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.

When you want to spend capital to purchase a web analytics tool, the decision is never easy.  There is no standard, scientific way to select a web analytics vendor and tool - it’s a bit of an art.  The decision is full of compromises - no one tool  or fancy family of tools from one brand will be able to do everything you think or want them to be able to do.  Nor will any one tool have all the bells and whistles you want.  

Lots of resources exist for helping you select a web analytics tool and vendor - from Marketing Sherpa to CMS Watch.   Even with good resources, it’s still tough to narrow down the selection and really identify what’s important.  I figured I’d blog on some of my thinking about the subject to add to the knowledge base in our industry.   And I figured I’d call it the “Phillips Framework” because 1) a friend told me not to share it without attribution 2) cool phonetic alliteration.

The first thing I’d recommend before beginning the due diligence process  is asking yourself or your boss the following (relatively) simple questions:

• How much money can I spend?
• What resources do I have?
• Do I have the organizational capability and maturity to run an in-house software solution?
• Do I prefer to eliminate overhead and technology expense by delegating control of my web analytics technology and infrastructure to a hosted solution run out of a vendor’s data center? 
• Do I want to integrate web analytics data with data from offline systems? If so, what systems and what methods (i.e. web services)?

You’ll have a short list of potential vendors rather quickly.  I would recommend framing your vendor evaluation across these dimensions in the context of how they are relevant to your business needs and goals:

1. Company and Technology
2. Product and/or Services
3. Features
4. Vendor Organization
5. Strategic Fit
6. Cost

Since this is Part 1, let’s discuss the first dimension of the assessment:  Company and Technology

I’d recommend creating a matrix so that the attributes presented below are on the left axis and the companies you are selecting are on the top axis.  Fill in the cells with your custom information evaluating a vendor:

Useful attributes for beginning your evaluation of a potential company and technology for web analytics include:

• Company Description. Describe the company using publicly available sources.  How long has the company existed?  How solvent is it?  What do customers say about the company?
• General Technology Description. Explain the technology and how it works. If technology uses OLAP, what happens to the confidence level and confidence interval (i.e. margin of error) when drilling down on the data?  Can I report on every dimension and attribute of available data about a segment or is the reporting limited?  How about when exporting?  
• Product and Service Capabilities. Assess the overall ability of the vendor’s technology and services organization when compared to the industry. What percentage of the company’s customers successfully deploy tags and get complete tag coverage across every page from day one?  Or successfully transfer and correctly parse customized log files from day one?
• Product(s) Required for Solution. List the product or products required to support the full solution. Can I run identical queries and get identical answers across all company technologies?
• Ease of Use. Indicate the complexity of interacting with and navigating through the interface and reports.   Assess the user experience of the GUI from usability and information architecture perspectives. Can I simply find the data I need to gain analytic momentum?
• Product Updates and Difficulty. Indicate difficulty of product updates and general migration path for upgrades. Does taking advantage of new functionality in a release usually require upgrading the code throughout my web site? 
• Real-time reporting latency. Identify the delay or lag in availability of the data within the technology.  Continuous processing?  Batch?
• Time to Implementation.  Indicate the time to deploy the baseline, out-of-the-box solution. What percentage of the company’s customers have successfully tagged all site pages and/or processed logs within 1 month after beginning? 3 month? 6 months? 
• Ease of Implementation. Indicate the difficulty level of implementing the technology. What percentage of the company’s application can I use if no changes are made to the javascript page tag?
• Data Collection Model. Identify data collection methods.  Does the company’s data schema simply rollup and report “unique” counts across time periods and delete the underlying data (even if I don’t buy an additional product)?  Does it cost more money to retain full, unsummarized visitor data for 12 months? 24? Longer?
• Data Retention and Ownership. Indicate if I retain ownership of my data.  If so for how long and at what level of granularity? For what duration does the company retain visitor data?  Is that the same across all applications (not just a data warehousing component)?
• Integration. Identify features and methods for integration with external systems.  API? Web services? Summary extracts?  Just Excel?
• Innovation. Indicate the level of innovation perceived by looking into the company when compared to industry competitors.  What do the analysts say?  How large is the company’s engineering organization?  What percentage of overall expense does the company spend on R&D?  Partnerships?
• Security. Identify the security model. Does the tool support integration with Active Directory or LDAP?  What is cost per seat or license?
• Segmentation.  Identify the flexibility and ease of segmenting data.  What is the total, maximum number of segments available for use “out of the box?”  How much more does it cost if I want to increase segments or filters?

