Last week at Online Marketing Summit (OMS) I had the pleasure of sitting in a panel of web analytics professionals, along with Eric Peterson, Matt Belkin from Omniture, Amanda Kahlow, Bill Bruno and Enrique Gonzalez from AARP.

First, I need to congratulate the folks from OMS for putting together a great show. There was a record attendance of over 800 professionals covering all areas of online marketing, along with a great lineup of presenters.

During the panel discussions, one of the questions asked was how should businesses deal with multi-touch attributions.

Here’s a sample scenario to help explain the pain point involved:

A visitor is interested in running shoes and conducts a Google search for the term “running shoes”. The visitor is presented a number of search ads from competing vendors such as Nike, Adidas, and others, and decides to check out Nike and Adidas sites. The visitor gets intrigued by the Nike ID line of products and decides to conduct some further research, and even registers for the Nike newsletter. While doing research on third-party sites, the visitor sees a banner ad for the Nike ID site and clicks the banner. Finally a day later the visitor gets an attractive email offer from Nike and ends up buying the shoes.

In this scenario, the visitor has been exposed to three separate campaigns. The “running shoes” search campaign generated the awareness. The banner campaign possibly helped increase awareness and instill further trust in the product and finally, the newsletter sealed the deal. By default, web analytics providers give credit to the last campaign touched by the user. In our example, the newsletter campaign will get the credit, whereas if it wasn’t for the search campaign, the visitor would not have even been aware of the Nike ID line. In fact, two variables that make multi-touch attribution a real challenge are:

• Number of simultaneous campaigns. If you’re a company running large numbers of campaigns in parallel, you should account for multi-touch attribution
• Complex or expensive product: the more complex the product, the longer the consideration and therefore the more likely you are to have multiple touch points.

So how does one tackle this challenge? First, for the large companies running many campaigns, there are a number of commercial solutions such as ClearSaleing that help solve this challenge (and a lot more). But what about smaller companies with small budgets using free solutions such as Google Analytics or Yahoo! Web Analytics?

First, we recommend that you investigate if you even have a multi-touch attribution problem. How? Let’s take another look at our example scenario. Two metrics within your analytics solution can give insight into this. They include time to purchase and number of visits prior to purchase (or conversion).

For example, if you use Google Analytics and have e-commerce tracking, you can use the “Visits to Purchase” report to see how many times do visitors come to your site prior to purchasing. If you are a lead generation type web site and have your conversions set up as goals, you can use the default “Visits with Conversions” segment and look at the loyalty report for the segment. In both cases, if most of your conversions come from first-time visitors, then multi-touch attribution is not going to be a problem for you and the rest won’t apply.

However, if you happen to see a big difference between converting visitors and others, then you can build a quick attribution report by following these steps:

• Create a Javascript that captures your marketing campaign parameters into a persistent cookie
• The JavaScript should also be configured to append campaign values together, as visitors go from one campaign to the next
• Push the cookie value into a custom variable  – such as a visitor-centric custom variable in Google Analytics, a session-based custom field in Yahoo! Web Analytics or an eVar in Sitecatalyst.

You now have a simple yet powerful solution for seeing which campaigns your visitors are responding to, but also in what order.

Happy analyzing.

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We are pleased to announce the availability of version 2 of Tealium Universal Tag. The new version provides many new enhancements following several enterprise-level web analytics deployments with large number of platforms, including SiteCatalyst, Omniture Insight, Google Analytics, Yahoo! Web Analytics, Unica NetInsight, Webtrends and Coremetrics, as well as a number of digital marketing solutions such as DoubleClick, Atlas, ForeSee Results and more.

Some of the new functionality include:

• Improved multi-vendor support: the new version provides a superior method for complex implementations with multiple vendors. For example, non-technical users can map page tag values differently into various web analytics solutions, while also mapping them to their PPC bid management tool.
• Attribution management: designed specifically for clients using multiple affiliates, version 2 of Tealium Universal Tag has the ability to conditionally send data only to the winning affiliate(s).
• Multi-currency support: the new version of Universal Tag supports transactions in multiple currencies for digital marketing vendors that do not provide such support by conducting on-the-fly conversions to the supported currency.
• Universal data capture: this feature allows non-technical users to automatically capture data elements from the page and map them to their web analytics and digital marketing solutions. Examples of such data elements include microformats, meta tags, in-page style elements, query parameters, cookie values, etc.

