(Estimated Time to Read this Post =3 Minutes)

I believe that every SiteCatalyst implementation should have a Previous Page sProp.  There!  I said it (I feel like I am channeling Avinash!).  In past blog posts I have touched upon the use of a Previous Page sProp, but I feel like I have not done it justice and wanted to take time to explain it in greater detail.  In this post, I will describe why I think this variable should always be set and provide some examples of its use.

Why You Need a Previous Page sProp
I find that in the web analytics world, I often receive the following question:

“What page was the visitor on when he/she _______?”

You can fill in the blank with many things.  Here is a list of the ones I have been asked:

• …searched for this phrase in our internal search box…
• …clicked on a button to go to a web lead form…
• …downloaded a white paper…
• …added products to the shopping cart…
• …clicked on a banner advertisement…
• …started using the ROI calculator…
• …clicked to fill out a website survey…

I could go on for days and never come to the end of these types of questions!  People want to know this information because it helps them get inside the head of their visitors.  Often times it leads to navigation or content changes.  Regardless of the reason, I assure you that you will be asked this question at some point and the truth is that it is not easy to answer with out-of-the-box functionality (i.e. Pathing).  The good news is that setting the Previous Page sProp is easy and will pay great dividends down the road…

How To Set the Previous Page sProp
Setting the Previous Value sProp could not be easier.  All you have to do is use the Previous Value JavaScript Plug-in to pass the previous page name to a new Traffic Variable (sProp).  You can even see a detailed description of the code for this in Ben Gaines’ great Summit blog post.  If you need help, call your Omniture Account Manager, Omniture Consulting or ClientCare.

Once you have your JavaScript setup to pass the Previous Page Name to the sProp, you need to enable a Traffic Data Correlation to any sProp for which you want to create a breakdown.  For example, if you want to see what pages visitors were on when they searched for a particular internal search term, you would correlate the Previous Page Name sProp with the Internal Search Term sProp…

…so you can see a report like this:

In addition, if you are familiar with correlations, you may recall that they are bi-directional, so in addition to seeing the pages people searched for specific terms from, you can also see the converse.  In this case, that would mean seeing all of the internal search terms visitors searched for on a specific page:

As you can see here, we see the same “4″ searches for the phrase “chatter” from the selected page as we saw in the first internal search term report (in this case I am just using Internal Search as an example, but if you want to learn more check out my Internal Search post).

One Is Usually Enough
However, one word of caution, I have seen many clients implement several Previous Page sProps and I am not a fan of doing this as I will now explain.  Let’s say you want to see what page people were on when the searched on a specific search term (as described above) and you also want to see what page they were on when the downloaded files on your site.  A lot of people will set two Previous Page sProps in this situation – one for the search term and one for the file downloads.  In my opinion, this just wastes a variable, wastes correlations and causes confusion for your users.  The truth is that all you need is one Previous Page sProp to answer both questions.  Since on each page there will be one and only one Previous Page value, there is really no reason to do this multiple times.

I have seen some clients who have chosen to pass the Previous Page Name to an eVar.  There are some interesting uses of this.  For example, if you want to see what pages visitors were on when they added a specific product to the shopping cart, you can pass the Product Name to the Products Variable, set the Cart Add Success Event and the Previous Page Name to an eVar.  The main issue you will run into is that Conversion Subrelations are an “all or nothing” proposition so you can only do breakdowns by eVars that have Full Subrelations.

One final tip that I will throw out there is to consider having your developer pass a value of “[NO PREVIOUS PAGE AVAILABLE]” (or something similar) to the Previous Page sProp on entry pages (or any other time no Previous Page is available).  I find that this is easier than dealing with questions around “Unspecified” in correlation reports and it is easier to remove this value using the search box than it is to hide the “Unspecified” values.

Final Thoughts
As I mentioned in the beginning, I highly recommend that you have a Previous Page sProp for all of your key report suites and add correlations as needed.  If you have any questions/comments, feel free to leave them here…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

(Estimated Time to Read this Post = 4 Minutes)

I recently received an e-mail from a blog reader who was having issues tying their Orders in SiteCatalyst to Orders in their back-end system.  Here is a snippet from the e-mail:

I have a little issue in my own SiteCatalyst setup that I recently discovered.  Sad for me I had trusted the number of Orders for each day’s Conversion Funnel and recently I decided to validate the numbers in SiteCatalyst against what our back-end system has.  SiteCatalyst is 5%-10% understated each day which makes for a heck of a difference at the end of the month!  I’d rather be understated than overstated, but can you give me some ideas where I should look first?

Unfortunately this is an all too common problem I hear out there.  In this post I am going to share some ideas on how you can tackle this Order/Revenue validation issue head-on and make sure you can trust your critical Orders/Revenue data in SiteCatalyst.

Order ID eVar
If you have an online shopping cart, you should already be setting the s.purchaseID variable with a unique Order ID when an Order takes place on the website.  This variable is used by SiteCatalyst to ensure Order uniqueness.  Unfortunately, the downside of this variable is that it is not readily available in the SiteCatalyst interface.  It is available in DataWarehouse but not in regular SiteCatalyst reports or Discover.  Carmen Sutter (@c_sutter) has submitted  an idea in the Idea Exchange to change this, but until then, I recommend that you set what I call an Order ID eVar variable.  To do this, all you need to do is set the same value you pass to the PurchaseID variable to a custom eVar.  This will allow you to see all Orders and Revenue by Order ID from within SiteCatalyst and Discover as you would any other eVar.  Once you have done this, you can open up this new Offer ID eVar and add your Orders or Revenue Success Event as needed:

In the example above, we can see that most Orders have only one Order ID, which is what we want.  However, in this case, we can see that one ID was counted twice.  That may require some research and I like to schedule a report like the one above to be sent to me weekly so I can make sure nothing strange is going on.

