One of the features that I find deceptively difficult at times in SiteCatalyst is the use of the Search feature. I feel like there are many times I use this and end up messing it up. Therefore, I decided to do my best to share what I have learned about what works and doesn’t work in the hopes that it will save you aggravation and time! I also hope that many you can add a comment to this post with your tips and tricks so we can all learn something…

The Basics

First, let’s start out with the basics. Hopefully if you are a SiteCatalyst user you know that the search function is used to filter results in eVar and sProp reports. You simply enter a value and SiteCatalyst will look for those values in the active report and return those rows. This is handy because you can bookmark reports, make custom reports or add reports to dashboards after you have created the filter so that you never have to apply it again.

For example, let’s start with a Pages report like this:

Obviously we have pages from all sorts of countries, but if we only wanted to look at pages from England, all we would have to do is enter “SFDC:uk:” in the search box (top-right) and we would then see a report like this:

But what if we wanted to see pages from England or France? At this point we have two options. You can either enter “SFDC:uk: OR SFDC:fr” in the search box or use the advanced search editor. Here is what it would look like with the OR statement in the regular search box (look at the top-right portion):

However, believe it or not, if you change the “OR” to be a lower case “or” you will get no results! I kid you not! I call that an “Omniture-ism” and you just have to remember it…

The other way to get to the same report is to use the Advanced Search tool. You get there by clicking on the Advanced link to the right of the search box. Once there, you would enter the appropriate phrase in the first box, click the “+” sign to add another search criteria and then enter the second phrase so it looks like this:

However, it is important that you change the top drop-down box from the default of “if all criteria are met” to “if any criteria are met” or you will get no results.

If you wanted to look for cases where there were pages on the UK website that had the phrase “form” in the pagename, that would be a case where you would use the “if all criteria are met” option and your query should look like this:

This would result in a report like this:

Finally, we can come full-circle and get more advanced and use an “AND” statement in the standard box to get the same result. Here is what the search box would look like:

Again, keep in mind that the “AND” is case-sensitive…

More Difficult Searches

So now that we have covered the basics, let’s get a bit more advanced. First, let’s keep going with our example and say that we need to find all pages in the UK or France that have the word “form” in them. This gets a bit tricky because we are mixing OR and AND statements. Using the Advanced Search query builder, here is how you would enter it:

Conversely, if for some reason we wanted to see any UK Pages that had the phrase “form” in them and all France pages (not sure why, but this is just an example), we would enter this:

Which would result in a report like this:

Note that in this case we had to change the drop-down box back to the “any criteria” option since we did the AND statement within one of the criteria (hey…I told you this was the difficult part!).

The trick here is to combine any OR and AND statements into each row since each of the individual search criteria have to be either an “AND” or “OR” clause.

On a separate note, in the advanced search area, you can change the drop-down which defaults to “Contains” to “Does Not Contain” so if, for example, you wanted to see all UK pages, but exclude those that had “login” in the name you would enter the following criteria:

Note that for this instance, we need the “all criteria are met” option…

Finally, just for fun I entered the following phrase in the “simple” search box…

…and miraculously it produced the same results!! I decided to stop here before I broke anything, but you can feel free to see how far you can push this!!

But wait…There’s more! I have been amazed by how few people I meet know this next one… Imagine that you are looking at an eVar report and you have broken it down by another eVar via Subrelations. Here is an example where I have taken the Site Locale eVar and broken it down by Internal Search Term:

Now, let’s say that you wanted to do a search filter to only see items that mention “Outlook.” The easy way to do this is to just enter the phrase “Outlook” in the search box and SiteCatalyst will show any rows that have that phrase. But what if you wanted to see the phrase “Outlook” in just United States or Japan? No matter what you put in the search box, you will not get the results you are looking for (i.e. outlook AND “united states” OR japan). Would you know how to do this? Most people I meet don’t. Here is how…

When you are using a Subrelation report, you have to keep in mind that SiteCatalyst is running two reports and it doesn’t know which report you want to filter on. Therefore, we need to tell SiteCatalyst which report we want the search term to be associated with. You can do this in the Advanced Search area. When you have a Subrelation report, and you click on the Advanced Search area, you will see a new option that allows you to select one of the two reports being subrelated like this:

Most people haven’t ever noticed this new option so now that we know it is there, all we have to do is select the right report and then enter the search term in the right report and we can get our results. For the example above, we would enter “Outlook” in the search box next to Internal Search Term and “United States OR Japan” in the search box next to Site Locale like this:

Now, since we have been a bit more specific, we can get a nice, clean report like this:

Just keep this handy feature in mind the next time you are trying to search in a Subrelations report and pulling your hair out because you can’t get the results you think you should!

Even More Difficult Stuff

Phew! If you’ve made it this far, you are really devoted to your craft. We’re almost there so hang on…

The next thing that is important to know is that you can use wildcards in your searches. To do this, you use the “*” symbol in the search query. For example, if we wanted to find any pages in the UK that has the phrase “landing” somewhere in the name, we could simply do a search like this:

The next thing to know is that Omniture can be a bit quirky when it comes to the [SPACE] separator in the search box. Let me illustrate. If I enter the phrase “home page” in the search box, here are the results I get:

This seems strange to me since none of these pages have a space in them. That would make you think that a [SPACE] is a valid separator and that this query is the same as “home OR page” right? But if I use that logic and enter this phrase “SFDC:uk: SFDC:fr:” which is really just two phrases separated by a space (just with a colon in the phrase), I get no results. I am sure there is a logical reason for this, but I am not sure what it is. Maybe if SiteCatalyst sees a “:” or a “|” it acts differently (maybe Jorgen can enlighten us on this)?