We’ll be publishing a number of case studies on Universal Tag deployments soon. In the meantime, to see Universal Tag in action, please contact us.

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One way to make web analytics actionable is to break the site into different sections (such as home pages, category pages, etc.) and generate reports specific to those pages/sections. In this post, we’re going to identify some of the most common reports for analyzing home pages.

First, lets start by defining home pages and their goals. The home page is typically the main gateway page for your site. It’s the first impression that your visitors will have of your site. Its role is to showcase your offerings, your value proposition and provide quick access to the most popular or important sections of your site. For this reason, web analytics should help you answer some of the following questions:

• How effective is the home page at directing visitors to product pages?
• Which part of the home page is the most effective?
• Is the home page effective at enticing visitors to learn more?

Based on these, below are some popular web analytics reports for home page analysis along with the explanation:

• Bounce Rate
• Micro Step Conversion Rate
• Conversion Rate
• Acquisition Sources
• Home Page Real Estate

Bounce Rate

The bounce rate is defined as the number of bounces (single page visits) divided by entries. It shows you what percentage of the traffic landing on the page bounces and does not view any other page on the site. It is a reflection of the home page’s ability to retain visitors. Clearly the goal is to make changes to the home page and lower the bounce rate. It’s probably one of the best reports to look for when analyzing home pages. This report is widely available in most web analytics tools such as Google Analytics, Yahoo! Web Analytics and Unica NetInsight.

Micro Step Conversion Rate

Although the ultimate goal of your site is to drive conversions, we recommend micro step conversions as a better way to assess home pages. The goal of your home page is to drive people to your product description pages. It’s at that level that you do the selling. For this reason, when assessing the success of your home page, it should be around its ability to get visitors to those ensuing pages. You can get this in a number of way. Inside tools such as Yahoo! Web Analytics and SiteCatalyst, you can tag your product description pages as events and look at the success of your home page around this event. In Google Analytics, you can create a goal for your product pages, as long as the pages have a consistent nomenclature. If not, you can create an advanced segment for your product pages and look at the home page traffic for the segment. Such metrics can pretty easily be created inside Unica NetInsight and Webtrends.

Conversion Rate

Yes, this should not be your primary report for home page analysis, but you can still use this report as a tie-breaker. For example, if two versions of home have similar bounce and micro step conversion rates, then you can use the overall conversion rate to see if one version does in fact do a better job. Unfortunately, we often see that many people use conversion rate as the primary report for assessing home page effectiveness.

Acquisition Sources

Want to lower your bounce rate? One place to start is by looking at the acquisition sources. You can start with the sources of traffic to your home page and look at their respective bounce rates. Start with referring sources with high bounce rates. Often, you’ll find a messaging gap between the referring sites and your home page. The referring site may be saying something while your home page could be promoting something else. While you cannot optimize your home page for all referring sites, you can start with those with high traffic and high bounce rates and provide messaging on your home page that helps retain this incoming traffic. You’ll typically find that a handful of sites may account for a high percentage of your bouncing traffic.

Home Page Real Estate

To understand the real estate effectiveness, you’ll have to look at the click activity on the page. Rather than looking at all page links, we recommend classifying the link into sections or categories (such as as header, footer, navigation, left box, right box, etc.), and analyzing the activity by such sections. This is different than the default site overlay that you typically get from web analytics tools and requires some additional configuration to get proper reporting. For example, if you’re using Google Analytics we recommend using Event Tracking to track the activity on various sections and links within sections. You can then see how effectively each section and each link gets visitors to product pages and to final conversion.

You can also investigate some of the in-page analytics tools such as CrazyEgg and ClickTale, which do a more thorough job of providing such reports than web analytics tools.

Of course, depending on your business, your reporting needs may vary, but we believe this list should provide a good starting page for optimizing one of your most important pages.

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If you’re a media site, one of the most critical measurement objectives is to assess the success of your content. But how does one go about measuring this? Default web analytics reports often fall short in this area. Let’s take a look at some of the most popular content metrics provided by the analytics solutions.

Page views

This is probably the best out-of-the-box metric for measuring the success of a content. The more the number of page views, the more popular the content. However, relying on this number alone has two potential shortcomings. First, it fails to differentiate between segment traffics. For example, a loyal visitor is more valuable to a content site than someone who visited the site for the first time and will likely never come back. Also, page views alone fail to report the level of engagement on the page. For example, visitors could be clicking an article and spending only a few seconds on it. The quality of traffic should therefore be accounted for.