Data Sources Setup
However, while adding an Order ID eVar is helpful in seeing if you are over counting Orders in SiteCatalyst, it won’t tell you if you are under counting Orders or  how close your SiteCatalyst data is to your back-end systems.  To do this, I recommend you use Data Sources.  As a quick refresher, Data Sources allows you to import external data/metrics into SiteCatalyst (see post link for more details).  In this case, I recommend that you import in a file from your back-end system into SiteCatalyst which contains your unique Order ID, the number of Orders (which should always be “1″) and the Revenue Amount.  When you import data via Data Sources, you include the date that you want the data to be associated with so it doesn’t matter if you import the data on a daily, weekly or monthly basis, but the more frequently you upload it, the better so you can find issues quickly.

Here are step-by-step instructions on how to do this:

• Create the Order ID eVar described above
• Create two new Incrementer Success Events and name them “Back-End Orders” (Type=Numeric) and “Back-End Revenue” (Type=Currency)
• Create a new Data Sources upload template (ClientCare or Omniture Consulting can assist with this).  You want to be sure to map the two new “Back-End” Success Events to the Data Sources template.  Even more critical, is that you want to include the newly created Order ID eVar in the Data Sources template.  If you do not do this, then you will not be able to see these two new Back-End metrics in the same Order ID eVar report that you have in SiteCatalyst (more on this later).

• When you are done, you should have a Data Sources template that looks something like this:

• Now all you have to do is work with your developers to have this file sent via FTP to the Data Sources FTP on a regular basis.

The Payoff
So by now, you are probably saying to yourself: “That’s a lot of work!”  No argument here!  However, hang with me as I share what the ultimate payoff is for doing this.  As you recall, our primary objective was to see if our online Order and Revenue data was matching what our back-end systems indicated.  Now that we have the Order ID eVar and two new “Back-End” Order and Revenue metrics, we have everything we need.  This is where the fun begins and we put it all together!

All you have to do now is to open the new Order ID eVar report and add all of the relevant metrics.  First, we will add the SiteCatalyst Orders and Revenue so we can see online Orders and Revenue by Order ID:

Next, we will add the two new “Back-End” metrics to the report and, since we were smart enough to include the Order ID eVar value in the Data Sources upload, SiteCatalyst knows which “Back-End” Order ID and dates line up with our online data:

Cool huh!  As far as SiteCatalyst is concerned, these offline metrics are connected to your Order ID eVar values just as if they had happened online.  Using this report, we can see if there are any differences between our online and offline data.  In the example above, it looks like the “Back-End” system had an order with $2,350 in revenue that wasn’t captured online.  Having this information makes it much easier to troubleshoot order submission issues.  You can even use DataWarehouse or Discover (only if you use Transaction ID Data Sources) to break down Order ID by browser, domain, IP address, etc… to see if you can figure out what is happening.  In addition, you can export this data to Excel and look at the totals to see how far off you are in general.

Finally, for the true SiteCatalyst geeks, you can create a Calculated Metric that divides Orders by Back-End Orders and/or Revenue by Back-End Revenue to see a trended % that each is off and set up Alerts to notify you if they deviate too much!  When you take into account this level of assurance all of a sudden the Data Sources work above might not seem like all that much in the long run!

Final Thoughts
If you sell products online, nothing is more critical than believing in your key metrics.  Even if you don’t sell online, the same principles here can be applied to lead generation forms, subscriptions or any other metrics you store in SiteCatalyst and also in your back-end systems.

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

(Estimated Time to Read this Post = 3 Minutes)

[I apologize in advance for such a horrible blog post title, but I couldn't think of a succinct way to describe what I intend to cover.  Maybe one of you out there will have a better suggestion after reading the post!]

If your website is like many I have seen, you get a fair amount of daily visits and unique visitors, but it may be the case that a large number of your visitors don’t go beyond the first few pages of your site.  When I see this, I get very frustrated when I think about all I have done to get people to my site and optimized the site for my designated conversion goals.  But as web analysts, we need to put our emotions to the side and get down to the numbers.  Therefore, one of the things I like to do is to quantify how big of a problem my website has with visitors who only view a small number of pages.  In this post I will show you how to quantify this so you can begin to take action on addressing this issue.

The Setup
Before I get too deep into this topic, I’d like to setup the scenario since I think this will help it make more sense.  Let’s say that the main purpose of your website is to get visitors to view and complete lead generation forms.  Let’s also say that on your website you see that your most significant drop-off takes place after the third page of each visit.  In this situation, you might have lots of Visits and relatively few Form Completes so that your Conversion Funnel looks like this:

As you can see in this funnel, there is a pretty significant gap between Visits and Form Views.  While that presents a huge optimization opportunity, I like to break massive efforts like this into smaller chunks that I can work towards (or as Avinash points out – Micro-Conversions).  Since we noted earlier that a large portion of visits exit after three pages, wouldn’t it be nice if we could bridge the gap between our Visits metric and our Form Complete metric in the funnel above?  Having a middle ground between these Visits and Form Views might get our team to think about ways to turn more Visits into Visits of four pages or more which, depending upon your site, might be a step in the right direction.  In many sites I have worked with, there is a direct correlation between visitors viewing more pages and higher form conversion rates.

X+ Visits Explained
Now that we have set-up the situation,it becomes a bit easier to understand what I mean by “X+ Visits” since I am really saying that you can set a new Success Event metric which represents how many Visits your website gets where the visitor viewed more than “X” Pages.  What “X” represents is up to you and should be based upon your own data.  In this example, we will say that we are going to call it “4+ Page Visits” meaning the number of Visits in which Visitors viewed four or more pages.