To be safe, I use the next feature – using quotes – whenever possible. My advice is that if you ever have phrases with spaces in them that you enclose them in quotes and stick to using OR statements. In the preceding example, if I change my “home page” query to be “home page” in quotes, I get the expected result which is no results. Another lesson to be learned here is that you should, whenever possible, avoid putting spaces in values that you think you will search upon. I do my best to remove all spaces from page names since that is the variable I search on the most!

Finally, you can use the “-” sign to remove things from search results. This produces the same effect as using the “Does Not Contain” feature in the advanced search area. As in the previous example, if I want to see all UK pages, but not ones that have the phrase “login”, I can enter the following in the search box:

To see UK pages that do have login in the name, you can also enter this phrase:

But when the results come back, it will mysteriously remove the “+” sign and just uses space as the separator producing the same results.

Final Thoughts…
So there you have it! Pretty much everything I know about using search and advanced search in SiteCatalyst. Do you have any additional tips or tricks? If so, leave a comment here…Thanks!

At the end of last year, I spent a bunch of time showing how you could dissect your website forms to see which were performing well and not so well. While this post will be different from those, it is still related to website forms. In this post, I am going to share a concept that will let you determine which of those visitors seeing your forms have the intention to complete them and which do not. This information can be very valuable as I hope to show.

(Estimated Time to Read this Post = 4 Minutes)

Almost all websites have forms. Whether you are a B2B/Lead generation site, an eCommerce site, a travel site, etc… you most likely have forms. More importantly, you have people who don’t fill out your forms correctly and get some sort of error message. While error messages are a fact of life, in the web analytics/optimization world these are painful since you work so hard to get people to your site, to read your content and then agree to give you personal information. That is a lot of time and money spent only to have someone potentially abandon because they have problems with your forms. This represents your “low hanging fruit” so to speak – people who have already decided they like you and want to give you their information! In this series of posts, I am going to share some techniques for seeing how much of a problem your website has with form errors and in the next few posts I will cover some more advanced things you can do to diagnose these form error issues.

(Estimated Time to Read this Post = 4 Minutes)

Recently, I had the pleasure of meeting a web analyst whose business relied on a subscription model. In a subscription model, you often sell a product initially and then there is a subsequent Recurring Revenue stream (normally monthly). During the conversation, I explained how I would address this in SiteCatalyst and since it is a somewhat advanced concept, thought I would share the same info here in case there are blog readers out there who also have Recurring Revenue models.

Why Is Recurring Revenue A Challenge?
So why is tracking Recurring Revenue in SiteCatalyst difficult? As always, I like to explain through an example. Let’s imagine that you sell a popular CRM product that has an initial sale price and then a monthly subscription. A visitor comes to your website from a Bing keyword of “CRM” and they end up purchasing your product for $10,000. You can track this $10,000 sale online and attribute it to the Bing keyword. However, what do you do after one month? Let’s say the customer pays $1,000 each month after the initial $10,000. How do you attribute the recurring monthly $1,000 to the Bing keyword that originally brought the customer? Many clients I have seen stop at the initial sale, but this is problematic. What if there are some marketing campaigns that bring in a lot of initial sales, but those campaigns produce customers who quit the subscription after two months? Perhaps there are other marketing campaigns that generate lower initial sale amounts, but result in customers who are retained for several years. How do you compare “apples to apples” in this case if you cannot tie both initial and subscription revenue to the original marketing campaign?

The answer for most clients is to simply pass the original marketing campaign to their back-end system and do all of the reporting outside of a tool like SiteCatalyst. However, this has the following negative consequences:

1. As a web analyst, you are now out of the loop which is not good for your program (or your career!)
2. There are hundreds of online web-only data points that you know about the original sale (i.e. visit number, internal search terms used, internal promos used, etc…). Are you going to pass all of these data points to your company’s data warehouse and do analysis there? If you are a big company that may be possible, but what if you are a small or mid-sized business?

If you are like me, at a minimum, I like to have all important data in SiteCatalyst so I can have a seat at the table. With this in mind, the following section will describe what you need to do if you want to get Recurring Revenue into your SiteCatalyst implementation so it can be tied to the same data points as the initial sale.

Recurring Revenue in SiteCatalyst Reports
If you have read my past blog posts or even just my last post on Product Returns, you may know that one of my favorite SiteCatalyst features is Transaction ID (I suggest re-reading this post!). At a high level, Transaction ID allows you to set an ID associated with a transaction and later upload offline metrics that are dynamically associated with any Conversion Variable (eVar) values which were active at the time the Transaction ID was set. Just as Transaction ID was important to solving our Product Returns issue, it can be used similarly to solve the aforementioned Recurring Revenue challenge.

When visitors make their initial subscription purchase, you can set a Transaction ID value on the confirmation page. Doing this allows you to establish “key” that can be used later to upload Recurring Revenue and tie it to all of the eVar values associated with the original sale. Keep in mind that you will have to work with your Adobe account manager to get Transaction ID set-up. Additionally, Transaction ID is normally only used for 90 days, but in this case you will need to work with your account manager to get it extended perpetually (or for as long as you want to include Recurring Revenue). For example, if you want to associate two years of revenue with the eVar values that contributed to the original sale, then your Transaction ID data must persist for two years.

