I’ve been thinking a bit about targeting, and how we in the web analytics industry have just a ton of visitor or segment-level data that can be used for targeting ads or content, but most tools don’t let you use the data or easily feed it to other systems to do any targeting.  It’s rather odd, don’t you think?   Even Omniture Test and Target isn’t using, as far as I’ve learned, a single data model or the data collected from their behavioral tools, like HBX or SiteCatalyst, for targeting.  All their data models and thus, their data, are unique to the products in their platform.   So I decided to resussitate/revise a blogviation and offer it as food for thought on MediaPost.  When I reread this post, it’s more of an informational post for product managers on how I’d begin thinking about targeting with analytics data and what types of targeting are possible, so here it goes.   

Targeting refers to the process of delivering content or ads to segments or visitors based on their known attributes.  The goal of targeting is simple to understand: maximizing the performance of content or an ad by serving it to visitors at a time when they are most open to the receiving the message. 

For example, you may visit a site, and see some type of ad unit calling out at you to “meet singles in <insert_your_city>.”  When browsing a real estate site, you may see ad units for realtors and mortgage companies.  After entering a keyword such as “car insurance” and clicking through the search results, you may land on a site and see an ad for a car insurance company or land on a page that persuades you to begin the process for creating an insurance price quote.  That’s targeting in a nutshell.  It’s simple for a site owner to understand:

1. Visitor X has these attributes.  
2. We have content or an ad that we think will appeal to Visitor X’s attributes. 
3. Let’s show the relevant content or ad. 

In online media, targeting is associated with paid search campaigning, ad serving, and content optimization based on recognizing and responding to the following attributes:

• Category and sub-category.  Conceptual constructs like “categories” of topics on a media web site or products on an ecommerce site can be targeted to include certain types of ads or messages.   The idea is that if visitors are browsing your category for “hardware floors,” you could offer them an ad or content specific to “flooring installation services.” 
• Geography.  Country, region, city, state, DMA are all targetable constructs.  You may run a sports site and choose to target people surfing in from 02116 (Boston) an ad for Red Sox tickets or content about Manny Ramirez’s recent trade to the Dodgers.
• Browsing environment such as the connection speed, type of browser, operating system, user software, domain, and ISP.  An ad network could serve an ad for DSL to a modem-based surfer by detecting the visitor’s browsing environment.
• Time.  The idea of only showing content during specific periods of time is called “parting.”  Common types include day-parting and season-parting.  For example, a B2B site only choosing to show ads for a particular manufacturer’s product during business hours — the site’s busiest time of day — would be an example of day-parting.
• Keyword.  There are many different types of keyword targeting.  Search engines target ads based on keywords in queries.  Content Management Systems target content based on site search keywords or referring keywords.  “Keywords” may be associated as metadata with site sections or pages, similar to zone or category targeting on an ad server.  Once a page is associated with keyword metadata in an ad tag, you can tell your ad server to target ads to that keyword on whatever page or pages the tag was placed. 
• Language.  When a language can be detected or known in advance, you can target ads to visitors in their language.
• Demographics. If the ad server is aware of a segment’s demographics, such as age, gender, income, title, purchasing power, and so on, an ad can be targeted on that basis. 
• Context.  Think of AdSense and how it matches text ads based on the semantics in site content.  Or when, after adding a product to your cart, a site offers you “free shipping” if your total purchase exceeds a certain price.  This is content targeting based on context.
• Profile.  Targeting is possible based on conclusions drawn and rules created from attributes about an individual or segment (such as purchasing propensity or job title).
• Rules.  Serve an interstitial ad only to visitors who don’t have a cookie set for the site.
• Events.  Someone deposits a large sum of money into his bank account, so the online banking site offers him a CD product on his next login.

We’ve all heard, of course, about a very specific type of often-discussed targeting in online advertising: “behavioral targeting.”  Behavioral targeting refers to the technology and process in which an ad or content is shown to a visitor based on their past actions and reactions.

Behavioral targeting involves:

1. Collecting behavioral data about visitors.
2. Identifying when those visitors visit a site.
3. Determining the current context of visitors on the site.  
4. Detecting the visitor’s current behavior.
5. Serving relevant ads (or content) matched to the behavior.

