(Estimated Time to Read this Post = 5 Minutes)

Unless your website is very basic, odds are that you use some sort of navigation to help visitors find website content.  Usually navigation is in the header or left side of web pages.  Inevitably, there will be times when you are asked how often and in what ways visitors are using navigation.  In this post I will cover some common navigation questions and how to answer them.

Common Questions
So what are the common questions you may get around navigation.  Here are some that I have been asked over the years:

1. Which individual navigation links are clicked the most?
2. Which navigation areas are clicked the most?  This is usually related to the main section area, not individual links.
3. From which pages are visitors using each navigation link?
4. For what percent of website visits is navigation used?
5. In what order do website visitors use navigation links?
6. Which navigation links lead to key website success milestones being accomplished?

The following will show how to answer each of these questions:

Which individual navigation links are clicked the most?
In this scenario, people are looking to see which detailed navigation links are clicked the most.  In the image below, this would represent such links as “Sales Cloud 2,” “Service Cloud 2,” “Custom Cloud 2,” etc…

To answer this question, you should have your developer write code that will pass the name of the link to a Traffic Variable (sProp) when a visitor clicks on each link in your navigation.  In addition, I highly recommend that you have them include the high-level navigation area in the value passed to the sProp.  For example, when a visitor clicks on “Sales Cloud 2″ in the example above, I would pass the value of “products:sales cloud 2″ (I always use lower case since sProps are case-sensitive) to the sProp.  Passing the high-level area will ensure that your data is clean as there are times when the same link can occur more than once in a navigation structure.  When this is complete, you can view a report that looks like this:

Which navigation areas are clicked the most?
In this question, people are generally asking to see (in the example above) if the “Products” section is clicked more than the “About” section and if so, by how much.  The good news is that if you have done the previous step correctly, you can answer this question by creating a SAINT Classification which rolls up the values in the preceding report into higher-level buckets.  You can create this classification easily by exporting the above report to Microsoft Excel and splitting the column by the separator and using the first part as the high-level navigation name.  Here is what your SAINT file might look like:

After you create and process this SAINT file you will be able to see a new high-level navigation report that looks like this:

From which pages are visitors using each navigation link ?
In this scenario, people at your company may want to know what is the most common top navigation link clicked from the home page or from another page on your site.  To see this, you need to have setup a Previous Page sProp.  This sProp passes the name of the previous page to the current page which allows you to create Traffic Data Correlations between it and any other Traffic variable.  In this case, once we have a Previous Page sProp, we can correlate it to the Top Navigation Link sProp shown above to see what navigation links are clicked from each page.  For example, I can open up the Previous Page sProp within a report suite and then break it down by the new Top Navigation sProp…

…to see a report like this:

In this case, we can see that the “customers:india customers” Top Navigation link was only clicked 482 times from the home page.

In addition, since this uses a correlation and correlations are bi-directional, you can also use this to find out all of the pages from which visitors clicked on a specific navigation link:

In this case, we can see that the “customers:india customers” link was clicked a total of 957 times and then see the breakdown of pages visitors were on when they clicked it.  This can help your content people understand when visitors are reaching for the navigation…  Finally, if you look closely, you can see that the “SFDC:in:homepage” shows the same 482 clicks referenced above, but in this case we can see that it accounts for 50% of all clicks this link gets across the entire website…

For what percent of website visits is navigation used?
In some cases, you may be asked how often website navigation is used (in general).  One easy way to figure this out is to look at the the total Page Views from the first SiteCatalyst report shown in this post and divide it by the number of website Visits.  This can be done easily using the ExcelClient where you can pull a Visits data block and the report above and divide the two.  However, if you think you might need this on a recurring basis and if trending is important, I will show you another way to do this.  When visitors click on navigation links, in addition to passing the link name to a Traffic Variable as shown above, set a “Navigation Clicks” Success Event.  Once you have a Success Event, you can create a Calculated Metric that divides Navigation Clicks by Visits as shown here…

…which will allow you to see a report like this:

In what order do website visitors use navigation links?
If you are redesigning your navigation, a useful piece of data is the order in which visitors click on navigation links.  Do they always click on the first items in the list?  The ones that are farthest to the left?  Fortunately, if you have implemented the items above, you can see this by simply enabling Pathing on the new Navigation Links sProp created above.  This will allow you to view the Pathing reports including a Next Page Flow and Previous Page Flow just for navigation items:

Which navigation links lead to key website success milestones being accomplished?
Finally, I will occasionally be asked which navigation links are contributing to success.  To answer this question, all you have to do is enable Participation for your key metrics on the Navigation links sProp described above.  This will allow you to add a Participation metric to the first report shown above to see which links were in the flow of your key website Success Events.