Once you have Transaction ID enabled and have started passing Transaction ID’s for online purchases, the next step is to create a new “Recurring Revenue” [Currency] Incrementor Event. Transaction ID uploads are similar to Data Sources and, as such, can only import data as Incrementor Success Event. Setting up a new Incrementor Event is easily done through the Admin Console.

Once you have Transaction ID set-up and your new Recurring Revenue currency Success Event, you need to generate a Data Sources template file that you can upload on a monthly/weekly/daily basis. This file will consist of each subscription account that is still active and the amount of [monthly] Recurring Revenue that should be recorded for each date. Normally, you will have subscriptions that expire at varying dates so you may decide to upload a file on a daily basis which represents all those who are starting a new subscription cycle on that date. The Data Sources template that you will create should have the following columns:

1. Date – Use the date that the new subscription cycle starts, not the date of the original sale. This date will determine which month the Recurring Revenue will appear in when using SiteCatalyst. NOTE: Please keep in mind that there is currently a SiteCatalyst restriction that you cannot upload a Transaction ID file that has dates spanning more than 90 days. You can upload dates that are more than 90 days old, but the date ranges for the entire file upload cannot be more than 90 days (kind of lame in my opinion!).
2. Transaction ID – The ID set when the initial subscription sale took place
3. Product Name/ID – Same value that was passed to the Products Variable during the original online subscription purchase
4. Recurring Revenue – Amount that the client will be charged for the next subscription cycle

When you are done, your Data Sources upload file might look something like this:

Seeing Recurring Revenue in SiteCatalyst Reports
Once you have successfully uploaded some Recurring Revenue data, it is time to see how all of this looks in SiteCatalyst. To do this, open the Products report and add Revenue and our new Recurring Revenue metrics. The report should look like this:

Next, you can create a Calculated Metric which combines the two metrics to create a “Total Revenue” metric as shown here:

Finally, since Transaction ID allows you to apply the eVar values that were associated with the original transaction to the new Recurring Revenue data, you can use Subrelation break downs for the Products report by Campaign and see both Revenue and Recurring Revenue (and the Total Revenue Calculated Metric)!

In the above report, we can see an example of the quandary I described in the beginning of this post. When we break down our first product (Sales Cloud) by marketing campaign, if we look at the Revenue column, it looks like we should be focusing our marketing spend on Bing Branded Keywords. However, when we add our Recurring Revenue, we can see that the majority of our Recurring Revenue and the most of our Total Revenue is coming from E-mail. Perhaps that is the best place to concentrate our marketing budget…

Final Thoughts
So there you have it. A simple, yet [hopefully] effective approach for making sure that you show all Revenue you are helping generate whether it takes place during the initial sale or subsequently… If you have comments/questions, please leave a comment here…

If you sell products or services on your website, you are probably working diligently to be sure that you are tracking the appropriate Orders, Units and Revenue associated with each product sold (you may even be doing some advanced stuff like I described here). Doing this allows you to see all sorts of wonderful things, like what pages lead to sales and what online campaigns have better ROI than others. However, one formidable challenge that web analysts don’t like to talk about is Product Returns. How often have you bought something online only to ship it back or return it to a brick & mortar store associated with the website? If customers return products in significant enough numbers, all of the great online data you have collected may be inaccurate.

I have seen some companies apply a “rule of thumb (dumb?)” in which they discount sales by 20% across the board to account for returns, but how does that help you determine if a specific marketing campaign is good or bad when your SiteCatalyst reports only show the good? By not tying product returns directly to their corresponding online sales, your web analytic reports will be inherently flawed. The truth is that I have seen very few clients who have adequately addressed this issue, so I thought I would suggest an idea that I think is an appropriate way to deal with Product Returns. Even if you don’t sell things through a shopping cart, I encourage you to read this post as its principles are applicable to any situation in which you have an online success that later is retracted in some manner offline.

Tracking Product Returns
The best way to understand the tracking of Product Returns, is through an example. Let’s pretend that are a web analyst for Apple and a first time visitor comes to the website from the Google paid search keyword “ipod” and purchases two iPods for $50 each. In SiteCatalyst, when we open the Products report, we would see $100 for the Product labeled “ipod” and if we broke it down by visit number, the same $100 would be attributed to visit number one. So far, so good…

However, let’s imagine that one of these iPods was returned to a local Apple Store. Now our reality has changed. The paid search keyword and first visit combination has now only led to $50, but SiteCatalyst still shows $100. If we create calculated metrics to compare our Revenue per Marketing spend, suddenly our ROI just got cut in half, but this is not reflected in SiteCatalyst. This might cause us to misallocate marketing dollars to campaigns that look to be good at first, but in reality are not as profitable as others when Product Returns are added to the mix (especially if we automate Paid Search using SearchCenter!). I don’t know about you, but I certainly wouldn’t want to be the one telling my boss to invest in marketing campaigns that turn out to be “duds!”