The goal being to use past behavioral data to influence the customer buying cycle or marketing lifecycle, in order to more effectively and more quickly deliver on advertiser and site goals.

So where does Web analytics come in?  You would think Web analytics data from “Web analytics” technology would be used to enabling “targeting.”  After all the best Web analytics systems store detailed visitor level data about past behavior.  Web analytics data certainly can be used, but in most cases, targeting is a function provided by the ad server or network, perhaps the ISP, or another technology called the “behavioral targeting platform,” not from data collected by the Web analytics tool.

In order to make Web analytics data useful for targeting, you will need to use your data to:

1. Define segments to target or identify visitors to target.
2. Feed past behavioral data about segments or visitors to the targeting technology.
3. Analyze segment and visitor performance against site or advertiser goals after targeting.

Targeting has a proven ability and amazing potential to generate tremendous returns, especially when combined with the rich, detailed behavioral data available in Web analytics.  As a method for optimizing site content and advertising, targeting technologies that integrate with Web analytics data will only become more important and a necessary “must have” for innovative companies that want to maximize business opportunities on the Internet. 

Every month I write a column for MediaPost’s Metrics Insider.  This month I wanted tackle my evolving take on Internet video measurement.  Very few companies offer solutions in this space.  Only a few are really differentiated.  Check out Visible Measures, NedStat, TubeMogul, Divinity Metrics, and the usual suspects, Omniture, Unica, WebTrends, ComScore, and Neilsen NetRatings. 

Here’s my column:

IN LATE 2007, THE DIGITAL Video Barometer Executive Survey indicated that more than 80% of media and entertainment executives believe tracking, measuring, and monitoring Internet video content is critical to bottom-line profit.  That’s not surprising. Accurate measurement informs decision-making and improves business performance, and Internet video is more mainstream and popular than ever before.  What may be surprising to those executives is that technology for measuring Internet video generally focuses on video content served on-site, not off-site.  It’s fairly straightforward for a Web analytics tool to tell you how people are consuming and interacting with on-site video, but consumption and interaction of videos distributed across multiple sites, perhaps virally or via social media campaigning, aren’t directly measurable by Web analytics tools.  Panel-based technologies can approximate certain off-site measures of video consumption and distribution, but don’t provide very deep on-site metrics. Measurements of Internet video consumption, interaction, and distribution may be categorized as follows:

• Instream measurement.  Refers to measuring the video itself and the various events and behaviors that occur during a video viewing experience, such as time-based duration metrics and interaction and behavioral metrics (for example, the number of stops, plays, pauses, rewinds, fast-forwards, sites that posted or syndicated the video, clicks on hotspots and social media features).
• Outstream measurement.  Refers to measuring the content environment and user experience surrounding the video on the site or in the skin, such as the conversion metrics (percentage of visitors downloading or viewing a video), source metrics (refers to the video page, players used), and content metrics (percentage videos viewed by topic, percent videos viewed by file type). 

Those categories form a framework for Key Performance Indicators (KPI’s) that help to identify how people interact with videos, how videos perform when compared to other videos, and against pre-defined business goals.  Analysis of KPIs enables video content to be tailored to maximize performance.  Example KPI’s include:

Instream KPI’s:

• Percent high, medium, and low duration video views
• Average viewing time per video
• Percent visitors who complete the video
• Percent visitors that stop the video within 10 seconds
• Percent visits when this video was the last video viewed
• Percent visits when this video was the first video viewed

Outstream KPI’s:

• Conversion rates by video, topic, channel, taxonomy node, referrer, geography, keyword, and so on
• Average video views per visit
• Percent visits/views from different channels (such as email/rss, organic search, paid search, direct)
• Average time between visits that include a video view
• Repeat visit rate for visits involving a video view or download

These KPIs are measurable using a Web analytics tool, and perhaps a few of them are possible using traditional panel-based measurement.  But if off-site video distribution creates a whole new set of challenges to using current analytics and audience measurement tools to track instream and outstream metrics and KPIs, what are publishers and advertisers to do?  It’s a business problem that demands a new technology solution for understanding audience behavior, consumption, and distribution patterns of off-site syndicated or viral video content.