More attributes exist.  More questions should be asked. 

Truly understanding a web analytics technology means asking hard questions and assessing the way a company answers those questions to frame your subsequent analysis and guide your selection. 

I’ll blog on part 2 in the future, probably as a monthly series over the next 5 months.  Thanks for reading!

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. 

Web analytics and statistics can make the head spin (just ask this guy)!  In this blog entry, I’m going to demonstrate applying some light statistical methods to web analytics data using Excel.   After you read this blog entry, I am hoping that you can answer the boss when asked “what’s the chance average time spent on site will be N next week?”  In honor of Avinash’s numbered posts, I’m calling this lesson #1 (not in order of importance).   Remember, 79.314159265% of all statistics are made up on the spot (indeed!), so when you can prove how you calculated yr stats, you are a more powerful analyst.

Some who read this blog entry may criticize the Gaussian nature of the following content, but I think the normal probability distribution may be the most important probability distribution in stats, and it has it’s applicability to some things in web analytics, like time-based metrics (hey, they’re continuous!). Check out Jim Novo for Pareto stuff.

First, a few definitions:

1. Continuous variable: A variable that can be measured, like Average Time Spent on Site (ATOS), as opposed to discrete variables, which are counts of things, like unique visitors.
2. Normal distribution: The bell shaped distribution measures central tendency (mode, mean, median) and has other common characteristics.  It has tails (sometimes, ahem, the tails are long).  But that’s for another post.

Determining probabilities and expected values of ATOS involves calculating things like standard deviations using integral calculus-(a good place to start).  But, good news!  We can use Excel!  It’s really easy to use special probability tables in Excel to solve complex equations like the one we’ll be solving below….

Let’s answer the Web 2.0-esque question I presented earlier: “what’s the probability the average time spent on site will be less/more than 201 seconds?”  Let’s jam!

Here’s my sample ATOS data in seconds.  Try to use as much data in your ATOS sample from your site as you can:

First, use Excel to calculate the mean of your observations (the average, my good reader, as in “=average” function in Excel!):

Average (mean) = 126.8

Second, use Excel to calculate the population standard deviation (”=stdev”):

Standard Deviation = 141.3

Third, apply the transformation formula, which allows you to convert a normal random variable to a standardized normal random variable Z.  Yup, it’s called Z. 

The transformation formula:

Z = (N - Average) / Standard Deviation

Substitute your data for N (201 seconds):

Z = (201 - 126.8)/141.3

Z = .525

Now use the NORMSDIST function of the Z value in Excel to calculate the cumulative probabilities of a value in the normal distribution, like this:

=NORMSDIST(.525)

The NORMSDIST function ”returns the standard normal cumulative distribution function. The distribution has a mean of 0 (zero) and a standard deviation of one.”  That means it shows you the probability your data will be less than N in the distribution. 

In this case (rounded):

=NORMSDIST(.525) = .70

Rounding up we find that an ATOS of:

• 201 seconds has a probability of .70

The .70 tells you that probability of ATOS being less than 201 seconds is 70%, shown visually below (the light grey).  To determine the probability of being more than 201 seconds, take the complement.  That is subtract .70 from 1 (i.e. 1-.70=.30).

Thus, when the boss asks what’s the probability ATOS will be 201 seconds, you can honestly answer “70% chance it will be less than 201 seconds, and a 30% chance it will be greater.”  And you can back it up with statistics. 

That’s all for today’s lesson, fine readers, of how one can apply web analytics and statistics to measure the Web 2.0 world.  Comments?  Criticisms?