So how do we fix this mess? In order to track Product Returns, you’ll need to re-familiarize yourself with the Transaction ID feature of SiteCatalyst (I suggest re-reading this post!). At a high level, Transaction ID allows you to set an ID associated with a transaction and later upload offline metrics that are dynamically associated with any Conversion Variable (eVar) values which were active at the time the Transaction ID was set (phew!). In this case, you need to ensure that you are setting a Transaction ID value when the original online sale takes place. By doing this, you create a “key” that will allow you to upload Product Return data later and back it out of its corresponding online sale. Keep in mind that you will have to work with your Adobe account manager to get Transaction ID set-up and you’ll want to be sure that the Transaction ID table persists for as long of a time frame that you require to upload return data (default is 90 days, but this can be extended).

Once you have Transaction ID enabled and have started passing Transaction ID’s for online purchases, the next step is to create a new “Product Return Amount” [Currency] Incrementor Event. Transaction ID uploads are similar to Data Sources and, as such, import data as Incrementor Success Events. Setting up a new Incrementor Event is easily done through the Admin Console.

Once you have Transaction ID set-up and your new Product Return Amount currency Success Event, you need to use Data Sources to generate a Product Returns template which you can populate and upload on a daily/weekly/monthly basis. This file will contain the following columns:

1. Date – I suggest you use the date that the original purchase took place, not the date of the return, so there is no lag time. NOTE: Please keep in mind that there is currently a SiteCatalyst restriction that you cannot upload a Transaction ID file that has dates spanning more than 90 days. You can upload dates that are more than 90 days old, but the date ranges for the entire file upload cannot be more than 90 days (kind of lame in my opinion!).
2. Transaction ID – The ID associated with the original online sale
3. Product Name/ID – Same value that is passed to the Products Variable during the original online purchase
4. Product Return Amount – This is the total $$ amount per product that is being returned

When you are done, your upload file might look something like this:

Using Product Returns in SiteCatalyst Reports
Once you have successfully uploaded some Product Return data, it is time to see how all of this looks in SiteCatalyst. To do this, open the Products report and add Revenue and our new Product Return Amount metrics. The report should look like this:

Next, we can create a Calculated Metric which subtracts the Product Return Amount from Revenue to create a “Net Revenue” metric as shown here:

Finally, since Transaction ID allows you to apply the eVar values that were associated with the original transaction to the new “return” transaction, you can even use Subrelation break downs for the Products report by Visit Number and see both Revenue and Returns (and the Calculated Metric) by Visit Number (pretty cool huh?)!

Orders & Units (Advanced)
For those that are a bit more advanced, I wanted to let you know that the above solution does not back out Orders or Units for Product Returns. Backing out Orders is a bit tricky since you may only want to remove the Order if the entire Order is returned. Units is a bit easier as you can simply create a second Incrementor Success Event for “Returned Units” as we did above. However, I suggest that you start with Revenue since most of your questions around Product Returns will be related to Revenue.

Finally, SiteCatalyst does provide an out of the box Data Sources template for Product Returns which can be found in the Data Sources Manager:

However, I have not used this template myself and I would have the following potential concerns:

1. I don’t believe that this template uses Transaction ID which can be problematic as you will be unable to use the Product Return metric with all of your pre-existing eVar reports
2. It looks like this template uses the same Revenue, Orders and Units Success Events to back out Product Return data. I feel like this can be a recipe for disaster if something goes wrong. With my approach, the worst case scenario (if you upload some bad Product Return data) is that your new Product Return metrics are temporarily off. If you use the standard Revenue, Orders and Units metrics, a mistake can be fatal and hidden amongst your normal online metrics (I never mess with Revenue, Orders or Units!).

For these reasons, I suggest you talk to your Adobe Account Manager if you want to pursue this route.

Final Thoughts
So if Product Returns are something that you have to deal with, the above is my suggested way to handle them. Those of you who work in Retail day in and day out may have come up with some other ways to deal with Product Returns, so if there are other best practices out there, please leave a comment here. Thanks!

(Estimated Time to Read this Post = 4 Minutes)

One of the funny things about SiteCatalyst (you will notice I can’t yet bring myself to call it Adobe SiteCatalyst!) is that there are some really cool features that are hidden. In some cases, it almost seems like someone has gone out of their way to hide them, but I like to look at these “hidden gems” as a sort of rite of passage. In this post I will share some of the ones I have found and hope that maybe you know of others so that all of us can learn! Also, if you haven’t read my old blog post on SiteCatalyst Time Savers, I encourage you to do so!

The Magic Triangle and Checkboxes
If you are like me, it may seem like you spend most of your day adding/removing metrics from reports! This can be a very time consuming process, so you might as well be as efficient as possible. However, I often find that new SiteCatalyst users add extra steps to the process because they don’t know a few easy tricks in the Metrics window. The first trick is that you can change the column that is used for sorting by clicking the [very] little triangle next to each metric. It amazes me how many people add metrics, wait for the report to load and then click on a column to sort and wait for the report to load again! Multiply that by twenty reports and it becomes a real time suck! Instead, simply click the triangle until it turns green (soon to be Adobe red?) and you are done!

But wait! There’s more…You will also notice that there are a bunch of check boxes next to each metric. Those check boxes are used to choose which metrics you want to graph with your report. You don’t have to graph every metric in the report, which may confuse your audience. Also, I find that many clients don’t take advantage of the fact that you can display two graphs per report. To do this, all you need to do is check off one of the boxes on the left and right side. This is helpful if some of your metrics are numbers and them are percentages. It is the closest SiteCatalyst comes to a secondary axis you may be used to in Excel.