So what would a new technology solution for measuring Internet video and audience behavior do?  First it would have to fill the gap between panel and census-based measurement systems in a way that helps both publishers and advertisers  – not just one or the other — understand audience reach, frequency, and behavior.  The technology must enable tracking and actionable reporting and dashboarding of key metrics and KPIs, distribution patterns, behaviors, and interactions regardless of where the video “goes” on the Internet.  Audience characteristics from external databases (like OpenID for example) and internal company databases (like subscription and registration dbs) should be able to be integrated with data collected about behavior, video metadata, and instream and outstream metrics. 

If measuring digital video is as important as eight out of 10 media and entertainment executives believe it to be, there are some huge money-making opportunities on the horizon — for companies that are already providing technology for tackling this emerging business need, for advertisers using Internet video to drive awareness and response, and for measurement professionals who can help make sense of the Internet video ecosystem, solve measurement challenges, identify significant business opportunities, and use video metrics to improve business performance.  We’re certainly at the beginning of the J-curve for Internet video measurement for both publishers and advertisers.  After all, Forrester predicts Internet video advertising spend to increase from $471 million last year to $7.1 billion in 2012. 

About a year ago, I wrote a guest blog post over on Robbin Steif’s blog about using Google Analytics for tracking Javascript and Flash events.  This weekend Jeremy Geelan, SVP over at Sys-Con Media, asked if he could republish the work.  Of course I said “yes.”  Then I noticed that a lot has happened to GA in a year (and more to come, ahem, API’s!).  What I had wrote was now incomplete, so what you’ll find below is my attempt to sum up “event tracking” using ga.js and the Great Google’s Event Tracking Data Model.  Let me know how I did covering it, and if you think I should clarify of expand on anything.

Since we all know about page tags, let’s get down to business with “the Google” and how it tracks “the Rich Media.”  Google Analytics currently has two different javascript page tags:

• urchin.js.  The legacy version of the Google Analytics page tag.
• ga.js.  The current, rebranded version of the Google Analytics page tag.

How you track rich media depends on which page tag you are using.  I’ll discuss using urchin.js first, then ga.js.  I’ll also provide some information about Google’s Event Tracking function for capturing specific “events” within their event architecture.

Tracking Rich Media using Urchin.js

In the legacy version of Google Analytics, the smarties at Google created a little JavaScript function called urchinTracker() that enables event tracking.  Use the JavaScript function with an argument specifying a name for the event. For example, the function:

javascript:urchinTracker(’/mysite/flashrichmedia/playbutton’); 

logs each occurrence of that Flash event as a page view of:

/mysite/flashrichmedia/playbutton

Some caveats:

1. Always use a forward slash to begin the argument.
2. Actual pages with these filenames do not need to exist.
3. You can organize your events into any structure or hierarchy you want.

Important: Google says to place your tracking code “between the opening tag and the JavaScript call” if your pages include a call to urchinTracker(), utmLinker(), utmSetTrans(), or utmLinkPost(). For example, if the page view is the major event and the “play” event a minor event; then, your hierarchy would be Page View > Event, where the page contains an event, such that:

/mysite/ria_bittons/playbutton
/mysite/ria_bittons/pausebutton
/mysite/ria_bittons/playbutton
/mysite/ria_clips/clip

Some examples of the code (from Google Help):

on (release) {
// Track with no action
getURL(”javascript:urchinTracker(’/folder/file’);”);
}

This one above tracks when you click and release (although technically, it just notices the release) of a flash button (and records the file you specify as a page view).

on (release) {
//Track with action
getURL(”javascript:urchinTracker(’/folder/file’);”);
_root.gotoAndPlay(3);
myVar = “Flash Track Test”
}

The second one is the same, but by using a function, passing it a parameter, and identifying the instance you want to track, you can measure when your file was used in a specific scene in a little flash movie. So it is a more specific method for handling event tracking in Flash.

onClipEvent (enterFrame) {
getURL(”javascript:urchinTracker(’/folder/file’);”);
}

And the third one repeats the action throughout the movie so that each time the file is loaded, it gets tracked as an event. If you were to pass a unique file at the end of the movie, you could recognize it using this method (or the other methods) to know that the whole movie was watched (as long as your session doesn’t time out). Next, wait until Google updates your analytics, then check the Top Content report to see if it all worked. Now let’s discuss how to the exact same thing using the new trackPageview function released with ga.js.