Time spent on page

This metric clearly adds a new dimension around engagement. The more time visitors spend on the content page the more engaged they are. However, you cannot rely on this metric alone. One key reason is the fact that this metric is not always available to all visitors. For example, if the content page was the only or the last page viewed during the session, then this metric is simply not calculated within popular web analytics solutions (we’ll discuss this in a separate post).

Another shortcoming of this metrics is that like page views, it fails to segment the reports by the quality of visitor (first-time vs. loyal).

And finally, the Time Spent metric alone does not take into account the popularity of the content. For example, an article could be very engaging but only be viewed by a handful of people.

Visitor Loyalty

Web analytics solutions often provide this in context of the overall site traffic and you may have to do some tweaks to your reports to get this, but it’s important to note what percentage of your content is consumed by first-time visitors and what percentage by loyal visitors – visitors that come back to the site. The reason this is important is because in the long run, you may want to create a loyal following and create content that’s tailored to them.

Bounce Rate

This is also one of the most popular metrics within analytics solutions, but media sites should be careful not to over-analyze their bounce rates. As an example, consider a media site with an RSS feed. Through the RSS feed, visitors can see the headlines of new content using their favorite RSS aggregator. If an article looks appealing, they click the link, enter the site, read the content and then leave. That’s a bouncing visit but still a highly qualified traffic, because the visitor has subscribed to the RSS. The visitor loyalty metric indirectly takes care of this shortcoming.

Content Engagement Score

In this post, we’d like to introduce you to a content scoring KPI that we’ve used to help some of our media clients put a monetary value next to their content.

The formula is as follows:

Engagement Score = (Page Views × Avg. Time Spent  × Avg. Loyalty)

Where:

• “Page Views” is the number of times the page was viewed during the reporting time period.
• “Avg. Time Spent” in the average number of seconds or minutes spent on the page by visitors.
• “Avg. Loyalty” is the average number of visits to the site by your visitors (1 for first time visitors, 2 for those who’ve been to the site twice, and so on).

Of the three metrics needed to create this KPI, “Avg. Loyalty” is the most difficult to get, but this can be obtained done using estimates in popular tools. For example, with Google Analytics, you can use the %New Visits metric to estimate the average loyalty. You can use the following formula for this purpose:

Avg. Loyalty = (%New Visits) + 2 * (1 - %New Visits)

What this formula does is that it assigns a score of 1 for each new visitor and a score of 2 for all others, providing a reasonable approximation. You can create a similar model with Yahoo! Web Analytics – see below figure for an example of such report in Google Analytics.

Using this model, pages with the highest traffic, time spent and the most loyal visitors will get the highest scores, which is the desired outcome. You can of course use any analysis tool to create your score. One popular tool is Microsoft Excel, where the score can easily be created and analyzed. See figure below for an Excel example. It shows that our posting for tracking internal campaigns is the most engaging even though it’s an old blog post.

Overall, this model provides a simple KPI for measuring site content, while taking into account the popularity, engagement and the quality of the visitor. It does however have its shortcomings. The primary shortcoming is that it is dependent on cookies. For loyalty to be counted, visitors have to accept cookies. Furthermore as visitors delete cookies, it will impact this KPI. However, it’s fair to assume that visitor cookie deletion is not dependent on their content preference, so you should expect the same rate of deletion across the board.

The metric also depends on time spent reporting, which is not available to all visitors. Having said that, it’s also fair to assume that the time spent by those who view a certain content as their last page should be inline with those who view the content in the middle of the session. After all, the purpose of this model is to provide an approximate score for content engagement and popularity.

You may also be in a mode where loyal visitors are no more valuable than first-time visitors. For example, newer web sites fall into this category. In that case, you can simply omit the “Avg. Loyalty” metric from the formula (or replace it with the value 1).

So there you have it. We welcome your feedback on the model and hope you find it of use.

Happy Analyzing!

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It has by now become a tradition for Google to announce new features of it analytics solution during the popular eMetrics events. The company announced its entry into enterprise web analytics during last year’s eMetrics show in DC, when it launched Advanced Segments and its API. This year’s eMetrics show in DC was no different, when Google announced some of its most exciting features yet. Here’s a summary of new exciting features that you can now find in Google Analytics.

Analytics Intelligence

Ever wondered how to navigate through the mountains of data and make sense of them? What do changes in trends mean to your business? Whether you should be concerned about them or not? What if the analytics solution automatically gave you clues about important changes to your site based on past performance and statistical model? Enter Analytics Intelligence. This exciting new functionality automatically alerts you of important site changes based on 11 dimensions and 18 metrics. With this feature, making sense of trend changes becomes an easier task than ever before. Spend less time analyzing data and more time improving your web site.