Awesome news.  Thanks to my friend, Dylan Lewis -some call him Bob or Meriwether- the web analytics industry has a WIKI.  According to the almighty “define:” operator at Google via Answers.com, a Wiki is:

• A website or similar online resource which allows users to add and edit content collectively.
• A collection of websites of hypertext, each of them can be visited and edited by anyone. “Wiki wiki” means “rapidly” in the Hawaiian language.
• Online collaboration model and tool that allows any user to edit some content of webpages through a simple browser.
• A web application that allows users to add content, as on an Internet forum, but also allows anyone to edit the content. Wiki also refers to the collaborative software used to create such a website.

In true New England diction, it’s a wicked wiki.  Wicked awesome that is.

Here’s the word from the Passionate Analyst, himself:

I am pleased to announce that WikiWebAnalytics.com is now up and running. WikiWebAnalytics.com is THE place to provide details, articles, lore, and information about the world of web analytics.

http://www.wikiwebanalytics.com/

This wiki is meant to provide an online resource for web analytics professionals and people wanting to know more about web analytics. Contributing to it will help shape the web analytics industry, community, and future web analysts.

Here is the goal - create 300 articles in 3 months. 300 articles will help the wiki become THE resource for new and existing web analytics professionals.

Check it out at http://www.wikiwebanalytics.com.  Have fun starting an article or editing one. 

It may be high time for the Standards Committee at the Web Analytics Association to add currently-approved definitions, methinks.

Found this site in blogistan:  The Open Web Analytics Project. 

Peter Adams, former CTO of LookSmart (NASDAQ:LOOK) wants to “make analytics free.”  While I already thought we had a rather awesome free tool, it looks like Peter may also want to “make analytics open.”  That’s inspired me to alert you about his work in my blog.

I quote:

“Open Web Analytics (OWA) is an open source web analytics framework written in PHP. OWA was born out of the need for an open source framework that could be used to easily add web analytics features to web sites and applications. The OWA framework also comes with built-in support for popular web applications such as Wordpress and MediaWiki. As a generic web analytics framework, OWA can be extended to track and analyze any web application.”

While I haven’t dug into this project deeply, I’m intrigued on the surface for a number of reasons:

1) Free.  OWA even has a wiki.

2) Open and Interoperable.  Supports a PHP API, PHP invocation, HTTP API, and Javascript.

3) Integrated with WordPress and MediaWiki. New media features are provided out of the box.  RSS tracking is present.  There’s Google Maps integration (visitor plotting), and it outputs Google KML files (for Google Earth).

4) Event-based framework.  Composed of ”event types and event handlers“ that perform a specific analytic or logging function. “Events are composed of an Event type and a message. An Event’s message could be an array, object or any other data type.“ 

5) Provides developers with a feature set including a full model-view-controller based framework, a extensible module and plugin framework, an object relational mapping layer, and a lite templating layer.  Database-driven configuration.  There’s even a heatmap (ClickHeat project).

OWA provides an interesting model for how vendors can move toward technical openness.  To me, OWA is another sign of how innovation outside of the “top vendors” pushes our industry forward to adapt to the rapidly-evolving internet and the future need for system and business actuation from integrated analytics.  

If this innovation can generate scale, it has the potential to be disrupting, but right now it still seems a bit esotericly technical and overly dependent on one person (but that’s how Linux started isn’t it…).  The average marketer wouldn’t know how to get started with it, but the Web 2.0 geek would know how use it.

I’m looking forward to seeing if new mashups provide open access to their analytics using OWA… 

One to watch…

I’ve opened a can of worms!  

First read these posts:

• Eric, which if you’ve found me, you’ve probably already read.
• Avinash, which if you’ve already read, you probably found me.

Here’s my take, and let the flames begin:

• Enterprise-class web analytics is SOFTWARE. 

Personally, I’m just not the biggest fan of on-demand, SaaS, ASP, or whatever term you want to call it for web analytics.   I’ve certainly considered and used a whole bunch of hosted solutions.   The limits of a hosted solution quickly became abundantly clear to me in this industry when I needed to “do web analytics” on a real scale that required deep integration, beyond my past of one site, subdomains, or a couple of sites and subdomains. 

