What’s the future hold for Web Analytics in 2008?  Here are a few predictions:

• Google Analytics releases a real API for getting (and perhaps setting) data.  As you know, I think GA is a fine tool for web analytics, but has severe limitations when you want to control over your data or to feed data into other systems.  Thus, I predict Google Analytics will go beyond the “Tracking API” and release a real API that allows you to at least get data out of the tool (if not set data as well).  Think of what Feedburner does with their REST-based Awareness API.  Wouldn’t that be nice to have with GA?!
• HBX Analytics goes away.  I’d be more than a bit nervous if I were an HBX customer because Omniture is going to sunset HBX and migrate everyone to SiteCatalyst, then try to aggressively sell them the rest of the suite. 
• Long live Visual Sciences.  VS is a powerful tool quite superior in some regards and very different than anything else Omniture offers.  It’s also real in-house software, not some blackbox.  VS’ extensible schema, flexibility in reporting, scalability, and performance is quite unparalleled in the industry.  I can’t envision Omniture killing it (unless they peel it apart in order to create Discover 3), like they will HBX. 
• WebTrends rebrands.  I’m not sure if you agree, but imho WebTrends Marketing Lab was an attempt to rebrand WebTrends.  I expect that interim management will continue attempting to differentiate WebTrends by rebranding products and perhaps the entire company.
• New and updated standards are released.  As a member of the IAB’s Measurement Council I can tell you that the IAB is getting ready to release the IAB Audience Measurement Reach Guidelines, which attempt to clarify and take a stand on various aspects of server/client-side analytics and audience measurement.  I also envision the WAA increasing the number of terms they define.  But standards are just dandy and quite meaningless unless they are adopted… thus…
• Standards enforcement is attempted in order to propel adoption. Existing and forthcoming standards will be enforced in 2008.  Enforcement from the WAA will probably come in the form of a publication of a matrix or documentation citing which vendors adhere to the standards and to what degree, what’s missing, what’s different, and so on.  If decision-makers who control budgets believe in standards, this type of document will cause the question ”do you adhere?” to be asked.  If vendors start losing deals because the answer is “no, not at all,” vendors will adopt the standards. 
• Internal data integration becomes more important for companies and problematic for ASP’s.  When we talk about “integration” I often think people can be a bit shortsighted.  They want to integrate data from other third-party services and tools (like Salesforce.com and their ad server).  While there is certainly real value in integrating external data with web analytics data, significant value comes from integrating web analytics with internal data, such as data residing in internally-hosted CRM systems, finance, subscription, and lead generation databases. Most vendors have barely figured out how to deal with detail-level external data integration in 2007, even though many customers are demanding it.  I expect that in 2008, internal data integration will be more commonly demanded and even more problematic for ASP’s. 
• BI tools provide better support for and integration with Web Analytics tools.  The current allotment of “enterprise” level web analytics tools are inferior to the capabilities provided by business intelligence tools from companies like Business Objects or Cognos.  Expect these BI vendors to create features for dealing with web analytics data in 2008.  Either that, or these web analytics tools need to grow up and learn a few things from BI. 
• Web Analytics as performance management.  KPI-based site optimization means using data to guide the modification of user experience to deliver on goals.   Since goals are measurable and can be plotted against performance, it’s totally logical to use web analytics as a performance management tool.  Expect to see that gestalt in tool usage come into vogue and be discussed more in 2008. 
• Web Analytics as part of business process automation.  Having the marketing department fielding page tags with campaign codes may work for some (small) companies, but when you work for an enterprise with thousands of clients and simultaneous campaigns across multiple channels, endemic tagging and subsequent tool configuration becomes challeging.  As part of the web analytics process, I expect to see tools support some level of business process automation enabling web analytics.
• Features for measuring the Mobile Web.  Right now, with a log file based tool, I can segment out Mobile traffic based on user agent.  If I want to use a page tag, I have to consider js limitations.  The mobile web is the next frontier, and I only know of one web analytics vendor who is doing a decent job measuring it right now, so I expect to see more features released this year for measuring Mobile.  