Remove Subrelation BreakDowns
If you frequently use eVar Subrelation reports, you may find that after breaking one eVar down by another eVar, you want to go back to the report before it was subrelated. For example, let’s say you have opened aTraffic Driver report and broken it down by Offer Type as shown here:

Now let’s say you change the date range and some other report settings and then decide you want to just see Traffic Driver Type by itself again. Unfortunately, if you use the trusty “Back” button in your browser, you will have to re-do all of those customized settings. However, there are actually two ways to remove this subrelation without losing any work.

The first way to do this is to click on the “Broken Down by:” link shown in red above. Once you click on this, you will see a list of all of your variables and you can choose the bottom-most one labeled “None.” The other way is to click the green magnifying glass icon you used to create the Subrelation and do the same thing as shown here:

Double Your Searching Pleasure
Another thing I have noticed that a lot of SiteCatalyst users don’t know is that you can add search criteria to two different variables if you are using a Subrelation report. To do this, click on the Advanced Search link and then you can use the drop down boxes to choose which variable/search term combination you want:

In this case, I have chosen to filter for all Traffic Driver Types containing “SEO” and can proceed to enter my search criteria for Offer Type…

Inherit Segments
If you use DataWarehouse or ASI, you probably spend a lot of time creating Segments. If so, you may find times where you want to re-use some parts of a segment you have already created. When I first started using SiteCatalyst, I did this by printing out my segments and re-creating them manually. This is both time-consuming and prone to error, so I found the trick to do this more efficiently:

There it is! See how easily you can copy an existing segment? Do you see it? If not, would you believe me if I told you that there are actually two different ways to re-use segments on the above screen?

The first way to do this is to click the icon to the right of the Segment title. This will pop-up a new window which allows you to pick an existing segment you want your new segment to be based upon.

The second way to do this is to use the Segment Library. You can access the library by clicking its name next to the “Components” item. The Library is used to store commonly use segment building blocks. In the example below, I have created a Page View container that looks for Pages where the IP Address Geography is in the United States. By dragging this over to the Library, I can re-use this anytime I am creating a new segment.

Filter Report Suites
If you have Admin rights to your SiteCatalyst implementation and deal with a lot of report suites, the Admin Console quickly becomes one of your best friends. One of the most time consuming Admin Console tasks is finding the report suites you are looking for among all of your report suites. I frequently see people scanning up and down over and over hunting for the report suites they need. Fortunately, there is a much better way that is somewhat hidden – using the “Saved Searches” feature of the Admin Console. Using this feature you can create a filter to find report suites such that even if you add new ones, they will be added to your saved search if they meet the criteria.

Here is a real-life example. When I joined Salesforce.com, we had a lot of report suites and I began creating new report suites. When I created the new report suites, I simply added the phrase “New” to my new report suites titles. Once I did this, I clicked on the “Add” link within the Saved Searches area of the report suite manager and created a “Saved Search” rule like this:

Keep in mind that this is a very basic rule. You can actually add multiple criteria items and can build rules that take into account any of the following report suite criteria:

Lastly, if you are an Admin, be sure to read my past blog post with even more Admin Console Tips.

Final Thoughts
So there you have it. As you can see, none of these items are critical showstoppers, but I have found that knowing them can help speed up your day and give you SiteCatalyst bragging rights! Do you know of others? If so, please share them here as comments!

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.

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…

[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!

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

(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…

(Estimated Time to Read this Post = 3.5 Minutes)

From time to time, I will see people talking about some of the limitations of SiteCatalyst Traffic Data Correlations on Twitter and blogs. Below is the most common request I see:

For those of you not familiar with Correlations, they are a way to break one Traffic Variable (sProp) down by another sProp, assuming that both are set on the same page (image request). Unfortunately, in SiteCatalyst, the only metric you can see for Correlations are Page Views. In the example below, I can use a Correlation to see what page a visitor was on when searching for a specific phrase in the internal search box:

While this is handy, what if I wanted to see how many Visits people searched for this phrase from each page or better yet, how many Daily, Weekly, Monthly Unique Visitors did this? Currently, the only way to get at this information is to use DataWarehouse or Omniture Discover. However, the following section will show you a handy little hack to get this information directly from SiteCatalyst…

The Hack
So in this scenario, we will say that we want to see how many Weekly Unique Visitors searched for the phrase above from each page on our website (as in the example above). Here is the trick to doing this:

1. Just as in a Correlation, you must have both data points you want to correlate available on the same page. In this example, it is Previous Page Name (from the GetPreviousValue JavaScript Plug-in) and the Internal Search phrase
2. Once you have the two data points available, create a new Traffic Variable (sProp) and concatenate the two values using a separator. In this example, if the user searched for the above phrase from the Japan Customers page, the value would be “キーワード検索:SFDC:jp:customers”
3. After you have passed the concatenated value to the sProp, contact your Omniture Account Manager and tell him/her that you would like to enable Visits, Daily Unique Visitors, Weekly Unique Visitors, etc… on that sProp

When all that is done, using the example above, you will have a report that looks like this:

The confusing part of this hack is that you won’t actually use a Correlation report anymore. You will no longer open one report and break it down by another report, but instead you will simply open the new sProp report and add the metrics you want to see. In the report above, I have added Page Views, Visits and Weekly Unique Visitors and searched on the specific Internal Search phrase for which I am interested.