Customizable Alerts

Although Analytics Intelligence is a great start, web analytics practitioners still know their business better than Google Analytics ever will. The new version of Google Analytics also lets you set customizable alerts based on events that are important to your specific needs. For example, you can create alerts if your social media traffic varies more than usual.

Custom Variables

In our opinion this is the most exciting new feature in Google Analytics. The new version of Google Analytics now lets customers send custom data points to Google to be analyzed as extra dimensions within their analytics account. Want to track additional data per page such as author, category, topic, genre, etc.? You can with Custom Variable. The new version lets you pass up to 5 simultaneous custom variables, with full control of their scope, including whether the variables are set at the page, session or visitor levels.

Extended & Threshold Goals

One of the primary reasons for using multiple profiles was a way to work around the 4 goals/profile limit. The new version of Google Analytics now supports up to 20 goals, with the ability to classify them in goal sets. Additionally, you can now create threshold goals: goals that are set based on engagement thresholds such as the amount of time spent on site or number of page views per session. This is particularly a welcome addition for media sites that need to use engagement thresholds as goals.

Advanced Table Filtering

This feature gives you more control in terms of how you want to filter your data. Example includes the ability to filter data based on multiple dimensions or metrics thresholds such as bounce rate figures.

Expanded Mobile Tracking

As more people use their mobile phones to browse the internet, there’s a growing need to track mobile usage of web site. This feature is welcome news for mobile marketers and site managers who need to better understand their visitor experience.

Unique Visitor Metrics

These new metrics provide a more comprehensive view of the dimensions reported in Google Analytics such as referring sources. Want to know how many unique visitors your various campaigns and marketing programs are generating? This is the answer.

Congratulations to the team at Google for coming up with another impressive release. This release further solidifies Google’s place as an enterprise web analytics solution.

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It is by now fair to say that everyone was caught off-guard when Adobe announced it’s acquisition of Omniture. There’s also been no shortage of opinions and commentaries about the acquisition: those who like it and those who don’t. By and large, most customers that we’re dealing with are somewhat neutral, as Adobe is a strong company that has successfully integrated the Macromedia products into its offerings. Of course Omniture’s business model is so different than Adobe’s that it remains to be seen how the acquisition goes.

Instead of providing commentary on the acquisition, we decided to take a different approach and provide some speculation (not predictions) about the market to come. Some are outright outrageous and they’re primarily for amusement purposes.

1. PDF Tracking becomes available

With Adobe owning both the PDF standard and the measurement technology of Omniture, tracking PDF usage finally becomes a reality. This will benefit the industry greatly and has been a feature that’s been requested for a long time, but technological hurdles have alaways made it difficult to pull off.

2. Adobe offers free web analytics

If Adobe’s plan is to compete with Google, then it’ll have to offer a free or a very low-cost analytics solution. However, this is unlikely to happen on the SiteCatalyst platform which is both expensive to maintain and difficult to implement and support using a free model. A better choice would be the HBX platform. Could we be seeing HBX making a comeback and being offered for free? If so, how would former HBX customers react?

3. Microsoft buys Webtrends

I have to admit we were expecting to see someone else like Microsoft acquire Omniture. Microsoft has already made an attempt to compete with Google Analytics when it acquired DeepMetrix. Although Microsoft Analytics did not pan out as expected, we still think that Microsoft will enter the analytics space. At this point Webtrends seems to be the most likely candidate for acquisition by Microsoft since Webrends also offers a SaaS product that can be repackaged by Microsoft.

4. SiteCatalyst adopts Flash cookies

We all know the limitations of regular cookies. Flash shared objects provide a more reliable way of measuring unique visitors. The adoption means a more accurate web analytics reporting and a more efficient way to measure uniques. Like all new technologies, Adobe will have to overcome the privacy PR, but if done correctly, the industry will benefit from a proper adoption of the technology.

5. Adobe to acquire an ad serving company

One of the main things that Adobe gets by acquiring Omniture is a diversification in its product line. Adobe’s core offerings have been on the decline for some time now and it needs to enter growing markets. Digital Marketing is one such area, but to compete with Google and Microsoft, it’ll have to offer its own ad serving technology, since Google and Microsoft have both acquired DoubleClick and Atlas respectively.