Final Thoughts
Well, there you have it.  Everything you wanted to know about tracking your website navigation, but were afraid to ask!  If you have any comments/questions, use the form below.

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 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 = 2.5 Minutes)

In my last post, I discussed how you can see how much money you are leaving on the table when it comes to the online shopping cart.  While I still have the shopping cart on my mind, I thought I would follow this up with a concept I call Product Pathing.  Product Pathing answers one of the questions I get from time to time:  How can I see the order in which website visitors are looking at my products or product categories?  The following will provide details on why you might want to do this and its implementation.

Why Product Pathing?
So why would you want to implement Product Pathing?  Here are a few reasons:

1. Understanding how visitors jump between products or product categories which helps you understand how visitors navigate your products
2. Seeing what products are viewed concurrently which helps you understand what cross-sell/up-sell opportunities might exist
3. If one of your website goals is to get visitors to view multiple products, you can measure how you are doing against that goal

There may be more reasons, but the preceding items should help you build a case for implementing this, especially since it is not difficult to do.

Implementing Product Pathing
So the standard way to see the answers to the questions above is to use page name-based Pathing reports.  You might find the page name of a particular product and then look at Pathing reports to see what visitors did after viewing the product.  However, I find that this approach does not work because there are so many pages on the website that it is impossible to sift through them all and isolate just product pages.  Therefore, I am going to propose the following alternative solution:

1. On all Product View Pages, in addition to setting a Product View Success Event and the Products Variable, pass the Product Name (or ID if that is all you have) to a new “Product” Traffic Variable (sProp). Be sure that you pass nothing but the Product Name (or ID) to this sProp.
   
2. After that is done, enable Pathing on this sProp

Believe it or not, that is all you have to do! By passing only the Product Name (or ID) to this new sProp, you will have a clean, new sProp that allows you to see Pathing reports on only Products like this:

Moreover, keep in mind that you have access to all Pathing reports so you get the bonus benefits of seeing the following as well:

• How often visitors looked at Product X and then didn’t look at any other Products (Exit % – 42.32% in this case)
• All paths containing Product X (Full Paths Report)
• What Products visitors see (if any) between Product X and Product Z (Pathfinder Report)
• How often did visitors see Product X and then Product Y (Fallout Report)
• Which Products were viewed first the most often (Entries) or last the most often (Exits)

A Few Other Cool Uses of Product Pathing
In addition to this, there are a few other cool things you can do:

• Instead of passing Product Names (of IDs), you can pass in Product Categories to see the same data at a higher level
• Instead of passing Product Name values at the Product View Success Event, you can set an additional sProp in which you pass Product Names when the Cart Add Event is set to see the order in which visitors add products to the shopping cart

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.

In the Web Analytics space, we spend a lot of time recording and analyzing what people do on our website in order to improve revenues and/or user experience.  While this implicit data capture is wonderful, you should be supplementing it with data that you collect directly from your website visitors.  Voice of Customer (VOC) is the term often used for this and it is simply asking your customers to tell you why your website is good or bad.  There are two main ways that I have seen people capture Voice of Customer:

1. Page-Based Comments – Provide a way for website visitors to comment on pages of your site.  This is traditionally used as a mechanism to get direct feedback about a page design, broken links or problems people are having with a specific page.  Unfortunately, most of this feedback will be negative so you need to have “thick skin” when analyzing this data!
2. Website Satisfaction – Provide a way for visitors to rate their overall satisfaction with your website experience (vs. specific pages).  This is normally done by presenting visitors with an exit survey where you ask standard questions that can tell you how your website is doing and compares your site against your peers.

There are numerous vendors in each of these spaces and the goal of this post is not to compare them, but rather discuss how you can integrate Voice of Customer data into your Omniture SiteCatalyst implementation.  In this post, I am going to focus on the first of the aforementioned items (Page-Based Comments) and specifically talk about one vendor (OpinionLab) that I happen to have the most direct experience with (their headquarters was a mile from my home!).  The same principles that I will discuss here can be applied to all Voice of Customer vendors so don’t get hung up on the specific vendor for the purposes of this post.

