category targeting on an ad server.  Once a page is associated with “keyword” metadata, you can tell your server to target that keyword (and all pages where it exists as metadata).  If two categories each with different content share a targetable keyword, I can target ads across both categories to pages tagged with that specific keyword.
• Language.  When a language is set, you can target ads to visitors with that setting. Think Google.  Keep in mind that when you target by language, the creative copy is not translated. 
• Demographics. If the ad server is aware of a segment’s demographics, such as age, gender, income, title, purchasing power, and so on, an ad can be targeted on that basis.  Sometimes this is called “profile targeting.”
• Context.  Think of Google AdSense and how it matches ads based on the semantics in site content.  Now you understand content targeting based on context.
• Profile.  Targeting is possible based on conclusions drawn and rules created from the known attributes (such as purchasing propensity) about and individual or segment.

Enter one of the holy grails of online advertising and new media: “behavioral targeting” – an advanced form of targeting. Behavioral targeting refers to the process in which content is shown to a visitor based on the web sites they visit (or have visited) and the actions they take on those sites.  

Behavioral targeting involves:

1. Knowing where a visitor “comes from” and what they’ve done in the past. 
2. Determining the context of the visitor on the site. 
3. Detecting the visitor’s current behavior.
4. Serving relevant content and/or ads matched to the behavior.

By understanding the visitor’s past history, current state, and most recent behavior the marketer can target content in order to influence some point in the customer buying cycle- often at the stages of awareness and consideration.

So where does web analytics come in?  You would think web analytics data from “web analytics” technology would provide the seed data for enabling “targeting.”  It can be but in most cases, targeting is a function provided by the ad server or network or another technology called the “behavioral targeting platform,” not the analytics tool… the data does not come directly from the web analytics tool.  I’d love to hear how well (or if at all) Omniture TouchClarity is integrated with Omniture Discover or other offerings. 

In order to make web analytics data useful for targeting (if you can at all), you will need to use your web analytics data to:

1. Define segments to target (hard to export from web analytics tools)
2. Feed those segments and associated behavioral data to another tool (achievable if you own your data and run a tool in-house.  Harder and more costly if not).
3. Report on segment performance after targeting (that requires employing the right people and enabling them with the right tools)..
4. Analyze segment performance after targeting (again employ the right people and enable them with the appropriate tools and resources).

While I’ve only covered a very little bit about “targeting” and even less about “behavioral targeting” in the context of web analytics, I hope that my simple description of current methods for targeting and some thinking about “what is BT” will help you understand the emerging ecosystem in which analytics tool are interoperating now and will interoperate in the future.

Web analytics data is the raw material from which companies will realize new online products and deliver differentiated services that generate future value.

Right now as I type I can get web analytics data from so many sources.  Google Analytics and Open Web Analytics provide the data for free (once I spend the overhead to set it up).  So does Compete and Quantcast.  Many other companies are willing to broker this potential commodity to me at various price points - from the low four to seven figures. 

The price from web analytics firms for what is essentially the same data is all over the map!  Why? Perhaps because enterprise vendors know that you the customer will no longer pay for just data (thanks Google!). 

Features and services provided on top of the core data that’s valuable to a practitioner.  To the company employing the practitioner, it’s the insights generated from the data that’s valuable.

We’re seeing a lot of web analytics data operationalization via features for:

• Extension into business intelligence (and in the future leading to business analytics).  The best web analytics firms are providing open relational databases and creating methods for joining data from other systems to “extend the data model” or feed the enterprise data warehouse.  
• Automated testing and optimization.  The notion that “you aren’t doing web analytics, if you aren’t testing” provides evidence that siloed, lonely data won’t do much for your business.  In that light, automated testing is only as useful for prediction as the people setting up the tests.
• Targeting.  Using analytics data during the session or after it to automatically target content based on key visitor attributes will increase conversion.  While targeting technologies use analytics data, the value derived isn’t from the data, but from the potential conversion lift of the activity we call “targeting.”
• Proxy scoring.  Assigning a value to an event, interaction, page view, or visit can identify high-value segments and customers.  Scoring abstractions operate on the data to indicate value. 
• Profiling.  Building a picture of your online audience by aggregating data from various sources including web behavior, customer transactions, and demographic data enables one-to-one marketing.  Web data is part of the profile.  The value is in the profile.
• Integration.  Joining analytics data with data from other systems in a unified data model, or enabling machine-to-machine communication of analytics data will yield value.  Again the data is important, but the value is in the outcome.
• Alerting.  Indicating when data exceeds pre-defined upper and lower bounds and where those thresholds have been exceeded is valuable.  Once again, the data is crucial, but the alert is catalyst for value creation.