So that’s that.  Like a band named PIL once said in the song called Rise “I could be wrong, could be right!”  Am I off-base, misguided, accurate, do you disagree, agree, then let me know… I’d love to hear your thoughts and your predictions for Web Analytics 2008…

Every month I write column for MediaPost.  This month I wrote  a short summary piece I thought I’d share with you in case you missed it.  Here it is:

As 2007 ends, I thought it worth looking back, from the practitioner perspective, at just a few of the issues that have shaped Internet measurement and thus online metrics over the last year:

• The Page View is Dead, Long Live the Page View.  During 2007, technologies like AJAX and Flash continued to erode the construct of the page view.  These technologies render content in a browser but do not always make requests to the server to do so.  If the technology you are using to measure behavior requires the page request and you do not have a page request, what do you measure?  The major vendors of online metrics tried to answer that question. 

Various audience measurement companies claimed “total minutes” and other time-based derivatives were better alternatives to measuring the page view.  Web Analytics companies rolled out features for measuring “events” subordinate or equal to the page view (and highlighted already existing time-based metrics).  Ad serving companies made inroads in reconciling view-through to assist advertisers in understanding the latent effect of ad exposure on the purchasing lifecycle.  Yet all these technologies still count and report page views.

• Engagement, Engagement, Engagement.  One of the hot topics in 2007 was a carryover from 2006.  Definitions for “engagement” emerged from the worlds of advertising, social media, online metrics, and more.  Engagement has been described as “turning on a prospect to a brand idea enhanced by the surrounding context” to “repeated, satisfied interactions that strengthen the emotional connection a customer has with the brand” to “apparent interest” to the more metrical “estimate of the degree and depth of visitor interaction against a clearly defined set of goals.” 

“Engagement” is very specific to the site being measured and full of nuance.  This fact has led agencies, consultants, and various companies to create complex engagement indices consisting of measures of key behaviors.  Behaviors are tallied and segmented in order to calculate an engagement metric, which is then used as the basis for site optimization.  These indices go far beyond often-cited simple time-based measures of engagement.  For a well-thought-of example, see Eric Peterson’s Engagement Metric.

• Cookie Deletion, Again!  Jupiter Research, in 2005, first uncovered and quantified how cookie deletion can affect unique visitor numbers in web analytics systems.  The effect of cookie deletion is not quantifiable in the basic way audience measurement companies want to quantify it in 2007 – by only examining cookie deletion rates from self-selecting panelists who visited one portal site and an ad server. 

Cookie deletion behavior varies greatly by audience segment and by site.  It may be as much of an accuracy problem in web analytics as selection bias and coverage errors are in panel measurement.  It is worth noting that some audience measurement firms use cookies to collect panel data. 

• Black Box Audience Measurement.  Many questions were asked about whether audience measurement companies adequately measure “unique visitors” or “unique users” or just the frame of self-selecting “unique panelists.”  In audience measurement, counts of “unique visitors” are generated using complex, black-box mathematics that project observed metrics to the entire online universe.  The projections are always unequal to the same data provided by other audience measurement companies or web analytics tools.  Panel inconsistencies (across at-home, at-work, at-university, or specific to the geography being measured) may cause some level of bias and error. 

Accounting for the difference between actual, observed panel metrics and projected metrics is perhaps even more challenging to clarify and resolve than the measurable effect of cookie deletion. 

• The Continuing Need for Standards Enforcement.  2007 was the year two significant industry bodies continued working on standards related to online metrics: the Internet Advertising Bureau and the Web Analytics Association.  While each organization serves the needs of different constituencies, they both share the inability to enforce standards.  Both bodies can say what you should do, but not what you “must” do. 

Throughout 2007, these issues (and others) brought increased attention and scrutiny to online metrics.  Corporations are inextricably linking online metrics to site and channel strategy and performance, and thus to overall corporate profitability.  The “numbers” are now more important than ever for managing an online business and maximizing online revenue.  Nevertheless, questions are still being asked about accuracy, precision, usage, and sources of online metrics.  We have a ton of collaborative work to do in 2008 to provide the best answers and numbers we can. 