Why Integrate Voice of Customer into SiteCatalyst
So given that you can see Voice of Customer data from within your chosen VOC tool, why should you endeavor to integrate Voice of Customer and your web analytics solution?  I find that integrating the two has the following benefits:

1. You can more easily share Voice of Customer data with people without forcing them to learn [yet] another tool.  People are busy and you are lucky if they end up mastering SiteCatalyst, lest you make them learn how to use OpinionLab, Foresee Results, etc…
2. Many Voice of Customer tools charge by the user so if you can port their data into SiteCatalyst, you can expose it to an almost unlimited number of users.
3. You can use Omniture SiteCatalyst’s date and search filters to tailor what Voice of Customer each employee receives.
4. You can divide Voice of Customer metrics by other Website Traffic/Success Metrics to create new, interesting KPI’s.
5. You can use Omniture SiteCatalyst Alerts to monitor issues on your site.
6. You can use Omniture Discover to drill deep into Voice of Customer issues

I hope to demonstrate many of these benefits in the following sections.

How to Integrate Voice of Customer into SiteCatalyst
So how exactly do you integrate Voice of Customer data into SiteCatalyst.  For most VOC vendors, the easiest way to do this is by using Omniture Genesis.  These Genesis integrations are already pre-wired and make implementation a snap (though there are cases where you may want to do a custom integration or tweak the Genesis integration).  You can talk to your Omniture account manager or account exec to learn more about Genesis.

Regardless of how you decide to do the implementation, here is what I recommend that you implement:

1. Set three custom Success Events for Positive Page Ratings, Negative Page Ratings and Neutral Page Ratings.  These Success Events should be set on the “Thank You” page after the visitor has provided a rating.
2. Pass the free form text/comment that website visitors enter into an sProp or eVar.  If they do not leave a comment pass in something like “NO COMMENT” so you can make sure you are capturing all comments.  If you are going to capture the comments in an sProp, I recommend you use a Hierarchy variable since those have longer character lengths vs. normal sProps which can only capture 100 characters.
3. Pass the actual page rating (usually a number from 1 to 5) into an sProp.  I also recommend a SAINT Classification of this variable such that you classify 1 &2 as Negative, 3 as Neutral and 4 & 5 as Positive.  This classification should take less than 5 minutes to create…
4. Use the PreviousValue plug-in to pass the previous page name to an sProp.
5. Create a 2-item Traffic Data Correlation between the Previous Page (step #4) and Page Rating (step #3).  This allows you to see what page the user was on when they submitted each rating.

All in all, this is not too bad.  A few Success Events and a few custom variables and you are good to go.  The rest of this post will demonstrate some of the cool reports you can create after the above implementation steps are completed.

Share Ratings
As I mentioned previously, you [hopefully] have users that have become familiar with the SiteCatalyst interface.  This means that they have Dashboards already created to which you can add a few extra reportlets.  In this first example, let’s imagine that you want to graphically represent how your site is doing by day with respect to Positive, Negative and Neutral ratings.  To do this, all you have to do is open the Classification version of the Page Rating report (can be an sProp or eVar – your call) and switch to the trended view.  You should have only three valid values and I like to use a stack ranked graph type using the percentage to see how I am doing each day as shown here:

This graph allows me to get a quick sense of how my site is doing over time and can easily be added to any Dashboard.

You can also mix your newly created Voice of Customer Success Events with other SiteCatalyst metrics.  For example, while you could look at a graph/trend of Positive or Negative Comments by opening the respective Success Events, a better way to gauge success is to divide these new metrics by Visits to see if you are doing better or worse on a relative basis.  The following graph shows a Calculated Metric for Negative Comments per Visit so we can adjust for traffic spikes:

Find Problem Pages
Another benefit of the integration is that you can isolate ratings for specific pages.  The first way to do this is to see which pages your visitors tend to rate positively or negatively.  In the following report, you can open the Rating variable report (or Classification of it as shown below) and break it down by the Previous Page variable to see the pages that most often had negative ratings:

This will then result in a report that looks like this:

Alternatively, if you want to see the spread of ratings for a specific page, all you need to do is find that page in the Previous Page report and break it down by the Rating variable (or its Classification) as shown here:

Share Comments
As noted above, if you capture the actual comments that people leave in a variable, you will have a SiteCatalyst report that captures the first 256 characters of the comments visitors enter.  This report duplicates scheduled reports from your Voice of Customer vendor in that it allows you to share all of the comments people are leaving with your co-workers.  However, by doing this through SiteCatalyst, you gain some additional functionality that some VOC vendors don’t provide:

1. You can create a Traffic Data Correlation between the Comments variable and the Previous Page variable so you can breakdown comments for a specific page.  Therefore, if you have users that “own” specific pages on the website, you can schedule daily/weekly reports that contain comments only for those pages so they don’t have to waste time reading all of the comments left by visitors.
2. You can use the Search filter functionality of SiteCatalyst to scan through all of the visitor comments looking for specific keywords or phrases that your co-workers may be interested in.  In the example below, the user is looking for comments that mention the words “slow” or “latent” to be notified of cases where the visitor perceived a page load speed issue:

Set Alerts
Another cool thing you can do with this integration is set automated Alerts in SiteCatalyst so you can be notified when you see a spike in Negative Comments on your site.  This allows you to react quickly to broken links or other issues before they affect too many visitors (and help avoid #FAIL posts in Twitter!).  Here is an example of setting this up:

Review Problem Visits using Omniture Discover
Finally, if you have access to Omniture Discover, after you have implemented the items above, you can use Discover to do some amazing things.  First, you can use the unlimited breakdown functionality to zero in on any data attribute of a user that is complaining about your site.  For example, if you had visitors complaining about not being able to see videos on your site, you might want to see their version of Flash, Browser, OS, etc… or even isolate when the problem took place as shown here:

Additionally, you can use Discover to isolate specific comments and watch the exact visit that led to that comment.  This is done through a little-known feature of Discover called the “Virtual Focus Group.”  This feature allows you to review sessions on your site and see the exact pages people viewed and some general data about their visit (i.e. Browser, GeoLocation, etc…).  While not as comprehensive as tools like Clicktale, it is good enough for some basic analysis.  Here is how to do this:

1. Open Discover and find the comment you care about in the custom sProp or eVar report
2. Right-click on the row and create a Visit segment where that comment exists
3. Save the segment in a segment folder
4. Open the Virtual Focus Group (under Pathing in Discover)
5. Add your new segment to the report by dragging it to the segment area
6. Click “New Visit” in the Virtual Focus Group
7. Click on the “Play” button to watch the visit

Now you can watch how the user entered your site, what pages they went to and see exactly what they had done prior to hitting the Voice of Customer “Thank You” page.

Final Thoughts
So there you have it, a quick review of some cool things you can do if you want to integrate your chosen Voice of Customer tool and Omniture SiteCatalyst.  This is by no means the only way to do this, but merely a few suggestions that I have found helpful over the years.  If you have done other cool things, please let me know…

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.

As I was at Omniture Summit last week, I couldn’t believe that it had already been a year since I started talking about integrating Twitter data into Omniture SiteCatalyst!  Since I haven’t seen many updates about this integration come from Omniture, I thought I would share a few enhancements I have made over the year in case any of them are useful to those out there using the integration…

Competitor Twitter Share
When I first envisioned importing Twitter data into SiteCatalyst, my primary focus was tracking how often my brand was mentioned and importing the brand-related tweets.  This allowed me to monitor my brand usage and filter tweet reports to send the right tweets to the right people based upon search phrases.  However, the more I thought about it, the more I realized that this integration could be used to keep tabs on competitors as well.  Instead of setting one “Brand Mentions” Success Event, you could expand the scope of what is tracked and also grab tweets mentioning your competitors and set a second Success Event named “Competitor Tweets.”  This second Success Event allows you to trend your competitors and track them on the same SiteCatalyst dashboard you use to track your own brand:

This led me to another cool idea…Why not track overall “Competitor Tweet Share” in which you quantify the % of tweets your brand gets in relation to those of your competitors?  This would allow you to trend your “share of twitter” for your narrow competitive niche.  To do this, create a Calculated Metric as follows:

This results in a graph like this which allows you to see when spikes occur to see if local events or press releases move the needle:

You can also set Alerts based upon this Calculated Metric to be notified when you are spiking or tanking in relation to your competitors!

General Tweets
The next concept I thought about was “general tweets” that were related to a business.  For example, if you are Coca-Cola, you might want to keep tabs on tweets mentioning “soda” or “soft drink.”  However, you wouldn’t want these counted as “Brand Tweets” or “Competitor Tweets,” so instead you can set a third Success Event called “Twitter General Mentions” and specify a list of keywords that should trigger this Success Event.  This allows you to see if a list of “general” keywords related to your business is rising or falling over time to gauge the general level of interest in your category over time:

#Fail
Lastly, I decided that the #Fail hashtag was too good to pass up.  If your brand is mentioned in the same tweet as the #fail hashtag, you probably want your social media team (if you have one!) to be alerted at once!  To do this, all you have to do is create a scheduled report with #Fail in the search box and schedule it to run hourly.  Unfortunately, SiteCatalyst delivers hourly reports whether there is data or not (to stop this please vote for this idea) so you may need your social media folks create an Outlook rule to filter the alerts that say “No Data” in the subject.

In addition, you can perform the same exercise for your “Competitor Tweets” since your social media team may want to be notified when your competitors have a #Fail hashtag in tweets mentioning their brand name!

So there you have it…a few minor updates or enhancements to the Twitter – SiteCatalyst integration.  If you have other ideas, please leave a comment here…Thanks!

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.