The implementation of this is very easy for any good JavaScript developer since all that is involved is setting a Success Event as soon as each Visitor hits the fourth page of the session.  Once you have done this, you can update the conversion funnel shown above to look like this:

While this may not seem like much of a difference, here are some cool things you can do once you have this implemented:

• Create a Calculated Metric to divide 4+ Page Visits by Total Visits to see what % make it to four pages and trend this over time to see if you are getting better or worse

• Use the filter feature of the conversion funnel to see your funnel by Visit Number or Traffic Source (i.e. SEO) to see how each impacts the mix of Visits and Visits of four or more pages

• Create a calculated metric for the inverse (in this case three pages & fewer) by subtracting 4+ Page Visits from Visits.  I also like to pass both to Excel using the ExcelClient to create a stacked graph like this to show progress

Final Thoughts
There you have it.  If you find that you consistently have significant website drop-off after a few pages, hopefully, this new metric will help you better dissect what is happening so you can “Micro Conversion” your way to more Macro Conversion!

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

(Estimated Time to Read this Post = 3.5 Minutes)

Imagine that you are in a retail store and you grab a bunch of items, bring them up to the counter and just as you are about to pay, you decide to push a few of the items off to the side and not include them as part of your purchase.  While this may not happen too often in real life, it happens quite often in eCommerce.  If you are a retail website, these discarded items can add up quickly!  In this post, I am going to show you how to quantify how much money you are leaving on the table.  For those not involved in a Retail site, I will also do my best to show how this concept can be applied to non-Retail sites.

The Standard Cart Process
So before we get to the more advanced stuff, let’s make sure we are all on the same page when it comes to the eCommerce shopping cart process.  Normally, here’s how it works:

1. Visitors view products on your website and you capture this with a Product View Success Event and store the products viewed in the Products Variable.
2. At some point, visitors add items to the shopping cart and you set the Cart Add Success Event and the Products Variable with the product ID or name(s).
3. Hopefully, visitors get to the Checkout Page and you set the Checkout Success Event and the Products Variable with the Product ID or name(s).
4. Finally, the order is completed and you set the Purchase Success Event which sets the Orders, Units and Revenue Success Events for each Product purchased.

Hopefully this is straightforward and if you sell online you have successfully implemented these steps on your site.  If so, you are ready to take things to the next level and do some stuff that is not traditionally done as part of standard eCommerce implementations.

How Much $$$ Left on the Table?
As the post name implies, in this scenario we would like to see how much $$$ we are losing online by website visitors leaving items in their Cart.  If you think back to the initial scenario above, this is equivalent to the Retail store adding up how much they could have made that day if no one had left stuff on the counter when they were checking out.  In addition to seeing how much $$$ is being missed out on, the store owner would probably want to know what products are being left to see if there are any patterns he/she could identify.  For example, it may be the case that items over $100 are left more often than products under $100, etc…

Well the good news, is that if you are doing business online, this much easier and you can see a lot more data on the items being abandoned and those who abandon them.  So here’s how you do it:

• When a website visitor adds one or more products to the shopping cart, in addition to setting the Cart Add Success Event (scAdd), you should set a currency Incrementer Event with the dollar amount associated with the items added.  As a refresher, an Incrementer Event allows you to pass in a numeric/currency value to a Success Event instead of using it as a counter.  By passing in the amount associated with the items added the Cart, you will have a new metric which represents the total potential that you could have made had no one left anything in the cart.  I call this new metric $$$ Added to Cart.



• Once this is done, you can compare this “$$$ Added to Cart” metric with your Revenue metric, either in a conversion funnel report or in a normal Conversion Variable (eVar) report by creating a Calculated Metric dividing the two metrics to see what % of $$$ Added to Cart turns into Revenue.

• If you want to be even more particular, you can set another incrementer event with the $$$ that the visitor has in the Cart at the time of Checkout.  However, if you find that you don’t have much loss between Cart Add and Checkout or between Checkout and Purchase, this may prove to be unnecessary.
• Finally, since you are setting the Products variable with the Cart Add event already, when you compare these two metrics, you can easily break it down by Product (or any other eVar variables you have set previously).

Beyond Retail
As promised, I wanted to touch upon a few ways you could use this same concept if you manage a non-Retail website.  Here are a few that come to mind:

1. On a Financial Services site, pass in the total loan amount a person is requesting and compare that to how much they are eventually loaned.
2. On a Media site, pass in the total amount of advertising your site could have earned if all ads were clicked.
3. On an Auto site, pass in the total value of cars visitors configure to see your max potential.
4. On a Lead Generation site, pass in a value for ever visitor who starts completing a lead form.
5. On a Travel site, pass in the total value of trips planned online and compare it to the amount actually booked.
6. On a Manufacturing site, pass in the total Bill of Materials value the visitor has added.

As you can see, the concept of seeing what your high-end potential is and comparing it to actual performance can be applied to almost any website and gives you another data point for comparison.  I like using this metric better than Visits or Unique Visitors since it is not realistic that you are going to convert every person who comes to your site.  However, once a visitor takes some more deliberate actions, they are self-qualifying themselves, and therefore, capturing their potential revenue streams gives you a high, but realistic goal to strive for and a KPI that you can use to see how you are doing over time.

Final Thoughts
So there you have it.  Just a quick, easy way to add some more data to your all-important shopping cart process.  In general, I feel like Incrementer success events are under-utilized by SiteCatalyst users so hopefully this example helps to get your mind working in new and inventive ways to use them…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

(Estimated Time to Read this Post = 4 Minutes)

If you are in the Web Analytics space, besides tracking what people do on your website, hopefully you are actively doing testing and content targeting to try and improve your conversion.  If you are an Omniture customer, you might be using their Test&Target product or you may be using Google’s Website Optimizer.  If you are just getting into the testing area, you may simply be using an eVar to see how your tests are performing.  Regardless of what tool you are using, there is a common question that arises in the testing/targeting area.  Here is the scenario:

1. You come up with a great hypothesis you want to test
2. You run a test and see awesome results (say a 10% uplift in conversion)
3. You broadcast it to your company only to hear the inevitable “well that was just a test…how do you know we’ll see the same result in real life?”