However, as you can imagine, you could have a lot of unique values in this report. One caveat is that you need to make sure you don’t exceed the SiteCatalyst variable limit which is 500,000 unique values per month. In this example, you would want to make sure that the combination of Page Name and Search Term does not exceed 500,000 per month, but for most sites this shouldn’t be a problem.

Another thing to keep in mind is that the above scenario is just one example. This “hack” is by no means limited to Internal Search Terms and Pages. Here are some other examples of what you can do with this “hack:”

• Unique Visitors/Visits who saw a specific page by visit number (i.e. Home Page:Visit 1, Home Page: Visit 2, Demo Page: Visit 1, etc…)
• Unique Visitors/Visits who searched for a term by visit number (i.e. Pricing: Visit 1, Pricing: Visit 2, Demo: Visit1, etc…)
• Unique Visitors/Visits who saw a specific page by country (i.e. Home Page:US, Home Page:UK, Demo Page:US, etc…)
• Unique Visitors/Visits viewing a specific product page by search engine (i.e. Product X:Google, Product Y: Google, Product X:Yahoo, etc…)
• Unique Visitors/Visits viewing a specific product page by search keyword (i.e. Product X:walmart, Product Y:walmart, Product X: walmart.com, etc…)

These are just a few examples and my advice is to look at whatever you are currently correlating (in Admin Console) and determine the items for which you would like to see Visits and Unique Visitors.

Advanced Users
For advanced users out there, I wanted to call out a few more things you can do with this concept:

• If you don’t have a lot of unique combinations, you can add multiple correlations to the same sProp and use the search box to find the item combinations you need. For example, you may use the Internal Search & Page example shown above, but also store Page Name & Visit Number combinations in the same sProp. As long as all of your data is underneath the 500,000 unique value monthly limit you are ok. Alternatively, you can multiple sProps assuming you have enough variables that can have Visits and Unique Visitors enabled remaining in your contract.
• With this alternative approach, you can also view Trending Reports for each of your combinations if you enable Pathing. This means that you can trend Visits or Unique Visitors for any combination (i.e. Weekly Unique Visitors who view Product Page X on Visit Number 1 over the last 90 days). This temporarily solves the following Idea Exchange item (Allow Trended Versions of Correlation Reports)

Final Thoughts
So there you have it. Just a quick “hack” that allows you to get a bit more information out of SiteCatalyst. In the future, perhaps Omniture will allow you to see Visits and Unique Visitors right form the normal Correlation interface (please vote for this here: Provide Visits and Unique Visitors in Correlation Reports), but in the meantime, hopefully this will help bridge the gap…

In my last post, I covered Comparison Reports and showed the basics on how to use them. In this post, I will cover a few advanced ways that I use these reports related to Pathing reports.

Date-Based Pathing Comparisons
For many of the analyses I perform, I like to see how website visitors are navigating my site via Pathing reports. However, these reports are usually static – for a specified time period. Therefore, what I like to do is to view a few of the standard SiteCatalyst Pathing reports in a way that I can see how Pathing is changing day to day, week to week or month to month. While SiteCatalyst doesn’t let you use comparison reports in all Pathing reports, there are a few key ones that do allow you to compare date ranges – Next/Previous Page Report and Full Paths Report. The following are some examples of how you can take advantage of this.

Next/Previous Page Report
Let’s say that you have a key page like your home page and you want to see what pages visitors are going to from it in March vs. February. To do this, simply open the Next Page report (under Pathing) and select the Home Page as the page of focus. Once there, you can use the calendar as shown in my last post to select two different time periods (February & March in this case) and see the report:

When looking at this type of report, I tend to change the graph so I am looking at percentages instead of the raw numbers since that is what I care about the most. Also, you can apply Normalization to this report by selecting it in the report settings (to learn about Normalization see my previous post). Finally, the same principles apply if you want to see the above report as a Previous Page report to see how people are getting to a specific page differently between two different time periods.

Full Paths Report
In this next example, let’s say that we don’t have a specific page in mind, but rather, want to see how all of our paths are changing between two different time periods. To do this, you can use the Full Paths report (found under Pathing). This report shows all of your paths and can be quite massive, but I tend to use it to see just my top few site paths. Using the date comparison feature, you can see how the paths of the same site differ between two distinct time periods. To do this, simply open the Full Paths report and use the calendar tool to select your two date ranges and you will see a report that looks like this:

As you can see, this report allows you to look at your top paths and see if there are any significant changes you should know about. This is handy if you have a special promotion on a page and want to see how it is changing your Pathing behavior. Finally, there are some cool advanced things you can do in the Full Paths report like limiting paths to a specific number of pages and specifying an entry page, but you can explore that as needed.

Site-Based Pathing Comparisons [If you don’t have multiple report suites or ASI slots you can skip this section!]
As I mentioned in my previous post, the other type of comparison report you can create is one based upon report suites/ASI slots. These comparisons allow you to see how one data set is doing compared to another. A perfect example of this is if you are part of a multinational and have basically the same website for different geographies. Other examples might be a company that has multiple divisions/products that are similar enough that they use the same type of website template. In both of these cases, you have the same page names, just different locales or products and might want to see how one is doing vs. another. In this example, we will assume that a website is basically the same except for the site geography and that we want to see how people are navigating from the home page of two different geographies. If you are clever, you will quickly realize that this might be problematic since each website might have different page names including the site locale. This why in my Page Naming Best Practices post I recommended that you set a custom sProp without the site locale (and have Pathing enabled). Here is a quick excerpt from that post:

One wrinkle that can emerge are cases where you have multiple geographic websites. For many companies, this results in a similar version of the website, but translated into different languages. If you have this situation, I recommend tweaking the above page names to include a site locale indicator. For example, each page in the UK site should have “uk:” in the page name and so on. When this is done, your page names might look like this:

[Advanced User Alert – If you have multiple site locales, I also recommend passing the page name without the site locale to a different sProp (with Pathing enabled) so you can see how a page does across all site locales (i.e. Participation). I also like to pass the site locale by itself to a separate sProp so in a global report suite I can create correlations between sProps and other variables (i.e. Internal Search Terms).]