6. SiteCatalyst 15 UI in Flex

This is more of a wishful thinking. Flex provides a great technology for building application user interfaces. One way to start integrating the technologies is to build the next generation SiteCatalyst UI completely in Flex. There are some upcoming analytics solutions that are built completely in Flex and the technology has proven to provide a great deal of flexibility and customization that otherwise would not be possible in HTML interfaces.

7. SiteCatalyst CS5?

Purely speculative we admit. It would be interesting though to think what a software version would look like. What is likely though would be an interface directly inside Dreamweaver and Flash where designers could see the performance of their content and their effectiveness, allowing them to make quick edits based on data. That’s after all the value proposition that the acquisition is promising to provide.

8. Quark buys Coremetrics

OK, I admit, this is very unlikely, but certainly amusing. For those of you who’ve been following Adobe for years, their top competitor in desktop publishing has been Quark, developers of Quark XPress. Quark still owns a large percentage of the desktop publishing market, but it’s a company on the decline, since desktop publishing is dying. What if Quark decided to diversify? Again, this is pure speculation and mainly entered for amusement purposes.

9. Adobe sells Visual Sciences assets

The big question mark is what’s going to happen to Visual Science (Discover On Premise or Omniture Insight) customers? We haven’t seen much discussion about that specific technology, but the engagements are far too consultative for Adobe to be interested in. It makes more sense for Adobe to sell off that technology to a BI company such as Business Objects, which would be a better fit.

10. Adobe sells Omniture

Key to any acquisition is that there are so many synergies that 1+1=3. Many are still scratching their heads to find the synergies and if Omniture continues to be a completely separate business unit, then it definitely remains to be seen. So what if the synergies don’t exist? If the only value proposition that Adobe is going after is integration of analytics into Dreamweaver and Flash, then the synergies are minimal since you can very easily integrate other analytics into those platforms today. In that case could we see Adobe sell the business unit? Again this is very unlikely because of the premium that Adobe paid for Omniture but as of today, the number of doubters is more than the number of believers.

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Tealium is proud to be a new member of the Yahoo! Web Analytics Consultant Network (YWACN). This certifies Tealium as one of the few organizations to implement and support Yahoo! Web Analytics. It also allows retaining customers to gain access to Yahoo! Web Analytics for free.

Yahoo! Web Analytics is a full-featured, enterprise analytics solution developed as part of the IndexTools acquisition by Yahoo!. It provides detailed segmentation, audience demographics, click-stream and campaign analysis features using a customizable user interface.

Some key features within Yahoo! Web Analytics include:

• Real Time Reports: information provided within minutes of the activity
• Custom Dashboards: create customized dashboards, including KPIs based on needs
• Custom Reports & Custom Fields: custom fields let you collect information beyond that captured by default web analytics. Examples include content date, author, genre, etc.
  With custom reports, you can get more detailed reporting using custom/multiple dimensions and metrics
• Custom Alerts: get automated notifications based on criteria that’s important to your organization
• And more

You can contact us to learn more about Yahoo! Web Analytics or for a live demo.

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As more large enterprises are adopting Google Analytics, there’s a growing demand for enterprise-level features from the solution. Google has made some tremendous progress over the last year by introducing some advanced functionalities such as Advanced Segments, Custom Reports and API access, which has created an impressive ecosystem of add-on tools. There are still, however, some functionalities that are highly desired by the more advanced user base. One such functionality is reporting on product conversion or abandonment.

The Ecommerce functionality inside Google Analytics already provides a great deal of insight, including transactions and identifying your top revenue sources such as keywords, campaigns and affiliates. However, for those companies interested in optimizing their site merchandizing, a useful report is that of product conversion or abandonment. In other words, companies would like to understand the effectiveness of individual products at generating a view, a cart-add, checkout progress and finally a purchase.

Although this is not a standard report in Google Analytics, you can use the new Event Tracking feature in Google Analytics in order to generate this insight. This post outlines the instructions for those that want to generate such reporting inside Google Analytics.

The Event Tracking feature was originally designed by Google to help track visitor interactions within the web site. Examples include link clicks, downloads or interactions within a video or a Flash application. A typical syntax for sending an event to Google Analytics is the following:

pageTracker._trackEvent(category, action, optional_label, optional_value);

Where category is the name you supply to the elements you want to track, action is the name of the user action, label is the name or label associate with the event and an optional value, such as an amount associated with the event.