Happy New Year!

In wild world of online metrics, it’s a well known fact that metrics from web analytics tools and ad servers never match. Variances can be substantial. 

What I mean is that, given no “refresh rate,” the total impressions for a single ad unit, which should be served on every page request, never matches the number of total page views on the site during the same period of time.  Sigh.

Reasons why identically-named metrics from these two tools (like page views and unique visitors) don’t add up are numerous:

• Different data collection methods.  Ad servers use page tags.  Many web analytics tools use page tags, but it’s not uncommon in web analytics to use additional methods, such as logs or packet sniffers.  The methods have no shared standards for collection or storage of the same data (like visit-level data).  Thus you get apples to strawberries comparisons when attempting to correlate the dimensions from different systems.
• Unique data models.  Ad servers aren’t focused on counting page views and the other dimension of web analytics (visits, time, and so on).  Rather ad servers focus on serving and counting impressions served (and loads of related derivative calculations, like CTR, CPC, and the coolness of view–thru).   Metrics are based on an ad request and an ad code.  Ads aren’t targeted to a page (though that’s possible), but rather to a “zone” or “keyword.” What that means is that “page” dimension may not even exist in your ad server’s schema.  In other words, you aren’t looking at impressions measured on a page, but rather at the number of impressions served in a different conceptual construct.  That’s one of the reasons why people say metrics and ad-serving systems “don’t measure the same thing.” 
• Untagged pages.  Just like analytics implementations suffer from challenges related to complete code coverage of page tags, so do ad serving implementations.  Companies need to determine how to centrally manage the deployment and orchestration of page tags *of all types* and verify all the pages have tags!  Don’t just expect it to work because tagging sounds so easy!  Suspect it won’t work, and determine what you’re going to do *before* you deploy.  Too late?  Time to reengineer. 
• Non-JS executing clients.  Ad servers use page tags.  Not everyone and not all user agents execute javascript.  Everyone needs to realize that page tagging misses traffic as efficiently as it excludes it.  Period.  What percentage of the traffic you miss, you’ll never know… running and filtering your logs may provide an indication…
• Ad blocking software.  Firefox’s Adblock Plus software is a big problem for sites that have a big techie audience, and it affects all sites.  Check your browser reporting and realize a good majority of those Mozilla users may be blocking your ads.  Look at the attitudinal data you have about visitor’s to gauge whether that’s a big issue for your online audience. 
• Cookie issues.  Third-party cookies get blocked (often by privacy software).  Many ad servers still serve third party cookies, and many corporations have not tricked their DNS to accommodate this issue (ahem, CNAME).  We all know how cookie deletion affects unique visitor counts.
• Refresh rates. One page rendered in the browser and many banner “refreshes” makes it really hard to correlate page views and impressions served.
• No rich media installed, and no fallback.  If the client doesn’t have certain plug-ins, and you have no fallback, you miss ad revenue.  Meanwhile the tag executes and you count the traffic.
• Robots, spiders, and crawlers, oh my.  The web is so robotic.  The problem is amazingly understated, especially by companies who want to bill you on page views.  Different data collection methods allow some level of bots to dirty the data.  Logs are harder to efficiently filter.  When the ad server uses tags, and the analytics tool uses logs, you may get some wildly different numbers. 
• Mobile, Mobile, Mobile, Mobile.  Not all Internet-connected mobile devices will display ads, but web analytics tools will track the behavior of mobile visitors.
• Latency.  Visitors who move through the site too quickly may not execute the tag, thus no data is sent back to the server(s).  Ever wonder why vendors tell you to put the tag “high” on the page?

The influence these issues have on your site varies depending on audience.  Investigate factors causing variance and deviation between metrics systems, and educate your audience on why the numbers differ.

The concept of web analytics data sources isn’t discussed nearly as often as web analytics “data collection.”  With that in mind, I’m often asked by people just beginning to explore the wild and wonderful world of web analytics  “where does this metric come from?” 