If you have done this, then you can simply open the next page report for this custom sProp (that has no site locale) and choose the “Compare to Site” option and select the sites that you want to compare. In this example, I am looking to see what visitors from Spain and Italy do when they are on the home page. In this case, I am normalizing the data so I can get a better view of the next page differences between the two sites even if they have different amounts of traffic. As mentioned previously, you can do the same thing for Previous Page and Full Path reports…

Using Comparisons With Other Custom sProps
Lastly, if you have Pathing enabled for other custom sProps, you can take advantage of this functionality there as well. Let’s say you have Pathing enabled on what internal search terms people look for on your website. You can compare this between websites or for two different time periods. Below is an example of looking at the same internal search terms for two different time periods.

Final Thoughts
These two posts should cover pretty much all you need to know about SiteCatalyst comparison reports. If you are using them in any other creative ways, please leave a comment here. Thanks!

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!

Recently on Twitter, there was some discussion about the best way to name your pages in SiteCatalyst. Therefore, I thought I would share some thoughts on the best way to set names for Pages, Sections, etc… when using SiteCatalyst. Hopefully, you are already doing most of what I will mention here, but just in case, here are my suggestions. Also, keep in mind that many people have different styles of naming pages (and feel very strongly about it!) so what is shown here are my personal preferences…

Naming Pages
If you don’t manually apply a “friendly” page name to the s.pagename Traffic variable (sProp) in SiteCatalyst, Omniture will capture the URL by default. This is not ideal for the following reasons:

1. URL’s can be very long and exceed the sProp character limit (normally 100 characters)
2. URL’s can be hard to understand by end-users
3. URL’s can have querystring parameters that get cutoff which means that many pages get treated as one page name in SiteCatalyst (which ruins Pathing reports)
4. URL’s can have a http:// and https:// versions which means two versions of each URL which subdivides Pathing, Unique Visitors, etc…

Therefore, it is highly recommended that you name your pages the way you would like to see them in the Pages report. I generally recommend naming pages based upon directory structures or manually by adding fields to your content management system. Once you figure out how you will name your pages, the key question is what should you name your pages. Here are my recommendations:

• Make sure all pages within each unique website have a common identifier. For example, if you have three distinct websites that serve different purposes, I like to assign a value in the page name for each website so I can easily filter those pages in a global report suite (one that has data from all websites). For example, for the Omniture website, I would have an identifer for the public (marketing) website (i.e. “omtr:”) and a different identifier for say the Idea Exchange (i.e. “ideas:”).
• I like to include the section in the page name when possible. For example, if the Omniture public website has a section for “Products” and another for “Services,” I would include those in the page name (i.e. “omtr:products:” or “omtr:services”). This allows you to easily filter Page reports to get all of the pages within a section. Some companies also include the sub-section in the page name which is fine as long as you don’t hit the sProp character limit.
• Make sure all pages have a unique name. If you have two pages with the same exact page name, SiteCatalyst will treat them as a single page and all stats for that page will be merged (including paths).
• Be mindful of case-sensitivity. sProps are case-sensitive, so if you have the same pagename value, but in different cases, you will get two distinct page names. A common best practice is to force upper or lower case in the JS file to avoid any issues.

So if you put all of these ideas together, a list of your pages might look like this:

One wrinkle that can emerge are cases where you have multiple geographic websites. For many companies, this results in a similar version of the website, but translated into different languages. If you have this situation, I recommend tweaking the above page names to include a site locale indicator. For example, each page in the UK site should have “uk:” in the page name and so on. When this is done, your page names might look like this:

[Advanced User Alert – If you have multiple site locales, I also recommend passing the page name without the site locale to a different sProp (with Pathing enabled) so you can see how a page does across all site locales (i.e. Participation). I also like to pass the site locale by itself to a separate sProp so in a global report suite I can create correlations between sProps and other variables (i.e. Internal Search Terms).]

One other item related to site locales is the use of different languages or translated pages. While I do recommend different page names for each site locale, I do not recommend that you have different page names for the same page translated into different languages. You can read more about this in my old Foreign Language post.

As you can see, this doesn’t look all that hard to implement, but by using the above items you can easily:

• Filter pages for a website (i.e. omtr: vs. ideas:)
• Filter all pages for a specific section (Search contains :services:)
• Filter all pages for a particular site locale (Search contains “:uk:”)
• Filter on a combination of the above items. For example, let’s say you wanted to see all UK Product pages (Search contains “:uk:products:”)

When you look at this it makes common sense, but I can’t tell you how many clients I ran into that had incomprehensible page naming which made everything more difficult. Even if it means losing some historical page data, I always recommend that clients have good page names as it pays great dividends down the road…

Site Sections (s.channel)
After dealing with Page Names, the next thing I like to tackle is Site Sections. These are useful if you want to see how visitors are navigating your website at a higher level than pages. If you have good page names, you really should be able to build your Site Sections by setting it equal to the page name minus everything past the last “:” symbol. For example, in the example above, if the page name is “omtr:us:services:consulting” then the section would be “omtr:us:services” (you choose whether you want to include the “:” at the end or not). I have seen many clients that can set Site Section values automatically based upon good page names which saves a lot of development work and ensures consistency.