For this solution, we’re going to use the following syntax:

• Category: the value passed into Category will be “product”. This lets us differentiate between other events if this feature is also used for other purposes.
• Action: the values passed into this variables will be “view”, “cart”, “checkout” and “order”, depending on the stage at which the visitor is.
• Label: this variable will be used to capture the name of the product.
• Value: not needed in this case.

The next step is to code your ecommerce pages accordingly in order to pass the product name and the event into Google Analytics. Below are some instructions.

For the product pages, the following line should be added to the Google Analytics page code. The PRODUCT_NAME should be inserted dynamically from your content management solution.

pageTracker._trackEvent(“product”, “view”, “PRODUCT_NAME”);

pageTracker._trackEvent(“product”, “cart”, “PRODUCT_NAME”);

pageTracker._trackEvent(“product”, “checkout”, “PRODUCT_NAME”);

pageTracker._trackEvent(“product”, “order”, “PRODUCT_NAME”);

Viewing Reports

The reports will be available within the Event Tracking section inside Google Analytics. If you want to see the overall progress at different stages, you can start with the “Categories” report and from there, click the “product” category. An example of the resulting view is shown below.

However, this merely gives you the progress at different stages without visibility into specific products. In order to see the progress within a specific product, you can go to the “Labels” report as shown below, which provides a list of individual products and select a specific item. The ensuing screen is also shown below and provides a view of the progress at each stage for the specific item selected. Here, you’ll be able to see how many times an individual item was viewed, added to cart, checked out and purchased.

Obviously, Event Tracking was not originally built for tracking product conversions, so it’s important to note the implications of such methodology. One of the main items to consider is that Event Tracking generates extra views in your account. As a result this methodology will have an impact on your overall account pageviews, pages per visit and bounce rates. For example, if a visitor hits a product page and bounces, because you’re using Event Tracking to track the page view event, you won’t be able to see the bounce event take place. However, for those who absolutely need to track product conversion/abandonment, this provides a reasonable solution.

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Every now and then, we get questions from customers about ways to use Motion Charts inside Google Analytics. For the most part, you want to take advantage of the time dimension inside Motion Charts to trend multiple items over multiple dimensions (such as visits and conversion rates). So for example, you can use Motion Charts to see how your keywords are evolving over time.

One of the best presentations that we’ve seen using this technology doesn’t even involve web analytics. Instead, it’s a presentation by Hans Rosling, the statistics guru about trends in developing countries.

This is not a new presentation, but the concepts presented and lessons about the use of data are still very relevant even in our industry.

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Hi everyone, this is Olivier. I wanted to drop a quick note to let you know that I’m running for the Web Analytics Association Board of Directors.

For those who read this blog, I would like to explain briefly the reasons behind my nomination and why I’m asking you to vote for me (weird feeling now… requesting your vote! I feel like I’m entering into politics).

Anyway, I have presented my candidacy because I feel that our industry needs as much help and support as we can provide. And having been in the Web Analytics and Online Optimization space for more than 10 years now, I felt it was time for me to give back to this industry that has given me so much. My objective with the WAA if I have the chance to be elected is to put much effort in helping web analytics and online marketers solve the challenges of spending more time on what matters. For the past 10 years, and everywhere I go, it’s the same challenge.

What matters is the analysis, the interpretation, and more importantly the actions/recommendations/changes we can make base on the data that has been collected. Testing new marketing or web design ideas, improving the user experience, watching the online conversion rates increase, that’s what is fun and rewarding.

But unfortunately, most people are bugged down with data accuracy, implementation challenges, best practice measurements, etc. Too many individuals are spending their time trying to reconcile numbers, understanding and explaining why A and B don’t match as they should, figuring out the best way to measure specific web content (i.e.. flash, video, Social Media, mobile, offline data, etc.), and finally coordinating efforts with IT to implement (or re-implement) their web analytics tags for the 10th times to capture all this. There’s got to be a better way. And that’s the challenge I would like to tackle with all the players in this industry if I’m elected.

I really think that the WAA could be more proactive as an organization. Look at the W3C (World Wide Web Consortium) and how they create web standards ahead of time for everyone, especially for technology vendors to follow. As a WAA director, I would love to list all the key challenges expressed by the WAA members (some of them listed above) and figure out proactive standards and best practices for measurement and implementation with all the key players in this industry so that WE (all of us) get to spend more time on what matters.

For more information about my candidacy, you can visit the WAA site.

I appreciate everyone’s support, and I wish you all a successful 2009.

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