As a web analyst, this can be infuriating and can be compounded by the fact that you often cannot simply run with the winning recipe and show the results in your testing tool because:

• You may be running multiple tests and things can get confusing
• You may want to apply what you have learned from the test to many places on your website which may or may not have the required “MBoxes”

In reality, it may take time for you to take your awesome test and let it out “into the wild” and when you do so, how can you prove that the uplift you saw in your test will actually occur over the next year on the website?  The following will tell you exactly how you can do this and hopefully put the naysayers in their place!

How To Prove Your Test Results
So now that I have framed the situation, let’s learn how to do it.  Our objective is to prove the long-term results of a test we did using our chosen testing/targeting tool.  In this example, let’s imagine that your website has twenty forms on it and you have just done a test showing that if you reduce the number of fields on a form, you can see a 15% uplift in Form Completion Rates.  This test was conducted using Test&Target for three weeks with a high level of statistical confidence (+95%).  Now you want to go ahead and take five of the twenty forms and remove the same fields you did in the test for the next three months and see what happens.  One way to do this would be to add lots of “MBoxes” and use Test&Target to deploy the winner in hopes of seeing the same lift results, but in this example, let’s assume that your conversion team has closed the books on this test, moved onto other tests and has told you that you now need to work with the web team to reduce the fields on your five forms.

So what do you do?  How will you know if these five forms will really see a 15% uplift over the next three months?  All you need to do is the following:

1. Create a new Testing eVar (not the T&T eVar)
2. On each of the five forms you modify on your website, pass in the name of the the test that it was based on to this new eVar.  This may be the name of the winning T&T recipe or you can use any descriptive name you’d like.  In this case, we’ll pass in the value “Remove Form Fields Test”
3. Set the eVar to “Most Recent Value” and expire “Never” in the Admin Console

That’s it.  Now when you open this new Testing eVar report, you can see how these five new forms are doing with respect to Form Completion Rate (assuming you have the right Success Events set – in this case Form Views and Form Completes).  When you look in this new eVar report, all forms that were not modified based upon a testing initiative will fall into the “None” row so you can easily compare those forms that are based upon testing with those that are not:

In the preceding example, we can see that the “Remove Form Fields Test” seems to have about a 17% uplift in Form Completion Rate after it was fully deployed so we are doing even better than the 15% expected!  What’s better, is that if you repeat this process every time you make changes to things on your website based upon testing, you can see how each is doing:

And, if you look at them all together, you can show your boss at the end of the year how much uplift you have been responsible for overall!  In this example, if we look at all of the tests we have implemented, we are seeing a cumulative uplift of 16.2% over forms that are not based upon any testing.  This is a great way to show the value of your conversion efforts and justify more headcount, get promoted, get more budget, etc…  In fact, you can show your boss, that if all of the “Form Views” on your website were, in this case, seeing optimized forms, you could produce 5,800 Form Completes instead of the 5,000 you are currently getting at the lower Form Completion Rate.

The only downside of this solution is that it might actually show you that something you expected to have an uplift, in reality didn’t.  For example, in the preceding screen shot, the “Form Headline Bold” change doesn’t seem to be pulling its weight (losing against the control)  and may need to be revisited.  However, even though this is disappointing, it is great information to have since it might prompt you to do some further testing in Test&Target and abandon the losers.

Finally, if you want to get a little more advanced, you could also apply SAINT Classifications to this new Testing eVar and group your tests into types (i.e. “Field-Related Tests” or “Color Related Tests”) so you can calculate the uplift of each type and see which ones you may want to focus on going forward.

Final Thoughts
So there you have it.  As a rule of thumb, I would build a step for passing in the Test Name a change was based upon into a Testing eVar into your conversion testing process so that you can look at how your tests ultimately perform.  While this will add one small step to your overall process, I think that in the long run you will be happy that you have this variable to show how your team is doing…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

(Estimated Time to Read this Post = 5 Minutes)

In my last post, I explained a bit about CRM and how you could improve CRM by passing Web Analytics data into your CRM system.  In this post, I am going to cover the reverse angle  – passing CRM data into Web Analytics.  Since most of you reading this are web analysts, I think you will find this post more relevant, but I think it is important to understand both sides.

Why Pass CRM Data into Web Analytics?
As I mentioned in my last post, we web analysts get lots of great information about website visitors, but for many companies (especially B2B), the richest data resides in the CRM (Customer Relationship Management) system.  If you want to be relevant in your organization, it is always best to be as close as possible to the $$$ and that often means playing nicely with CRM systems.  Don’t get me wrong,  showing your CMO that you can lift form completion rates by 200% through optimization is awesome, but if you can show him the revenue impact of it right there in your Web Analytics tool, you will be a rock star!  Additionally, I will show that if you don’t have actual revenue-generating events on your site (eCommerce and Media sites have this easy!), then not doing this could actually result in Web Analytics data causing incorrect business decisions…

Passing Post-Website Data from CRM to Web Analytics
OK.  So there are many different ways to merge CRM and Web Analytics data including passing data from both into a massive Marketing data warehouse (or Omniture Insight), but just for the purposes of this post, I am going to assume that you are a SiteCatalyst person and want to get something done relatively quickly.  In this scenario, we’ll assume the following:

• You want to see which of your website visitors completing lead forms on the site evolve into Leads, Opportunities and Revenue
• Your CMO has charged you with capturing all of the different marketing channels and asked for your opinion on where the company should invest to get the most Revenue
• You are tracking the various sources of traffic you receive and using SAINT Classifications to roll each up into a high-level marketing channel (SEO, SEM, E-mail, etc…)

Given all of this, you might have a SiteCatalyst report that looks like this:

As a web analyst, at this point, it looks like we might want to invest more in our E-mail program since that seems to be converting the best.  Without CRM integration, that would probably be as far as we could go.  But let’s now dig a little deeper.  As I mentioned in the last post, when website visitors complete a form, we have a brief moment in time when we can connect our website data with our CRM data.  Most CRM tools allow you to capture leads and set a unique ID for each form completion.  At the same time, Omniture SiteCatalyst has a really cool feature (that many don’t use enough!) called Transaction ID.  I highly recommend you read my full post on Transaction ID, but at a high level, it allows you to set an ID to a special SiteCatalyst variable and then days or weeks later, upload [normally offline] metrics into SiteCatalyst.  The magic of Transaction ID is that when you upload these metrics later, they are tied to the eVar values (sorry – no sProps or Participation) that were present at the time the Transaction ID was set.  That means that if a website visitor had a City eVar value of Chicago, a Traffic Source eVar value of Paid Search and a Visit Number eVar value of 3, then any offline metrics you import will also be tied to Chicago, Paid Search and Visit Number 3 in the respective eVar reports.  This means that if you set the CRM ID associated with a website form completion, you now have a primary key (think Rosetta Stone!) that can connect your Web Analytics data to your CRM data!