When people ask me that question I often think back to someone who I may have seen walking in the local village a few miles away if I had lived about 170 years ago.  Ralph Waldo Emerson.  The father of American Transcendentalism once asked “whence is the flower?”  From where did it come from?  Ralphie-boy was questioning the accuracy of long-standing 19th century beliefs.  Keep in mind Mr Emerson parted ways with Harvard for questioning the Trinity (not Avinash’s ;)!

In that spirit, people are really asking me “whence is the online metric?”—from where does the metric come from?  Advertisers and your colleagues want to know because they may be questioning the origin and accuracy of our 21st century metrics! 

While folks who have been “doing web analytics” for some time know how to answer that question, I thought it would useful to blogivate on data origination for folks who are new or aren’t dealing with web analytics in full-time, day-to-day role. 

The answer is that online metrics originate from one of three data sources:  internal data sources, external data sources, and hybrid data sources.  Each source has its own particular challenges to “accuracy.”

• Internal data sources.  Web analytics technology that collects data from websites, mobile phones, or other Internet-connected devices via javascript page tagging, log file processing, or packet sniffing falls into this category.  Major vendors include Unica, Visual Sciences, Omniture, CoreMetrics, Webtrends, Google Analytics, and others.  The issue with Web analytics tools is that two tools will yield different numbers for the same data source.  That’s because each tool has its own “secret sauce” for “sessionization.”  That is, each tool counts traffic in slightly unique ways.  For example, tools may be configured to include or exclude certain filetypes or server responses.  Robotic traffic may or may not be filtered.  Web Analytics tools also depend on cookies for attributing “uniqueness” to visitors; thus, cookie deletion can overinflate unique audience numbers. 
• External data sources.  Data collected from panels, toolbars, and ISP’s–not from the actual site– are examples of external data.  Companies like Comscore, Neilsen Netratings, Compete, and Hitwise provide metrics generated from external data.  These companies provide some sort of incentive (i.e. gifts) or perceived value to entice people to participate in panels and download software that observes their Internet usage.  Self-selecting panelists are monitored, and metrics related to their behavior are projected to the entire online universe using statistical methods.  External metrics are never identical with each other because of differences in consistencies of their panels.  As you’ve probably noticed, Comscore data does not match Nielsen.  Significant divergences across panel-based measurement systems when compared to each other and web analytics tools has led to an audit by the IAB and MRC.  The hope is that auditing will vet methods and identify any potential coverage error or selection bias inherent in sources of external data.  Personally, I’m really curious to see what all the hubba bubba about auditing without guiding standards will really accomplish.
• Hybrid data sources.  Sources of metrics that use some type of internal data collection and some form of external data collection are considered hybrid data sources.  Microsoft Gatineau and Quantcast offer free services that fall into this category.  Reports in the highly anticipated web analytics offering from Microsoft include data collected from Javascript page tags combined with anonymous demographic data from Microsoft Live profiles.  Quantcast’s panel-based measurement system (i.e. external) may be augmented by adding a javascript page tag (i.e. internal) to every web site page.  Behavioral data is collected via javascript and combined with demographic data from the Quantcast panel, then reported to end-users.

In real world practice, companies use many of these different, overlapping data sources to understand their online presence.  Given the free nature of companies like Quantcast and Compete, and the pervasiveness of firms like Neilsen and ComScore in sales and agency cultures, expect that different sources regardless of type will never ever absolutely match site-specific Web analytics tools.  And that’s okay because data from all these data sources have different utility:

• Metrics from internal data sources derived from web analytics tools are very useful to site owners for identifying the behavior of their online audience.  The data can be used for site optimization, to understand what sites are referring traffic to the site, to identify conversion rates for particular marketing campaigns, to understand the broad content themes and particular search keywords driving traffic on your site, to segment and give context for other metrics and attitudinal data and more.  Metrics from site-centric sources should be provided to advertisers for comparison with external data.  Be prepared to discuss why the numbers differ!
• Data generated from external sources are useful to advertisers and agencies.  These metrics can be used for comparing the performance of a site to its competitors and for understanding audience behavior across one or more sites by demographics.  Media buyers and web strategists desiring to understand generalized Internet traffic trends and measures of site popularity use external sources.  Metrics from “audience measurement” sources should be used for comparison with internal data.  But the data won’t match, and that’s okay because you should be looking at the site performance of your competitors, not using that data to optimize your site.  Use the external data for insights into demographic makeup of your audience.  Then compare that information to data from your own internal research teams (who don’t report to web analytics). 
• Numbers from hybrid data sources blend both external and internal metrics together for both site owners and advertisers.  No duh, ay?  New insights about the online audience can be realized from segmenting visitors based on demographic and behavioral data within the same source.  We’re just entering the “early adopter” phase of this market, so I’m curious to see how it all plays out.  How will Microsoft Gatineau differentiate its hybrid analytics service and communicate the value proposition?  Will publishers adopt Quantcast’s hybrid service (and how will they make money)?  One barrier to adoption is that some companies have already combined web analytics behavioral data with audience demographic data using business intelligence and data warehousing technologies (or the more flexible and open web analytics tools that suppor open software standards).   Companies bridging data internally hope to enable a grand orchestration of automatic site optimization and content targeting (with an eye toward behavioral targeting).  Microsoft already seems to be doing this targeting to some degree on based on what I learned about “personamous” content targeting on MSN.com (at Emetrics).  So I’m curious what other online products (for example advertising offerings and site optimization technologies) the gentlemen at MSFT have up their strategic sleeves based on this hybrid source data. 

As you immerse yourself in the wondrous world of overlapping, hardly-standardized, online metrics, it’s critical to consider the source of the metric, bias from the source, and the audience for the metric.   Most critically, understand how a metric, regardless of source, relates to business goals and site objectives before using it as measure for identifying your online performance to internal and external stakeholders and taking action from your insights. 

GO RED SOX! 

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.

Web analytics standards are few and far between, which is why I’m glad to blogivate about the Web Analytics Association’s recently released standard definitions for 26 web analytics metrics.  I’m curious to see how the world will respond to these basic definitions.   Standard vocabulary and definitions educate new practitioners, enable consistency in discussions, and lead to shared understandings that foster and promote innovation.  IMHO, the web analytics industry can only benefit from standards.  I certainly think they help to:

• Clarify misunderstanding and prevent confusion.  As the Internet continues to “go mainstream” and more money is invested in the “online channel,” the capital markets will continue to scrutinize and demand consistency in measurement.  The WAA standards set a new baseline for discussing internet measurement. 
• Align other companies and bodies and people expressing standards and using non-standard vocabulary.  If the WAA definitions reach a tipping point through broad industry adoption, other standards-setting bodies and industry organization will adopt and follow suit.  However
• Create a shared vocabulary.  It is not uncommon to hear references to objects in web analytics that are archaic (pages served), industry-specific (page impressions), or conceptually obsolete for certain goals (the number of “hits” as an indicator of site success).  The “names of things” are different across competing technologies.  I hope this document furthers discussion and leads to a common, shared global web analytics vocabulary.

So what are these new standards, you ask?  Here is the standard vocabulary (thanks to my friend Avinash Kaushik whose digitization of the document I have cut and pasted here :

• Building Block Terms: Page, Page Views, Visits, Unique Visitors, New Visitor, Repeat Visitor, Repeat Visitor & Returning Visitor
• Visit Characterization: Entry Page, Landing Page, Exit Page, Visit Duration, Referrer, Internal Referrer, External Referrer, Search Referrer, Visit Referrer, Original Referrer, Click-through, Click-through Rate/Ratio, Page Views per Visit
• Content Characterization: Page Exit Ratio, Single-Page Visits, Single Page View Visits (Bounces), Bounce Rate
• Conversion Metrics: Event, Conversion

