Next Sunday afternoon I am moderating a panel at eMetrics San Fran.  The panel is called ”Web Analytics -vs- Audience Measurement.”  Andrea Hadley at NetSetGo was the brainchild of this panel idea (and yes that is her picture on her site :).  In fact, I was a panelist on the same panel at eMetrics Toronto, filling in for my friend Marshall Sponder.  Since he’s going to be in San Fran, I yielded my seat 0n the panel and decided to stand up at the podium.   Other panelists include Jodi McDermott, Director of Product Management, at ClearSpring, and some other surprise guests (from comScore and IAB maybe)… You’ll have to show up and find out…

The panel description is as follows:

Are you confused about the number of customers visiting your website? Are the metrics reported by your web analytics tool different from the metrics reported by your online media, or by audience measurement organizations? The WAA invites eMetrics Marketing Optimization Summit attendees and the local San Francisco business community of web marketers, publishers and agencies to attend this community meeting. A panel of experts will discuss the value of the metrics, methods and tools used by web analytics practitioners, online advertising media and audience measurement organizations. Find out how-to use these metrics and tools to better understand your customers, your website’s competitive standing and overall website value.

The goals for this panel include:

• Adding clarity around the tools and data associated with each set of technology and metrics - web analytics technologies and website data, ad servers and ad data, and audience measurement tools and data.
• Learning how each data source can be used to expand our understanding of customers, how effective our website is as a business channel, the website’s competitive standing and value, and so on.
• Providing insight into the role of the web analytics practitioner and how this role is growing in importance and influence over business, marketing, product, and strategic decisions.
• Discussing the role of the Web Analytics Association (WAA) and how the WAA serves the practitioner.  That the WAA is an unbiased organization that doesn’t serve advertisers, publishers, or technology vendors, rather that the WAA serves and exists for the benefit and betterment of the the practitioner and the web marketer/strategist.
• Articulating the announcement made at eMetrics Toronto on the important collaboration between the IAB and the WAA for standards review.

My goal as the moderator is not to critique, demean, or criticize audience measurement, Internet advertising technologies, or to embellish or hype up web analytics tools.  Rather I hope to clarify the differences between the technologies and speak about the value they hold together - like I did in my article for MediaPost called the Yin and Yang of Online Metrics.

So why am I telling you all of this on my blog???  Well it’s because I really want your help, whether you are going to eMetrics or not…  Since I’m the moderator, I get to ask the questions, and I don’t want to just ask “my” questions, I want to know what questions YOU would ask if you had the chance to ask.  Of course, those of you reading this and attending the panel will be given the microphone if you raise your hand.

Please help my crowdsource by telling me in comments or via email to judah (at) webanalyticsdemystified.com:

What questions would you ask to clarify the differences and value between web analytics and audience measurement tools?

Any questions you think worth asking from “why don’t the numbers match?” to complexly “what are the differences between audience measurement and web analytics systems in terms of data collection?” would be awesome and appreciated.  Thanks in advance for your help!  I’m eager to see if this social media experiment in blog-based crowdsourcing actually works!

Measuring video content with web analytics isn’t super difficult, but it has its nuances and challenges.  I’ve been thinking a bit about it lately, and have had some good conversations with a few people.  Folks I know are playing around with the likes of Joost, Vuze, and Hulu, TVUNetworks, as well as using BrightCove and Videoegg.  And, man, the popularity of BitTorrent and other swarm structure 4th gen P2P networks is larger than ever.

Simply speaking video measurement can be divided into the following types:

• Instream measurement.  Refers to measuring the video itself and the various abstract elements of the video experience, such as duration metrics (average viewing time) and interaction metrics (number of stops, plays, pauses, rewinds, fast forwards, and clicks on video content).
• Outstream measurement.  Refers to measuring the content environment and user experience surrounding the video, such as the conversion metrics (percentage of visits downloading or viewing a video), behavioral metrics (referrers to the video page, players used), and content metrics (percentage videos per channel, percentage videos viewed by topic, percent videos viewed by file type). 

By categorizing the web video analytics into these two buckets, you are better able to answer meaningfully the following questions, which must be considered prior to any rollout:

1. What are the business objectives for rolling out video features on the site?
2. What format are the videos in?
3. Are the videos downloads or streams?
4. Am I using a content distribution network or streaming video network?
5. Does my web analytics tool have the features necessary for video measurement? Or should I look for a third party, niche vendor?
6. What data collection method should I use?
7. Do I understand event models?
8. What KPI’s are relevant and important based on my business goals?