So what does this mean to you?  Following our preceding example, let’s assume that you have made this connection and later imported all of the new leads your CRM system has seen along with the status (i.e. Qualified)  of each into SiteCatalyst (these new metrics would be Incrementor Events).  This gives you a new metric named “Qualified Leads” that you can now see in SiteCatalyst reports and since you used Transaction ID, these imported CRM metrics are correctly attributed to all eVar reports in your implementation.  The result is that you can now open a report similar to the one we saw above, but now it has “Qualified Leads” instead of Form Completes and a new Calculated Metric that divides these Qualified Leads by Visits:

The icons above the report show where each data point comes from and as you can see, the last column is truly magical in that it is combining data from two disparate systems (Cool huh?)!  Once we have this, we can see that even though E-mail looked to be the best channel a few minutes ago, it now appears that SEM is where we want to spend our money.  It turns out that E-mail generates form completions at the highest rate, but perhaps those form completions are all junk!

However, I like to go as far downstream as possible and nothing is better than cold, hard cash!  Applying the same principles, we can import Qualified Opportunities, Potential Pipeline, but the CRM metric that trumps them all is Revenue.  By uploading Revenue via Transaction ID, we can see how much $$ we got from each Lead Form completed on the website and tie it to any eVar value we have – in this case marketing channel/traffic source.  The following report shows the result of this:

Again, we see that some data is coming from SiteCatalyst and some is coming from our CRM system.  Our new Revenue/Visit Calculated Metric can be used to see that, in the end, it is really SEO that provides the most Revenue/Visit and maybe we should consider additional investment there.  Please keep in mind that these examples are simply meant to illustrate the concept and show the value in adding CRM metrics to your Web Analytics tool.  Finally, don’t forget that Transaction ID data is available in Omniture Discover so you can slice and dice this data even further there!

Targeting Based Upon CRM Data
Another really cool integration between CRM and Web Analytics is in the area of Test&Target.  For those not familiar with Test&Target, it is an Omniture tool that lets you test and dynamically target content to website visitors based upon what you know about them.  It is commonly used to optimize your website success metrics.  However, this can be extended by importing in CRM data so that your targeting is based upon both online and offline data.

Let’s walk through an example.  Imagine that a website visitor named Bill has been to your website a few times, looked at a few of your products and completed a lead form.  Next, Bill spoke to your sales representative and is at “Stage 3″ of the sales process (the discovery phase).  Over the next few weeks, meetings take place and Bill comes to the website occasionally (your sales team would know when and exactly what he is doing if you read my last post!).  But now let’s say that Bill is in sales “Stage 9″ which is the final stage before the deal is won or lost.  We know what products he wants, we know he is close to making a decision, we know how big is company is, etc…  If we knew all of this, what would we want to show him the next time he arrives at our website?  Here are a few things I would show to Bill on my home page when he (and only he) arrives on it:

1. Case studies related to his industry
2. ROI calculator for the product Bill is interested in
3. Links to community content to show Bill that he would be well taken care of if he were to be a customer
4. A time-sensitive offer (“Buy in the next 24 hours and get XX% off”) – You could even address him as “Bill” but that might freak him out!
5. etc…

The point is that if you can get the rich customer data related to Bill and multiply this to all of your prospects, each one could see more personalized content that helps move them further down the sales funnel.  You can even track how often they see these “recipes” and track the success of your intelligent targeting.  If you are interested in this type of CRM-based targeting I suggest that you contact @brianthawkins who is a Test&Target Jedi-master…

Final Thoughts
Hopefully this sparks some ideas about ways in which you can enrich your Web Analytics data by adding CRM data to the mix.  In the next post I will cover ways in which you can import CRM meta-data into your Web Analytics tool to augment your current web analyses.

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

Often times when I used to work with clients and now internally, I am surprised to see how many SiteCatalyst users don’t take advantage of Comparison Reports within the SiteCatalyst interface.  In this post I will review these reports so you can decide if they will help you in your daily analysis.

Comparing Dates
Hopefully most of you are familiar with this type of Comparison Report.  This report type allows you to look at the same report for two different date ranges.  To do this, simply open up an sProp or eVar report and click the calendar icon and choose Compare Dates when you see the calendar.  In the example shown here, I am going to compare February 2010 with March 2010:

For this example, I have chosen the Browser report, using Visitors as the metric.  After selecting the above dates, my report will look like this:

As you can see, SiteCatalyst adds a “Change” column where it displays the difference between the two date ranges.  This can be handy to spot major differences between the two date ranges.  In this case we can see that “Microsoft Internet Explorer 8″ had a big increase and that “Mozilla Firefox 3.5″ had a decrease (probably due to version 3.6!).  You can compare any date ranges you want from one day to one year vs. another year.