Brief definitions for all these web metrics are listed below.  Make sure you download and read the full document.  There’s a lot more to it than listed below:

• Page: A page is an analyst definable unit of content.
• Page Views: The number of times a page (an analyst-definable unit of content) was viewed.
• Visits/Sessions: A visit is an interaction, by an individual, with a website consisting of one or more requests for an analyst-definable unit of content (i.e. “page view”). If an individual has not taken another action (typically additional page views) on the site within a specified time period, the visit session will terminate.
• Unique Visitors: The number of inferred individual people (filtered for spiders and robots), within a designated reporting timeframe, with activity consisting of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the reporting period.
• New Visitor: The number of Unique Visitors with activity including a first-ever Visit to a site during a reporting period.
• Repeat Visitor: The number of Unique Visitors with activity consisting of two or more Visits to a site during a reporting period.
• Return Visitor: The number of Unique Visitors with activity consisting of a Visit to a site during a reporting period and where the Unique Visitor also Visited the site prior to the reporting period.
• Entry Page: The first page of a visit.
• Landing Page: A page intended to identify the beginning of the user experience resulting from a defined marketing effort.
• Exit Page: The last page on a site accessed during a visit, signifying the end of a visit/session.
• Visit Duration: The length of time in a session. Calculation is typically the timestamp of the last activity in the session minus the timestamp of the first activity of the session.
• Referrer: The referrer is the page URL that originally generated the request for the current page view or object.
• Internal Referrer: The internal referrer is a page URL that is internal to the website or a web-property within the website as defined by the user.
• External Referrer: The external referrer is a page URL where the traffic is external or outside of the website or a web-property defined by the user.
• Search Referrer: The search referrer is an internal or external referrer for which the URL has been generated by a search function.
• Visit Referrer: The visit referrer is the first referrer in a session, whether internal, external or null. 
• Original Referrer: The original referrer is the first referrer in a visitor’s first session, whether internal, external or null.
• Click-through: Number of times a link was clicked by a visitor.
• Click-through Rate/Ratio: The number of click-throughs for a specific link divided by the number of times that link was viewed.
• Page Views per Visit: The number of page views in a reporting period divided by number of visits in the same reporting period.
• Page Exit Ratio: Number of exits from a page divided by total number of page views of that page.
• Single-Page Visits: Visits that consist of one page regardless of the number of times the page was viewed.
• Single Page View Visits (Bounces): Visits that consist of one page-view.
• Bounce Rate: Single page view visits divided by entry pages.
• Event: Any logged or recorded action that has a specific date and time assigned to it by either the browser or server.
• Conversion: A visitor completing a target action.

In order for broad-based adoption and continued relevancy of these standards, I encourage the Web Analytics Association to: 

• Create broad consensus and agreement.  I was surprised the Web Analytics Association didn’t release these standards for comment to the larger membership and the public before releasing these standard definitions.  While I support the standards, I fear the perception of “dropping” standards on practitioners and vendors without providing a period for public commentary may slow adoption as people grumble about the nuances of the language.  After all, not all vendor’s tools or reporting comply exactly to the subtleties in these standards.
• Necessitate adoption by vendors and practitioners.  The old American expression says you say “po-tay-toe” I say “po-tah-toe;” I say “to-may-toe” you say “to-mah-toe.”   For broad adoption and usage of these standards, vendors need to integrate this vocabulary into graphical interfaces, reporting, documentation, training programs, and marketing messaging.  Consultants and practitioners need to “talk the talk.”  The Web Analytics Association should think about creating a “standards certification” program to verify adherence by certain companies and consultants.
• Identify compliance by vendors.  Current vendor vocabulary doesn’t conform to the standards, and there is currently no persuasive argument for vendors to adopt the definitions and modify their offerings.  The WAA needs to let the public know which vendors comply and which don’t and to what degree!
• Go beyond definitions to focus on interoperability.  Systems integration requires more than just definitions.  I’m looking forward to when these standards are described in XML.

Excellent job, Web Analytics Association!  If you haven’t joined, you should!