Maybe those aren’t your needs.  A hosted solution may be the right choice for you.  That’s great.  In fact, maybe if those are your needs and you have an unlimited budget for rollups, extracts, segmentation, filters, that model may be absolutely perfect for you.  They all have excellent features.  They all were built by smart people and have good support teams.  They all help solve real business problems.   They are proven to work (usually).  I may use them again in the future.

Don’t get me wrong, I appreciate and respect the on-demand, SaaS, ASP model.  Big time, mucho respect.  Other systems, like CRM (salesforce.com), work very well for companies everywhere, and the model works well for the many wonderful web analytics vendors who have entertained (and frustrated) me and other customers with the hosted model.  I find hosted web analytics models just fine and dandy for reporting and analysis, not integration.

Wikipedia is right.  Enterprise class software brings together all departments under one tool:  from finance, to editorial, to production, to IT, to product development, to product management, to marketing, to sales, to executive leadership, to customers.

• Enterprise class web analytics impacts the entire value chain.  

When you have deep integration needs and desire to join yr data mart with the data-warehouse and do real integration beyond simple summary extracts or Excel dumps, you need software that is interoperable, portable, and supports open software standards.  You need a system that feeds a real database like Oracle.  Then you can extend the goodness of web analytics data across the enterprise and even collect data at the edges of the enterprise to drive your value chain. 

When a corporation is dependent on web analytics data across the entire enterprise, you need control that software brings- over the data and over the system.  I also want an an open database that doesn’t have contention issues.  Somebody at one eMetrics told me “I don’t care about an open database” and “My IT department sucks.”  So software isn’t right for you.  Fine with me. 

• Buy what you need based on your requirements.

What real control do you have over a hosted model?  Calling support who will tell you after the report times out, “it’s a feature because we email the report to you in 30 minutes or less (or more).”  “But I need it now for a meeting in 10 minutes!”  ”Sorry, your query was too big.”  Huh?  Yeah, right, and so was the subscription cost. 

But “we have an SLA (service level agreement) and a T&C (terms and contract).”  So you think you have control because you signed a contract?

Who ya gonna call?  These guys:

What happens when some executive or engineering team totally and repeatedly ignores you when really need support?  Or the system goes crazy and you get no data, just error messages, with promises to fix at some time in the future?  It’s like a chapter out of Sartre.  Existential dread.  While people may have never lost their jobs for buying enterprise class software, I know people have lost their jobs from being forced to use the wrong delivery model. 

When $#1+ hits the fan, I’ll try to query the db myself, or I’ll call my wonderful team, and say hey “what’s up?”  I’d look at the artifacts of process, like a change management system.  Perhaps the DBA’s are doing fun stuff with partitioning.  I’ll call them too and ask “what’s up?”.  I’ll get the truth.  And verum factum, indeed.

Software is also a wonderful enabler for web analytics adoption and support in a business full of process.  It makes process even more optimal, and you the web analyst get to define that process.  The success (and risk for failure) is shared by everyone, up and down and across.  Web analytics is cool.  People want to do it.  The DBA’s want to use new technology.  The server team wants to virtualize.  The scripters want to write Perl to generate XML.  The analysts want the latest and greatest tool, whether created by Quahogers or Gamers.  Give the team what they want so they can be all they can be. So that’s what I’d want to give them through interoperable, portable web analytics software that supports open standards.

Eric brings up a very salient points.  Here’s one that I can’t emphasize and agree with enough: PROCESS.  P - R - O - C - E - S - S.  Live it.

Here’s a high-level process-based framework that works for software and hosted deployments:

1. Baseline - The software (or hosted) system up and running, out of the box, maybe with some light customization to feed your data warehouse.
2. Granular - Site specific meaningfulness, semantics, and metadata, and things like funnels, conversion points, and success events.  Maybe you build new metrics, add filters, and extend the data model.
3. Integrated - EAI across financial systems (like Oracle/Peoplesoft), ERP (like MS Great Plains),  and CRM (like SAP).

Avinash brings up a very salient point as well, which I can’t emphasize and agree with enough.  I quote “in the end people matter, tools don’t.”  P-E-O-P-L-E.  Believe it.