However, when you compare ranges that have different numbers of days, your results can be skewed.  For example, in the report above, March had three more days than February so that may account for why the differences between the two are so stark.  If this ever becomes an issue, you can take advantage of a little-known feature of Comparison Reports – Normalization.  In the report settings, there is a link that allows you to normalize the data.  When you normalize the data, SiteCatalyst makes the totals at the bottom of each report match and increases/decreases the values of one column to adjust for the different number of days.  I am not 100% sure what specific formula or algorithms are used to do this, but for the amount of times that you will use it, I would go ahead and trust it.  Below is an example of the same report with Normalization enabled:

If you look closely, you will see that the March 2010 column has been normalized when we clicked the “Yes” link shown in the red box above.  By doing this, SiteCatalyst has reduced the numbers in the March 2010 column to assume the same number of Visitors as there were in February.  If you want to normalize such that February is increased to match March, you simply have to reverse the date ranges so when you select your dates, March is the first column and February is the second column (the second column is always the one that gets adjusted).  As you can see, the “Change” column is now dramatically different!  In this version, “Microsoft Internet Explorer 8″ no longer looks like it has changed much.  I find that using this feature allows me to get a more realistic view of date range differences.

Finally, you may notice a tiny yellow box in the preceding report image (says “6,847″).  This is a secret that not many people know about.  When you normalize data, Omniture artificially reduces or increases the values in the normalized column.  But if you want to see what the real value is (if not normalized), you can hover your mouse over any value and you will see a pop-up with the real number!  If you look at the first version of the report (the one before we normalized), you will see the same “6,847″ number in the first row of the report… Pretty cool huh?

Comparing Suites
This second type of Comparison Report is the one that fewer people are aware of or have used.  In this type of comparison, instead of comparing date ranges you compare different report suites.  Obviously, this only makes sense if you have more than one report suite, but it also works with ASI slots so don’t assume this isn’t relevant to you if you have just one report suite.  Much of the mechanics of this are similar to the steps outlined above.  You simply open one report (in this case we will continue to use the Browser report) and then choose the “Compare to Site” link and choose a second report suite or ASI slot.  In this case, I am showing an example of the Browser report for two different geographic locations.  Since most report suites have different totals, I tend to use Normalization more in these types of comparison reports.

Final Thoughts
This covers the basics of Comparison Reports.  Hopefully you can use this to start creating these reports and adding them as scheduled reports or even to Dashboards.  In my next post, I will take this a step further and demonstrate an advanced technique of using Comparison Reports…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  You can also hear Adam on the BeyondWebAnalytics podcast.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

Last week I shared a way to capture the various traffic sources (i.e. SEM, SEO, E-mail, etc…) so you could calculate the Bounce Rate for each of these Traffic Source types.  In this post I am going to build upon this and show you another cool way you can leverage this to have what I call Cross-Visit Traffic Source Attribution.

What is Cross-Visit Traffic Source Attribution?
As an online marketer, one of the things I want to see is how each traffic source leads to online success.  Within a visit, it is relatively easy to see which Traffic Source types lead to success.  Normally this is done by capturing the various campaign elements and using SAINT Classifications to roll these up into Traffic Source types.  However, what many marketers want to see is the overall mix of Traffic Source types that lead to success over several visits.  For example, maybe Paid Search is always the last thing your visitors are doing before placing an order, but maybe the first thing they did was to click on an SEO keyword.  I touched upon this a bit in an old blog post on Cross-Visit Participation which you can review here.  If your organization has a desire to see a high-level view of which combinations of Traffic Source types lead to success, then Cross-Visit Traffic Source Attribution may be your answer.

Implementing Cross-Visit Traffic Source Attribution
If you have followed the instructions I laid out in my last blog post, then you have already done much of the work required to enable this feature in your SiteCatalyst implementation.  Now that you have an sProp that contains the Traffic Source type set on the first click of each website visit, all you have to do is the following:

1. Pass this value to an eVar (Most Recent Allocation)
2. Implement the Cross-Visit Participation plug-in
3. Have the eVar expire when your primary success event takes place (i.e. Orders)

As a refresher, the Cross-Visit Participation plug-in stores a list of elements, in this case Traffic Sources, with each visit so when a Success Event takes place, you can attribute the success to the current string of cross-visit values.  For example, if someone comes to your site three times, first from SEO, second from E-mail and third from SEM and then places an order, the current value in the eVar would be “SEO|E-mail|SEM.”  As time goes by, and you have more website visitors, the combinations that occur most frequently will rise to the top (web analytics darwinism?).  Usually the single Traffic Sources will be at the top (i.e. SEO by itself or SEM by itself), but what I look for are the combinations that are at the top of the list.  I sometimes even hide the individual items using the advanced search feature (Tip=Show if it Contains “|”) so I can see only multiple session Traffic Sources:

The only warning I will give about using this functionality is that it might burst the bubble of some of your co-workers who think that their Traffic Source type is the “end all, be all” of success.  In my experience, many people bounce around quite a bit and the results can surprise you!

First Touch, Last Touch
When it comes to attribution, many talk about First Touch, Last Touch and All Touch, meaning which Traffic Source was the first that visitors saw in a sequence leading to success, the that visitors saw last or a list of all of the Traffic Sources that influenced the success.  In SiteCatalyst, the easiest way to implement First Touch and Last Touch is to use two separate eVars.  Both capture Traffic Sources, but one has Original Allocation and a long expiration (never or say 6 months), while the other eVar is set to Most Recent Allocation and expires at the Visit.  However, you can also use the new Cross-Visit Traffic Sources eVar shown above to do this.  Simply download the above report to Excel and then isolate the first Traffic Source or the last Traffic Source and add up the Orders (or use a Pivot Table) to see the total for each Traffic Source.

Traffic Source Influence (All Touch)
For me however, I am most interested in seeing the total influence of a specific Traffic Source (All Touch).  While this is not readily available in SiteCatalyst (since Linear eVar Allocation only works within one visit), you can use the new eVar mentioned above to quantify the potential impact/influence of a specific Traffic Source Type.  Here is how you do it:

1. Download the report above to Excel (you decide if you want to include the single Traffic Sources or only when multiple exist – as shown above)
2. Use an Excel Formula to set the Traffic Source Type for a specific Traffic Source Type (i.e. SEO) in all rows where it is found (see green column below)
3. Create a Pivot table off this new column (i.e. SEO) and look at the total Success Events (Orders in this example) that are associated with a row that contains the Traffic Source Type you chose in step two (in this case 754,328)
4. Take that total (i.e. SEO Influenced Orders in this case) and divide it by the Total Orders (in this case 76.07%).  This will show you how much SEO influenced Orders such that SEO was involved in a visit that ultimately led to an Order.