Data SEPARATED from the application, from the presentation, is extremely valuable.  When “unsiloed” and described or made available using open standards, it can be reused by other applications.  Insights realized from moving/sharing/synching data that drives enterprise value 

Yet functionality on top of the data layer doesn’t make web analytics easy and instantly drive value.  Few corporations have the slightest clue about how to take advantage of all this functionality. 

To creation value the modern web analytics practice requires:

• Dedicated in-house professionals. No duh, here.  You need people who understand the data, use ”features” to help analyze it, and who can then test hypotheses to optimize and measure outcomes.   
• Vendor and third-party professional services.  Consultants must go beyond “repeating back what you say, then claiming they can solve the problem” and deliver quantifiable, measurable value that improves business process.
• Web services. Web analytics tools need to use web services.  The “web analytics” tool of the future will take advantage of technologies that provide platform-independent protocols and standards used for exchanging data between applications. 

We’re going to continue to see highly-specialized web analytic’s “experts” at companies work with services firms (or professional services staff from vendors) to combine the ”off the shelf” web analytics products with web services technologies to create the automated marketing architectures of tomorrow. 

These “marketectures” will:

• Bridge quantative analytics data with qualitative data to identify the 360-degree view of the customer experience.  Ahh buzzwords…
• Use web services to operate on analytics data to solve highly-specific and specialized technology and business problems.  For example, using a WS to pass custom interaction events in Adobe AIR/FLEX to understand behavior in your RIA. 
• Enable HUMANS to realize new *predictive* insights from data.  Yeah, automated testing will help, but think about it… who sets up the tests? 

Facebook’s emergence as the platform du jour for social networking and the Internet marketer made me start thinking about how to “do web analytics” on the Facebook platform.  Even Eric’s moved the Web 2.0 Measurement Group to Facebook.

Apparently Facebook is thinking about Web 2.0 measurement too.  A few days ago Facebook posted this blog entry.  Facebook claims to be measuring engagement based on touchpoints in applications across four areas:

• Canvas Page Views 
• Link Clicks in FBML
• Mock-Ajax Form Submission
• Click-to-Play Flash 

They are calculating a number of “Daily Active Users” from midnight to midnight each day by “putting” the touch points together.  Well that’s dandy, but it assumes all touch points are equal and biases the measurement toward applications that have more touchpoints… In fact, they’re only measuring engagement with applications in the most liberal of definitions.  Take a closer look, and I’d have to agree with Jeremiah Owyang that Facebook may have their terms confused.  These metrics measure Interaction.  Where are the frequency and time measures necessary for engagement?

Regardless it’s a good start for creators of Facebook applications!  But what’s a Facebook user to do?  Well I’ll tell you, my fine reader.

Over the last several months I’ve learned that there are several methods:

• Facebook’s API  and FQL.  The Facebook API is a REST interface, like Feedburner’s.  You can use it to add “social context” to a Facebook application using profile, friend, photo, and event data. Facebook’s FQL is a SQL-like language for querying the platform. Cool. Developers are psyched.
• FBML and Google Analytics. You can use the FBML Google Analytics tag to count the number of page views you’ve had on the canvas page of your Facebook application.  That’s one part of the FB engagement metric.
• Facebook Applications.  I’ve found a whole bunch of cool Facebook applications that provide unique ways of understanding the Facebook network.  Each application provides a slice of analytic-like functionality. 

Here are some of the social media analytics applications that I’ve been playing around with on Facebook:

• Friend Wheel.  Spin an interweaving mandela of ties to your all your nodes.  This cute app captures how all my friends related to each other, and also uses Web 2.0isms like “click to embiggen.”  Kewl.  Check it out:

• Friend Grid.  This app displays a little grid of your friend’s Facebook pictures.  It updates itself too.  That’s handy when you have a loose Facebook friending policy, like me.  Or like The Scobleizer who just today reached the limit of 4,999 friends on Facebook.  Because of Friend Grid I now know what some of my Facebook friends look like.  Heh.  Do you recognize any of these people?  I recognize most of them.