And Bill Gassman is right too: “a lot of enterprise class organizations don’t have the skills to operate an enterprise class Web analytics program.”  S-K-I-L-L-S.  Trust it.

Web analytics success comes from people who have skills and who employ process to use and extend tools.

Virtual Worlds and Web Analytics… Y’all ever play around with Second Life or World of Warcraft?  I have.  I think the concepts and worlds are very, very interesting and fun.  I find their messaging around the analytics of their user base even more entertaining though.  It’s like looking at ComScore and NNR for accurate web analytics data… really fascinating demographic stuff of questionable accuracy outside the frame of their audience panel and technology.  For example, I have three avatars, but have only downloaded one client. The trends are compelling though…

Some CMO’s I know won’t touch Second Life with a virtual ten foot, paisley, polygon pole.  Some finance folks I know laugh over beers about Linden Dollars.  Does that mean specific corporations become a central bank setting monetary policy subordinate to the central bank in the server’s home country?  How do International Fisher Relations apply when you have no interest rate?  My friends who have physical bodies say “virtual worlds are for when you have no friends in the real one.” Harsh criticisms, but they don’t negate the fact that something is happening and people are participating on some scale.  We’re all going to “do web analytics” on virtual worlds some day (maybe sooner than we think).

Where are the API’s for analytics data from these companies?  I believe Linden Labs announcing an analytics API would help push adoption by marketers forward and increase spend rates.  When I look at emerging technologies for 3D online collaboration, like OpenCroquet, I see the end of walled gardens like Second Life and WoW unless they open up the platform:

“Second Life doesn’t create a computational environment that belongs to its users - it uses a constrained computational environment (its servers) to capture “eyeballs” for a variety of schemes to derive revenue from them. With Croquet, users/developers may freely share, modify and view the source code (due to Croquet’s liberal license), the technology is not hosted on a single organization’s server (and hence governed by that organization as was the case with ViOS and now with Second Life), and it provides a complete professional programmer’s language (Smalltalk/Squeak), integrated development environment (IDE), and class library in every distributed, running participant’s copy (the programming development environment itself is simultaneously shareable and extensible). Croquet based worlds can also be updated while the system is live and running.”

Other online collaboration environments that would benefit from an open source of verifiable measurement include:

• Uni-verse.  An “open source Internet platform for multi-user, interactive, distributed, high-quality 3D graphics and audio for home, public and personal use.”
• Muse. A “software platform allowing organizations to create collaborative custom solutions that utilize rich media, 3D environments, and multi-user capabilities. Using Muse, developers can create immersive 3D environments that unite video and animation, audio, html, 3D models and much more.”
• Virtual Object System.  A “free and open platform for multiuser 3D virtual reality and interactive, collaborative 3D virtual spaces, and collaborative data systems in general.”

And the big guys and gals over at Microsoft and Sun are experimenting too (where’s Google and Yahoo? - do tell me!):

• Microsoft’s Task Gallery.  A “novel approach to bring existing, unmodified Windows applications into a running 3D virtual environment. The result is a working platform for experimentation in 3D user interfaces, in which the user retains all familiar productivity tools. This also allows for a smooth transition between traditional 2D interfaces and our new 3D territory.”
• Sun’s Looking Glass Project. A “Java technology and explores bringing a richer user experience to the desktop and applications via 3D windowing and visualization capabilities.”

Notice what all of these visionary ideas have in common: openness.  It’s only through open standards to key interfaces in these systems that we web analysts will be able to do what we do.  

So that beckons the rhetorical question, which web analytics tools right now could even work with extended data models for 3D virtual collaboration environments? 

I’m looking forward to how management at the following companies evolves their business models to focus on openness through analytics enabling their sustainable growth rate:

• There.com
• Multiverse
• Forterra Systems
• Whyville - ahh, my favorite question “why?”
• ProtonMedia
• Entropia Universe
• Habbo
• Areae

As Marshall Sponder forms the Web Analytics Association’s Social Media working group, I’m looking forward to hearing your voice on the phone calls.  Make sure you also read my good friend Eric Peterson’s take on some of this area as well.