To help you formulate answers to those questions, here’s some thinking:

• Business objectives.  You, the analyst, must understand why your company is rolling out video.  In other words, what’s the goal and what strategy underpins the goal?  While video is “the rage” right now, simply rolling out video because “everyone is doing it” is no strategy (though doing so may yield a strategy ;).  A goal for video deployment could be “to generate leads,” thus you measure the scenario conversion rate for the funnel resulting in the lead generation and video download (outstream video analysis).  The objective might be “to keep visitors on the site longer,” then you would measure duration and interaction (instream video analysis).  As you all know, I firmly believe that it the business goal that allows you to contextualize what you’re measuring so that you may build KPI’s.
• Video format. Lots of different video file types exist: mpegs, qt, mov, swf, flv, avi, wma, ra, wmf, mp4 and more.  You’ll need to identify the video types you want to track so you can configure your web analytics tool to measure them.  Removing or adding filters or changing your tag’s javascript might be necessary. 
• Download or streams.  Videos can be downloaded (by right clicking) or spawned in a media player.  They can also exist embedded on the page or in another object for on-page streaming.  Thus, the way you instrument your pages will differ based on the way you present the video content. For example, if you are streaming videos, you may want to use javascript (or a vendor provided scripting language) to instrument your pages to track the video.  If you are just hosting downloads, you may simply want to run your logs to detect the number of times videos were downloaded.
• Content distribution network or video network. If your video content is distributed by a CDN or a video network, you will have to apply page tags on all the pages rendered by combining your server’s content with the content served by the CDN. Some video networks provide basic reporting that you can extend with a client-side page tagging solution.  Alternatively, you can process the logs provided by a CDN. The challenge with CDN log file processing is that you will most likely not be able to merge the data with your log files for the same site, resulting in two “profiles” of analytics data related to one site: one profile with the site analytics data and one with the CDN analytics data.
• Data collection method.  If you’ve read this far in my blogivation, you probably picked up that the data collection method you have at your disposal will constrain or enable the way you measure video.  Page tags will enable you to instrument your pages with onclick functions that pass values to the javascript and in turn to the analytics server.  Packet sniffers and log files enable you to measure downloads without modifying code.   If you need modify your web analytics tool or tag configuration to track video filetypes, you can reprocess logs to access the data.  With tags any data related to downloads or interactions with the video object prior to the config change will be lost.
• Web analytics tool features. Many web analytics tools will allow you track a video play or download in your page view reports, but only two tools support true event models: Unica NetInsight and Google Analytics.  At Emetrics San Fran in May 2007, Ian Houston and I gave a preso on “from page views to events.”  It looks like the vendors agreed, ay? ;)
• Third party tools.  With the convergence of internet and television, we’re not many years away from having a single-screen for viewing the internet, tv, and movies.  Many of us already connect our TV’s to our computers (Windows Media Server), use Slingbox, have had Tivo for years, use BitTorrent and perhaps even consume content from the sites I listed at the beginning of this post.  Companies like Visible Measures, Zango, VidMetrix, and Maven Networks already provide some flavor of a video measurement solution too.
• Event models provide the conceptual and logical framework for measuring interactions that are subordinate, equal, or a replacements for the page view.  Without getting into much detail, “events” are interactions such as the play, stop, pause in a video stream, or the pan, zoom events in a online mapping experience.  In order to articulate the instream video experience, you should understand what an event model is and how it applies in Web Analytics 2.0.
• KPI’s.Based on business goals resulting from site strategy, you can build KPI’s related to instream and outstream video measurement.  For example:

Instream:

• Percentage high duration streams
• Percentage medium duration streams
• Percentage low duration streams
• Average viewing time per stream/overall across all streams
• Percentage visits who complete stream
• Percentage visits that stop stream within 10 seconds
• Percentage visits when this stream was the last video viewed
• Percentage visits when this stream was the first video viewed

Outstream:

• Conversion rates by video filetype, video topic, channel, taxonomy node, referrer, geography, keyword, and so on
• Average streams per visit
• Percent visits/views from different channels (such as email, organic search, paid search, direct, offline)
• Average time since last stream/video downloads
• Average time between stream/video downloads
• Repeat visit rate for visits involving a stream/video download

The Internet has come a long way since I saw my first streaming video over 9 years ago (VIVO for those old timers out there).  The options for consuming video content over the web are growing everyday (and not at all limited to YouTube, ay?).  I firmly believe video on the Internet is still in its infancy, and video measurement technologies both inside and outside of “web analytics” are quite embryonic.  What a huge space for growth! 