Tracking Rich Media using ga.js

In the current version of Google Analytics, the brainiacs at Google created a little JavaScript function called trackPageview() that enables event tracking.  Use the JavaScript function with an argument specifying a name for the event.For example, the function:  

javascript:pageTracker._trackPageview (“/mysite/flashrichmedia/playbutton”);

logs each occurrence of that Flash event as a page view of:

/mysite/flashrichmedia/playbutton

Some caveats:

1. Always use a forward slash to begin the argument and use quotes around the argument.
2.  Actual pages with these filenames do not need to exist.
3. You can organize your events into any structure or hierarchy

You must put calls to _get._getTracker and _initData above the call to _trackPageView.  For example, you would insert the following code:

<script type=”text/javascript”>
var pageTracker = _gat._getTracker(”UA-xxxxxx-x”);
pageTracker._initData();
pageTracker._trackPageview();
</script>

Here are some examples of the ga.js code (from Google Help) that replicate what I described above using the most recent code:

on (release) {
// Track with no action
getURL(”javascript:pageTracker._trackPageview(’/folder/file.html’);”);
}

This one above tracks when you click and release (although technically, it just notices the release) of a flash button (and records the file you specify as a page view).

on (release) {
//Track with action
getURL(”javascript:pageTracker._trackPageview(’/folder/file.html’);”);
_root.gotoAndPlay(3);
myVar = “Flash Track Test”;
}

The second one is the same, but by using a function, passing it a parameter, and identifying the instance you want to track, you can measure when your file was used in a specific scene in a little flash movie. So it is a more specific method for handling event tracking in Flash.

onClipEvent (enterFrame) {
getURL(”javascript:pageTracker._trackPageview(’/folder/file.html’);”);
}

And the third one repeats the action throughout the movie so that each time the file is loaded, it gets tracked as an event. If you were to pass a unique file at the end of the movie, you could recognize it using this method (or the other methods) to know that the whole movie was watched (as long as your session doesn’t time out).

Tracking Rich Media using Google Analytics Event Tracking

When Google released ga.js in fourth quarter 2007, Google also released a data model for tracking events.  It provides more flexibility and ease of customization than the methods I described above.   The data model makes use of:

• Objects. These are named instances of the eventTracker class and appear within the reporting interface.

var videoTracker = pageTracker._createEventTracker(”Movies”);

• Actions. A string you pass to an event tracker class instance as a parameter.

videoTracker._trackEvent(”Stop”);

• Labels. An optional parameter you can supply for a named object.

downloadTracker._trackEvent(”Movies”, “/mymovies/movie1.mpg”);

• Values. A numerical value assigned to a tracked object.

To set up event tracking you should:

1. Identify the events you want to track.
2. Create an event tracker instance for each set of events.
3. Call the _trackEvent() method on your page.
4. Enable “event tracking” in your profile.

To instantiate an event tracker object, you might do something like this:

var myEventObject = pageTracker._createEventTracker(”Object Name”);
myEventObject._trackEvent(”Required Action Name”, “Optional Label”, optionalValue);

createEventTracker() is order dependent and must be called after the main tracking code (ga.js) has been loaded.Next you would call the _trackEvent() method in your source code either on every page that contains the event or as part of the tracking code for every page:

_trackEvent(action, optional_label, optional_value)

If you wanted to track interaction with the Flash UI, such as the button on a Flash Video Player, you would create a videoTracker object with name “Video”:

var videoTracker = pageTracker._createEventTracker(’Video’);

Then, in your Flash code for the video player, you would call the videoTracker object and pass a value for the action and label for the event:

onRelease (button) { 
   ExternalInterface (”javascript:videoTracker._trackEvent(’Play’, ‘MyVideo’);”)
}

You could also use the ExternalInterface ActionScript function as an eval() function to parse FlashVars and attach them to every Flash UI element that needs a tracking action.  For example, the code below associates a Stop action for the Video object and retrieves the provided label and value from the FlashVars:

onRelease (button) { 
   ExternalInterface (”javascript:videoTracker._trackEvent(’Stop’” + label + “,” + value + “);”)
}

Adding event tracking code would generate event reports in the Content section of the Google Analytics Interface.  Pretty cool stuff, Google!

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? 

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?

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 began my career in “information retrieval” back in the 1990’s when there was no Google. At a little startup