Finally, if you want to see Cross-Visit Attribution of individual Campaign elements (Tracking Codes) instead of Traffic Sources, you can apply the same principles shown in this post and my last post.

Hopefully, between this post and my last post, you will be able to answer the nagging Traffic Source questions that come up from time to time and help your organization better understand where it should use its precious marketing dollars…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

One of the holy grails of online marketing teams is to find a way to track and measure a company’s Brand Awareness.  There are many different approaches to do this including the use of products like comScore, Compete, Twitter, but more often than not, it takes place offline in research studies.  While this trend is not going to change anytime soon, as a web analyst, you may be looking for data that you can collect to provide an estimate of your Brand Awareness.  Therefore, in this post, I wanted to share a “quick and dirty” way to use online data to see and trend the popularity of your company brand.  While this will not be a comprehensive approach, it might provide a basic starting point into the larger “Brand Awareness” puzzle.

Why Track Brand Awareness?
There are many schools of thought on whether it is even worthwhile to try and track Brand Awareness.  While people like us try to track everything, sometimes, there are things that are just not meant to be tracked.  If you own a website that sells stuff, then there is so much you can do with Web Analytics that tracking Brand Awareness is probably way down on the list.  However, there are many companies (i.e. B2B) that don’t sell products directly and inevitably the question arises:

“What is the true purpose of my website?”

If you are part of one of these companies, the above question is often followed with a spirited debate about whether success should be judged by lead counts, unique visitors, visitor engagement, etc…  At some point one Marketer will say that the website should be used to build Brand Awareness so success should be judged by increasing Unique Visitors, only to be countered by another saying that Unique Visitors don’t mean anything if they aren’t the right types…After about an hour of this, there is rarely a consensus on how to judge the success.  Soon you can see why this is not a popular topic in Web Analytic circles!

Amid all of this confusion, I think that people sometimes forget the real reason that people care about Brand Awareness.  At the end of the day, you want to measure how often consumers that are interested in a product/service that you provide think of you when the time comes to research or buy that product/service.  If you are doing a really good job at branding your company such that you are top of mind when consumers are at this stage, then one way or another you have done something right.  This is why I think there is some value in trying to quantify this and trend it over time.

So What Can Be Tracked?
So building upon the previous section, let’s assume that you don’t sell a product directly on your website, but that there are consumers out there who need your product/service (and have a blank checkbook in hand!).  Do you think they would:

1. Come to your office and ask to see your salespeople?
2. Pick up the Yellow Pages and give you a call?
3. Mail you a letter asking for information?

Maybe in the 1980’s, but not today!  Most are going to go to a Search Engine and a few savvy ones will go to Twitter.  So if the bulk of these will go to a Search Engine, and you are truly “top of mind” from a branding standpoint, they would probably search for your company name or the name of one of your products.  For example, if the consumer is looking for a “CRM” product they might search for “CRM.”  But if you are doing your job and have an awesome brand such that the first thing people think of when they think about “CRM” is your company brand (I don’t know…maybe something like “salesforce.com” ), then you would know that your brand is alive and kicking!

Following this logic, you can see that one interesting way to track your brand awareness is to quantify how often people are coming to your website from a list of “Branded” keywords of your choosing.  This list of keywords would include your company name, product names, key executive names, etc…  If you can aggregate these SEO keywords (I wouldn’t include Paid Search Keywords), then you have a number that you can trend over time.  Keep in mind that this is not an exact way to track brand awareness, but the logic behind it is that the more people [organically] search for your key brand phrases, the more pervasive your brand is out there.  In my consulting experience, I have often found that the number of SEO Brand Searches has a direct correlation with other key website success metrics.

So How Do I Implement SEO Branded Keyword Tracking?
In a perfect world, it would be great if there were an easy, reliable way to track how often your brand keywords were searched on all of the major search engines.  Companies like comScore try to estimate this, but it is not always accurate due to the panel-based methodology.  Another way I have tried to get at this data is through Google Trends, but I have not found ways to automatically export that data through API’s (if you know how please let me know!).

That being said, if you want to use SEO Branded Keywords to track your brand, take the following steps:

1. Work with your Marketing team to identify the list of keywords that everyone agrees are “Brand Keywords.”  In order to not distort the trend, it is important that you not continually add to the list so try and get an exhaustive list and stick to it for an extended period of time (i.e. readjust yearly).
2. The next step is to isolate these Branded Keywords in your SEO reports.  One way to do this is to add each one to the advanced search criteria for your SEO Keywords report (in the interface or ExcelClient), but if you have a lot this can be difficult.  My preferred approach is to pass SEO Keywords to a custom eVar.  Once you have done this, you can use SAINT to classify these keywords as “Branded Keywords” and then use the trended view of reports.  If you are using the Channel Manager plug-in or the Unified Sources Vista Rule, you should already have the data you need in a custom variable.
3. Once you have these branded keywords isolated, you can create a report that looks like this:

In addition, if you have specific products that are brands of their own, you may want to apply the same technique to the SEO Keywords that represent those brands and chart the Brand Awareness of your different products amongst each other (maybe inspire some competitiveness?).  For example, at Salesforce.com, we group our products into “Clouds” so you might chart the SEO Keywords related to the various “Clouds” on a graph to see how each is doing (shown with sample data here):

Don’t Forget About Twitter!
As mentioned earlier, another way to look at how your brand is doing is to look at Twitter.  This can be done using the Omniture Twitter Integration I proposed last year.  Implementing this provides you with a way to see how often your brand is being talked about so you can see a chart like this:

If you want to get fancy, you can even measure how your brand compares to the brand of your competitors on Twitter.  The graph below shows what I call “Twitter Competitive Share” and is calculated by the following formula:

Branded Tweets / (Branded Tweets + Competitors Branded Tweets)

The result is a chart that looks like this:

Final Thoughts!
Well there you have it, definitely not world peace, but if you are looking for some different ways to leverage your web analytics data, hopefully these ideas give you some food for thought.  If there are other ways that you are using web analytics data to track Brand Awareness, please leave a comment here as I’d love to hear about it…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.