As the internet-originated video becomes even more pervasive for home entertainment and for business communication, companies will need to employ analysts who know how to create frameworks measuring video content.  Do you? 

I’m contributing a monthly article to MediaPost’s Metrics Insider Column.  My first contribution was published last week, and I’m reposting it here to get your thoughts.  Soon I hope to describe what I mean in more detail… The article was called “The Most Measurable Medium needs and XML Standard.” In case you missed it here it is:

OVER THE LAST TWO WEEKS, my fellow Metrics Insider columnists have correctly pointed out that online metrics are neither standardized nor easily integrated across systems. Vocabulary is muddled. Numbers do not match. Data exists in silos and is isolated from related data. Systems do not adequately or easily talk to each other. Research services, ad servers, and Web analytics tools report similarly named, overlapping and often conflicting metrics. Unfortunately, these problems will not disappear anytime soon, even with emerging “standards” and continued attention paid by the industry to these important issues.

Current industry standards for Web metrics are limited, basic, and come from independent entities. Most recently, the Web Analytics Association released a set of “standards.” The WAA’s standards are elementary definitions of concepts from various periods of Internet measurement. Web 2.0 concepts like “events” are mingled with dated measurements like “hits.” Regardless, these definitions provide a very useful starting point for framing a discussion about metrics. Recently, I’ve learned that the IAB and MRC are developing a set of IAB Reach Measurement Guidelines. Let’s hope the IAB and WAA align their work efforts.

The IAB and MRC are also currently auditing “audience measurement” firms, like Comscore and Nielsen. It’s rather unclear to practitioners what standards the IAB/MRC are applying to the audit. But the hope is that auditing will expose issues of coverage error and selection bias in the black box methodologies used to create the panels and generate the audience measurement data.

It is important to note that the IAB’s audit has two parts. The first is certification, which indicates the company being audited is applying the “standards,” and the second is accreditation, which demonstrates adherence to the IAB standards.

Only time will tell if companies like Hitwise, Compete, and Quantcast will be asked to submit to auditing. It’s worth mentioning that legacy metrics “standards” (and audits) from historic organizations like ABCe still occur and carry weight with publishers and advertisers (especially outside of the United States). It’s entirely possible that newly formed organizations, like the Association for Downloadable Media will offer their perspective on “standards” for online metrics.

The idea of “standards for the standards”–however absurd it sounds on the surface — starts to seem like a good idea when considering that all these parallel efforts aren’t intersecting. Honestly though, I question whether “standards” that are purely “definitional,” even if agreed upon, will solve many of the measurement challenges companies have when trying to understand Web data and take action from it.

Standard definitions are helpful for promoting understanding and creating a controlled vocabulary for discussing online metrics, but they don’t help with what I see as a huge challenge in today ’s metrics technologies. The problem is this: currently available online metrics systems do not adequately separate data from presentation . That’s a huge limitation preventing Web data from being easily integrated with other systems.

Detailed-level Web data (the raw data) is often costly to extract, if available at all. It is nearly impossible to deliver detailed data in real time from Web analytics, ad serving, and research-based technologies in order to feed other systems. The majority of hosted (ASP) metrics systems are closed and do not allow access to key interfaces using open software standards. For the most part, today’s metrics technologies are black boxes where data goes in, but can only be extracted in various file formats after creating a report. Common export formats include csv, pdf, and doc. While XML exports are often available from many vendors, there is no standard XML schema for describing the same type of Web data across different sources!

The industry must begin collaborating and creating a standard XML schema for describing Web data. Creating a widely used, consensus-based, published, and maintained XML standard for online metrics would make it possible to more easily share, transform, and use Web data in other systems.

I firmly believe that current metrics standards must go beyond simple definitions and tackle issues pertaining to data portability and system interoperability. Then we’ll all be in a better position to reuse Web data across the enterprise value chain. Once we all agree on “standard” definitions, I encourage us to start working together to develop a standard Online Metrics Markup Language.

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?

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. 

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.