• Friend Sets.  Create multivariate syllogistic like visualizations about dimensions of your Facebook friends as you define them.  Don’t know what I mean. Check it out below (and note that I did not create any of these sets… they are ”presets” from the creators):

• Interactive Friend Graph.  This app is a cute little tool that provides a multinodal visualization of the ties that bind your Facebook friends.  You can rollover a circle to view the persons full name and an overlay mapping of their connections.  Then you can click on the circle to view their Facebook profile, send a message, poke, or add someone to friends.  Check it out:

• Socialistics.  By far this super cool application is my favorite for doing analytics on Facebook.  It gives you a bunch of insights into the relationships between your network including an intriguing amount of demographics and influence-based characteristics.  It can generate a multitude of tag cloud visualizations, pie charts, and assorted visualizations about things like gender, location, influence, relationships, and more.  Check out these cool screen captures below.

Here’s a distribution of the educational institutions of my Facebook friends, those Ivy leaguers:

Here’s a distribution of the political beliefs of my Facebook friends, those liberals:

Socialistics also has tag clouds of my Facebook friends.  The tag cloud on the right shows the popularity of friends within my personal network by name. The one on the left by picture. I’ve shrink it all to protect the identities of the guilty (mostly )

                

As Facebook goes more mainstream and social networking become more ubiquitous in the business world, we’re only going to see an increasing demand for tools that help measure activity, behavior, demographics, opinions, and influence on social networks.

While these applications aren’t enormously powerful and or very engaging for business purposes, they represent a widgety beginning of new type of new media analytics.  I’m excited to see how all this Web 2.0 social networking stuff will continue to play for out for “web analytics.” 

Integration of social networking analysis features into current offerings from web analytics vendors could take social media measurement into new exciting areas full of profitable revenue.  I envision many uses of social networking and social media analytics for online business:

• Helping companies realize new products.  Imagine the lessons to be learned from 227 groups with thousands of people discussing new product development.
• Identifying social trends impacting their business.  Anyone want to learn about social characteristics of those who believe in alternative energy and/or boycott Exxon, Citgo, and Shell? 
• Enabling larger enterprises to more proactively respond to the voice of the customer and manage risk.  Are companies like Walmart, Coke, McDonalds, and Nestle listening to the thousands of voices?
• New revenue models for behavioral marketing and targeting campaigns.  Maybe this is a long stretch, but could cost-per-target (CPT) ads be very far way?  It seems obvious to me to say that social network analytics will be used to target ads and offers in smaller batches focused on high-value niche segments that are typically hard to reach using mass media broadcasting techniques.  Facebook seems to already be investigating this niche. 

Do you Facebook or use other social networks?  Are you interested in analytics for social networks?  What do you think? 

Web analytics and keyword metrics came up in a conversation I had last evening with a friend of mine from my days in “information retrieval“ - when Googol was a really, really large number, and we called keywords ”queries…”  Over a Belgian beer (a Cantillion), I was asked to “name the top couple of metrics I’d want to know about a set of existing keywords if I were selecting a few to continue to optimize or buy?”  

I told him that any keyword-related metric should be analyzed within the context of campaign objectives, which in order to be measured and reported need to be defined before the campaign begins.   Macro level campaign goals should be identified before performing micro-level keyword analysis.  Once campaign goals are known, analysis can focus on achieving the optimal keyword mix to fulfill them.  A single, keyword-related metric should rarely be taken as a stand-alone indicator of performance. 

Here’s a synopsis of what metrics I told him I think are useful to examine when performing keyword analysis:

• Referrers.  At a basic level, identifying the sites that sent keyword traffic is common sense (like not excluding the Googlebot ;).  You may uncover keywords for which your site’s content “accidently ranks” on a particular engine.  These rankings may not be immediately obvious from a straight list of top-performing keywords.  By digging deeper into keyword referrers, you may find sites like these: forex-cash-fast.info, gambling1×2.com, nhadep.net, nghenhac.com, and xn--q2yr34f.com.  Clickfraud?  Poor targeting by an engine?  Lost money?   So many questions can be asked from keyword referrers!
• Geography. Show me my keywords segmented by dimensions like Continent, Country, City, Zip Code to assist in planning geo-targeted campaigns and identifying the broad content themes that appeal to the geographic long tail.
• Number of Visits and Percentage of Total Site Visits.  Raw visit and percentage totals indicate the “reach” of the keyword- the degree to which a keyword has penetrated a target audience.  I could compare the number of visits to the number of searches for that keyword using Overture’s Keyword Selector Tool to assess reach and correlate whether the cost to buy or the effort to optimize the keyword is acheiving the desired effect.
• Average Visit Duration.  It’s not an engagement metric, but average visit duration does tell you whether or not the visitor remained on your site and if so for how long.  It can be useful when taken into context with the page-view to visit ratio and segmented by other dimensions, such as conversion rate.  
• Page View to Visit Ratio.  One of my favorite metrics on a per keyword basis is the view:visit ratio.  This ratio identifies the average number of pages viewed per visit for that keyword.  If your keyword should convert the visitor from the landing page, and you are seeing a page view to visit ratio greater than one, what’s up?  If your trying to persuade visitors to enter some sort of non-linear or linear, multistep funnel leading to a conversion, and your page-view to visit ratio is one, what’s up?
• Bounce rate.  A key metric that identifies what percentage of visitors enter the site on the keyword’s landing page and immediately leave.   If your bounce rate for a keyword is over 35% and you are targeting that keyword, you should think about landing page optimization.
• Conversion rate. Conversion rate is the percentage of visitors referred by the keyword who succeeded in completing a pre-identified, value generating event on the site, such as a purchase or registration.  Conversion rates measure how well the keyword acted as a trigger for driving on-site revenue.  By segmenting your keywords based on conversion rate or other dimensions, you may notice broad content themes that drive on-site success events.  These themes could be used in persuasive messaging that includes hyperlinked points of resolution moving visitors into the non-linear conversion funnel.

Then I told him to “segment, segment, segment.”

Many metrics and dimensions can be applied to the analysis of keywords beyond the few I listed above.  What metrics do you look at on a per keyword basis when planning search engine optimization efforts or when planning paid search campaigning?

Let’s conclude this series on the spiders and bots of web analytics 2.0 with a framework for ranking the spider and and bot capability maturity of  a web analytics practice! 

Before I do so, let me mention a few things:

• How/if you detect spiders and bots depends on your data collection methods.  It is thought that page tags exclude spiders and bots because they don’t execute javascript or request images. Until they do. Log file data must be examined and filtered (either programmatically, manually, or both), and those filters need to be maintained by a web analytics professional.
• Bots are used for a many purposes.  From inflating metrics, to indexing text for search, to chatting, to spamming, to data mining, to site performance monitoring, to click fraud, and more.
• Bots are constantly evolving. Just like web analysis.
• Not all bots are bad news.  You want bots crawling your site.  Some bots are good and helpful for your online business. Imagine if you blocked Googlebot.  Segment the robotic traffic into separate reporting (in the best case) and make sure its filtered from your externally reported numbers.   

Now without further adieu, here’s a web analytics 2.0 framework for ranking your spider and and bot capability maturity:   

