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? 

Why do we do web analytics?  What value does it generate?  What is the ROI from web analytics?”  Companies trying to justify or in the process of allocating capital to “do web analytics” are wondering…  Recent research has shown that companies serious about web analytics need to invest money in people and technology.  But what do companies get in return for doing so? 

I believe that web analytics helps online business identify potential opportunities for taking action to:

• Increase revenue.  Web analytics helps you make more money.
• Decrease cost.  Web analytics helps you spend less money to make more money.
• Improve operations.  Web analytics helps you work smarter and more efficiently.

Let’s explore some of the ways web analytics can help online business across these three core actions:

Increased revenue through:                                                                                    

• More targeted advertising sales.  Content is monetized in a many ways, from cpm, cpa, cpl, ppc, and more.  Web analytics can tell you which of these methods for generating revenue and which advertising campaigns using those channels are performing most effectively.  External campaign effectiveness may be tracked using referrer data and related dimensional reporting.  Metrics related to internal campaigning, like microsites or special advertorial offerings, can be easily provided to advertisers and agencies to identify audience consistency and quality.
• Better insights into audience segments to realize incremental revenue.  Segmentation refers to dividing a total population into groups based on one or more characteristics.  A good web analytics tool easily enables you to segment on dimensions and attributes relevant to your business.  Segmenting web data enables you to answer questions about which visitors visit when and with what frequency, depth, and duration, and more, which provides otherwise unknowable insights.  New incremental revenue streams may be realized by mapping newly discovered behavioral or demographic characteristics to existing advertiser or agency demand.
• Creating effective online-marketing and editorial offerings.  Reports showing visit frequency, depth, recency, and the time periods when the online audience visits the site assist product managers, editors, and producers in optimizing, crafting, and targeting content and advertising, increasing reach and exposure time of advertiser messaging to key audience segments.
• Ensuring pages effectively lead to conversion funnels. Metrics like bounce rate, conversion rate, clickstream pathing, and conversion metrics provide indications about how to modify or tailor pages to generate value.  Funnels can provide insights about which calls to action, content, pages, sections, and campaigns yield the best conversions.

Reduced costs by:

• Increasing the effectiveness of online work products. By identifying, monitoring, and evaluating important KPI’s (key performance indicators and KKPI’s!), the business learns what works and what doesn’t work online.  Web performance data has amazing utility when evaluating, planning, and monitoring current and future trends when assessing how to reduce cost in an portfolio of online products.
• Maximizing site operations, content, and opportunities for organic and paid search.  The performance of pay-per-click, paid inclusion, and contextual advertising and linking campaigns may be audited to eliminate projects that fail to meet goals based on conversion, revenue, or KPI’s.  By tying conversion to capital budgeting, online projects that fail to meet site hurdle rates may be tailored or eliminated.  The business can then better focus on the driving profitable revenue without misallocating resources.
• Optimizing user experience and information architecture.  Overhead reducing tools like Google Site Optimizer and offerings from other companies providing site optimization services use web analytics data to programmatically alter a site to increase conversion and lift.  CMS automation can be driven off of web analytics data.
• Pinpointing the performance of online marketing campaigns.  By creating custom KPI’s, metrics, and segmented conversion rate and slicing data via custom filters and business relevant dimensions, deep insights into online performance can be attained.  Misappropriated resources and efforts can be easily recognized and eliminated.

Improved operations via:

• Deep understanding of site traffic, visitor activity, conversions, and online value-generation.  You can’t manage it, if you don’t know about. Companies most successful with web analytics dedicate a full-time staff to analyzing and contextualizing data and performance metrics from channels like organic and paid search, affiliate partnerships, and offline.  The best staff understands the impact of the web channel across the value chain.
• Contextualizing strategic decisions with accurate data.  The ability for a corporation to gain insight and intelligence into its online activities provides management with transparency into performance.  Performance must be monitored to be improved, and there’s no other way to gain true insight into online performance to than using web analytics to guide web strategy.
• Identifying site operational effectiveness in a timely manner.  When using log files, server errors and other impediments to online customer satisfaction can be quickly discovered and remediated, which reduces negative impact and minimizes risk. 
• Predicting the impact of business decisions on performance.  By applying statistical methods to web analytics data, businesses increase their abililty to predict the impact of site changes on performance. 

Every Internet business can benefit from technology that positively impacts these three important business actions.  I recommend that you consider how your projects are framed across three actions whether you’re just thinking about getting involved with web analytics, if you’re growing your web analytics practice, or if you’ve already established web analytics at your company.

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. 

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.

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.

American’s don’t want to hear about audits in April, so I can only imagine how comScore and Nielsen felt after receiving that letter from the IAB. 

The letter made salient points.  It was “right on.”  While audience panel measurement is a time-tested method for deriving conclusions about “audience,” it must evolve beyond current maturity to fit today’s Internet.  The long tail, cookie usage, and impact on b2b traffic measurement can’t be rolled up into a bunch of people “at-home” visiting one portal site and using one ad network.   “Event” tracking, rich internet, digital video, and connected device measurement make it all even more difficult.

Panel selection must avoid coverage error and selection bias.  And that seems really hard, to me, to do on the modern Internet.  I’ve heard rumors these panels tend to self-select, offer participation based on incentives, or random dial yr digits.  Well, ah, see, I’m too busy. I buy what I want.  I have two cell numbers, three laptops.  Those methods missed me.  And guess what?  I use the Internet too.  A lot. 

So the question must be asked, do existing methods for panel measurement frame judgment samples leading to nonprobable data? 

Well, I don’t really know and I sure hope not.  comScore and Nielsen are good companies, but I’d sure like to learn more about what goes on inside. 

The panel measurement that will “win” in the long run is the company that understands it’s just one, external, metrical input into an online company’s value chain.  The internal metrics are another input (and output), and one much more valuable because I can reconcile them.  If questions arise, I can identify the derivation of my internal numbers and release that data for auditing by IPRO or ABCi or processing by any number of vendors who want my lucre.  External metrics are the Web’s dark matter.

I’ll propose that the smart option for companies with the ambitious goal of measuring the entire Internet for everybody everywhere is to think with “open” in mind.  What that means is take that black box methodology and make sure it’s simply:

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Radical, I know.  Otherwise, we’re left with the “damned statistics” and the perception of the first part too (lies, lies).  I have no way of knowing if comScore’s recent data is representative of the Internet population or if the data is simply representative of the “frame” they sampled using whatever clever method.  “Trade secret” or “proprietary,” but certainly not standard or open. Even the answers to Eric’s questions left more questions.  

A band named Pavement once sang “questions are the answers to questions in themselves.”  It sure sings true here.