While most of you reading my posts are focused on your public website, in this post I am going to share how you can leverage your web analytics skills internally at your organization.  Company Intranets are often times larger than the public website and using the tips I will share here, you can get some big visibility internally and become the hero of your HR team!

Why You Should Care About Your Intranet
Companies often spend a LOT of $$$ on building Intranets.  Unfortunately, not everyone at the company uses the Intranet.  If you can help your internal team show what is working and what is not working on the Intranet, you can help them to save a lot of money.  In addition to the altruistic reasons to track what happens on the Intranet, there are the following selfish reasons:

1. Tagging Intranets is a great way to try new things and get better at web analysis in a safe environment
2. Intranets often have low traffic volume so it is a great way to help cost-justify increased budgets for web analytics (“Mr. CEO, not only does this money go towards tracking the website, it also allows us to track our entire Intranet!” – Just don’t tell them that tracking the Intranet costs all of $1,000 in server calls!)
3. Showing people what is happening on the Intranet does wonders for people inside your organization understanding what the heck you do for the public website!

I have seen situations where a web analytics team has killed themselves trying to get senior executives to see what is taking place on the website and what improvements could be made based upon solid web analysis, only to see the same team get promoted or more budget after spending 2-3 weeks showing what takes place on the Intranet (something that they actually use)!  It sounds completely illogical, but I guess if you can’t beat them, join them!

Tracking Intranets
So what should you track on Intranets?  The following are my best practices learned working with a few large clients.  The one caveat to everything below is that you have to be sure to track all of this data in a different report suite than all of your other website data!

Employee ID
Depending upon the security policy of your company, ask if you are able to track down the the Employee ID level.  I tend to not do this since it can be a bit creepy, but it is technically possible and you can replace the Omniture Visitor ID with your own unique employee identifier.

Non-Personally Identifiable Employee Info
On each Intranet page, I recommend that you pass Department, Region, Business Unit, Office Location, Employee Band Level (i.e. VP, Manager), etc… to variables.  This will allow you to break down all Pages by these data points.  I generally pass these to an sProp and an eVar (save some time setting both through this post) and also recommend you put your top five of these into a 5-item Traffic Data Correlation.

Pages & Sections
Obviously, you want to pass in a unique page name for every Intranet page like you would any other website.  In addition, you should pass the Intranet section to the Site Sections (Channel) variable.  As always, I recommend that you enable Pathing on the Channel sProp so you can see how employees are navigating between Intranet sections.

Internal Search
Just like a public website, Internal Search is usually important on Intranets.  You should track Internal Search on the Intranet just as you would on a public website.  You can apply the same principles I mentioned in this Internal Search post.  This includes tracking what search terms people are looking for, but the beauty here is that you can see these by Department, Region, etc…

Timeparting
Many of my Intranet clients were keen to see when employees were accessing the Intranet, so I recommend you implement the Timeparting Plug-in.  This allows you to see what day of the week and time of the day employees access the Intranet.  Don’t forget to create a correlation between these sProps and your other ones so you can see when each page/section is accessed most often.

Internal Promotions
Much in the same way that I described Internal Campaigns in the past, Intranets may have promotional areas that try and entice employees to click.  You can track these the same way you would a public website.

Intranet KPI’s
The following are the types of KPI’s I have seen used for Intranets:

Page Views/Visit & Average Time Spent/Visit
Depending upon whether your goal is to get employees in and out or get them to spend more time reading Intranet content, you can use this calculated metric to see how you are doing.

Page Views (Event)
As I described in this post, I would recommend that you set a Success Event on each page.  Why?  Well let’s say you want to see how many pages on the Intranet a specific internal e-mail led to.  You can open the Campaigns report, find the e-mail and then see how many pages were viewed.  You can then use an eVar Subrelation to break this down by page name (as long as you pass Pagename to an eVar) to see the exact pages viewed.

Internal Searches
As you would on a website, you should track and trend the # of Internal Searches taking place on the Intranet.

Logins
If employees have to log into your Intranet, you can capture that as a KPI to see how you are doing at getting them to access the Intranet.  This can also be used for segmentation (i.e. show me all users who have not logged into the Intranet in the past 30 days…)

Custom KPI’s
Many times, Intranets are used to get employees to fill out forms, surveys, etc…  Each of these key actions should be captured with a Success Event and in the case of Forms, you should capture the Form Name in an eVar so you can break it down appropriately.

Employee Profile Views
As we march down the road of internal social media, it is fun to track how often each employee’s Intranet Profile is viewed.  Using new tools like my company’s upcoming “Chatter” product (see shameless plug video below!), we may be moving to a world where employees get “followers” so you can track how often people are looking at or following other employees.  This allows you to see who your employees think are important (which may not always align to the org chart!).

Final Thoughts
As you can see, if you know what you are doing for tracking a public website, tracking an Intranet uses many of the same principles.  If you are just getting started in web analytics, feel free to apply the above items on your Intranet as a testing ground before you tackle the public website.  If you have some other cool things you have done to track your Intranet, please feel free to leave a comment here…

Adam Greco is the Director of Web Analytics at Salesforce.com.  You can read his previous Inside Omniture SiteCatalyst blog at http://blogs.omniture.com/author/agreco/ and can follow him on Twitter at http://twitter.com/adamgreco.  Please send questions and comments to adam@the-omni-man.com.

Please note: I am no longer an employee of Omniture and the content/views expressed here are my own and not those of Omniture.