• Red.  You are in the red if the following applies:
  • I don’t understand the impact of spider and bot traffic nor do I measure, filter, or segment it at all. 
  • If I run an in-house solution, I haven’t updated my list of filters and exclusions since I deployed it. 
  • If I run a hosted solution, I rely solely on my vendor to control all aspects of bot filtering and data purification.
  • I don’t know when or how the Googlebot hits my site, but it sure does! 
  • I’ve never thought of reporting spider traffic for SEO.
  • I think the words “yahoo” and “slurp” refer to delicious soup.
  • Spiders scare me.
  • All bots are bad (no, they aren’t!)
• Yellow. You are moving out of the red zone, into the yellow if:
  • I occasionally look for suspicious traffic. 
  • If I run an in-house solution, I have occasionally updated my list of filters and exclusions since I deployed it. 
  • If I run a hosted solution, I rely solely on my vendor to control all aspects of bot filtering and data purification, but I ask for verifications of compliance with industry standards.
  • I’ve heard of the Googlebot and maybe I use or I am thinking about using Site Maps (do it!). 
  • I know about Yahoo! Slurp.
  • It would be cool and useful to report on spider and bot traffic and learn which bots are good and bad. 
• Blue.You are in the blue if at least every month or on an ad hoc basis:
  • I work with my vendor or in-house team to recognize and remove spider and bot traffic, conforming to industry standards. 
  • I do a monthly update of my filter and exclusions list, or I know my vendor does.
  • I capture bot traffic in my server log files, but I may or may not report on it.
  • I know all about the Googlebot, and I use Site Maps. 
  • I realize the Googlebot and Yahoo! Slurp behave differently on my site, but I don’t know exactly how.
  • I know which bots are good (like search bots) and bad (like content scrapers).
• Green.  You are in the green if:
  • I have established a process for regularly removing spider and bot traffic and for keeping my lists of exclusions and filters up-to-date, or I am *absolutely certain* my vendor has such processes.
  • I am in compliance with industry standards.
  • I measure spider and bot traffic and segment it into distinct reporting separate from my human traffic reporting.
  • My SEOers love me, the web analyst, and I am involved in educating people in my company about bot traffic from IT to managers to consumers of reporting and analysis.
  • I know that there’s more than one Googlebot, and I know how its crawl differs from Yahoo! Slurp (hint: Slurp visits more).
  • I know detailed metrics like the “total time online” of all my bots.

What I’ve covered in this four-part series only covers a little bit of what I know and what there is to know about spiders, bots, and crawlers and how they affect web analytics.   I’d enjoy hearing other experiences or opinions, so please share your comments if you feel like it.  Until next time, fine readers.  Thanks for visiting!

I’ve been taking a look a deeper look at the trends in social networking and the analysis of social networks using nodes (such as taxonomy) and ties (such as clickstream data).   A few concepts from networking theory are intriguing me, and I figured I’d bring them up here to see if anyone has any thoughts:

• Betweenness. Identifies the degree to which a node in a social network is interrelated to another node.  Identifying degrees of betweenness in taxonomy nodes and combining with “normal” analytics data could enable the analyst to:
  • Detect nodes with the most betweenness to identify content that should be *automatically* served when a visitor interacts with a related taxonomy node (extending site optimization technologies)
  • Determine misappropriated editorial agenda and withering products by contrasting the “popularity” of nodes with the most or least betweenness.
• Clustering.   A concept used to express how visits relate to core taxonomy nodes could:
  • Provide a means for visualizing how visitor segments cluster around particular pages or nodes in a taxonomy
  • Enable the analyst to visualize the broad content themes that drive the most visits
• Density.  Certain bloggers and site pages tend to see larger numbers of repeat visitors, comments, or maximized time-based metrics when compared to other pages.  Can a metric for “content density” of a site be calculated?  Perhaps by crafting a equation from counting objects in a taxonomy node, value-scoring each object, and seeing which objects were interacted with most frequently?
• Influence.  The guideline is 99% lurk and 1% influence.  Can we gauge visit “influence” and visualize it from:
  • Pathing where visitors who have performed the most/least interactions and contributions ”go next” off-site.
  • Value scoring an “influence metric” for Interactions, Contributions, posts and comments, and off-site exit links in each visit, then adding up the values to calculate a new influence-based KPI measurement per visit.  Finally comparing the “influence metric” across all visits.

If you are still following me ( ), what I’m working at understanding and reconciling is whether social network analysis theory when combined with web analytics can illuminate the analyst with new ways for thinking about a web site. 

By combining a rules-based approach to processing this type of data, the possibility for automatic content targeting and the idea of a “living site” self-optimizing based on visitor interactions with taxonomy nodes or site objects becomes closer to reality.  The potential to use analytics data and social networking theory for building and realizing new combinations of product, content, and design becomes possible.  For example, I could create rules and logic commanding my CMS fill a “related topics” module or widget on a particular page with content from nodes that have the smallest amount of betweenness and the greatest density.

It’s clear that social networking impacts web analytics. Most major analytics vendors don’t seem to be thinking about applying (or how to apply) concepts from social networking.  I’m looking forward to vendors bringing social network theory into their technologies by perhaps combining, rules-based algorithms for site optimization with existing analytics data and new, open API’s (for example, Facebook’s new API or LinkedIn’s forthcoming API) to drive profitable revenue from new and existing channels.

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.