Site
One variable that many clients forget to include is the Site variable. Essentially, what you are doing here is to pass in a value for the website by itself into an sProp. In the example above, this would mean values of “omtr” or “ideas” by themselves in an sProp. Doing this allows you to see total Visits and Unique Visitors by site in one report and when Pathing is enabled, allows you to see how people are navigating from one website to another. Again, if you have good page names, you can set the Site variable by simply grabbing everything before the first “:” symbol in the page name.

Page Type
Those of you who have read my previous blog posts know that I am a fan of setting a Page Type on each page that represents the function of the page. I won’t rehash this topic, but recommend you check out my prior post on this.

Advanced Stuff
For those who are a bit more advanced in their SiteCatalyst usage, you can check out the following page related advanced topics:

• Setting a Page View Event
• Passing Page Name to an eVar
• I recommend passing the URL to a custom sProp. This is a good thing to do since URL is not available in DataWarehouse by default. There are also cases where being able to segment on the URL can get you out of a jam…

So there are a few items related to naming Pages, Sections, etc…Let me know if you find these tips helpful and/or if you have come up with best practice suggestions of your own you’d like to share…

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…

Recently out on Twitter, someone had asked how you can calculate Bounce Rate for various Traffic Sources. In the past I have shed light on how you can create Bounce Rates for campaign elements, visitor types, etc…, but I failed to share how to see Bounce Rates for SEO, SEM, E-mail, etc… In this post I will share the way to do this. If you are reading this post, odds are you know what Bounce Rates are, but quickly, it is the percent of visitors who saw one thing (normally page or section) and then went no further. If you need a refresher on Bounce Rates, you can look at my old Bounce Rate post, or better yet, check out Avinash’s post on Bounce Rates.

Why Traffic Source Bounce Rate?
Often times, marketers want to see how each of their disparate online marketing channels are doing when compared to each other. Most will rate them by how well they perform against the website KPI’s. However, due to its popularity, may want to see the Bounce Rate for these online traffic sources. While my Segment Pathing post showed you how to see the bounce rate for a specific traffic source element (i.e. SEO Google keyword going to your home page), what if you want to see the total Bounce Rate for SEO or SEM? Unfortunately, that doesn’t come out of the box in Omniture SiteCatalyst (though you can derive it in Omniture Discover). I am not going to tell you whether the Traffic Source Bounce Rate is a valid metric as that depends upon your business objectives, but the next section will outline how to implement it.

Implementing Traffic Source Bounce Rate
So how do you implement Traffic Source Bounce Rate? Like any Bounce Rate metric, you need to be able to calculate Single Access and Entries. In SiteCatalyst, that means you need Pathing to see these metrics, so you know right off the bat we are going to need a Traffic Variable (sProp). Once you have identified which sProp you are going to use and had Pathing enabled for it by ClientCare, you need to find a way to get the various Traffic Sources that you use into that sProp. The ones I commonly use are:

1. SEM
2. SEO
3. E-mail
4. Display Ads
5. Affiliates
6. Social Media
7. Other Websites
8. Typed/Bookmarked (a.k.a. the rest)

The key to this solution is that you need to find a way to identify the Traffic Source of the first click to your site. This can be done manually in your JS file or semi-automated using the Unified Sources VISTA Rule or the similar Channel Manager Plug-in. Regardless of the method, what I try to do is to find something that uniquely identifies each online marketing channel. Usually the best way to do this is through a query string identifier.

Here is how I do this:

SEM – If a click to your website comes from a Search Engine, you should have an identifier (i.e. ?s_kwid=) in the URL. If you do, you know the Traffic Source is SEM.

SEO – If the click comes from a Search Engine, but doesn’t have that identifier, it is SEO!

E-mail – When you send e-mails, you should be tracking the inbound clicks with a query string parameter. If so, set it to something unique for e-mails (i.e. ?eid=) so you know that if you see that identifier in the URL, the Traffic Source is E-mail.

Display Ads – In a similar manner, if you are buying Display Ads, you normally get to choose the destination URL. Therefore, you can set the destination URL on your site to have another unique identifier (i.e. ?displayid=) so you know which clicks have come from Display Ads.

Affiliates – See Display Advertising (i.e. ?affID=)

Social Media – This one is a bit trickier, but what I do is make a list of the key Social Media sites I want to track and when I see the referring URL contains one of those URL’s, I set the Traffic Source to Social Media.

Other Websites – If all of the above criteria have not been met, but there is a referring URL, set the Traffic Source equal to “Other Website.”

Typed/Bookmarked – If none of the preceding conditions have been met and there is no referring URL, set the Traffic Source to “Typed/Bookmarked.”

Phew! It sounds difficult, but you should be using different query string parameters anyway in your campaigns area and any good JavaScript developer can do this somewhat easily. It does require coding (which I don’t do myself!), but maybe Omniture will provide this out of the box one day…

But Wait…There’s More!
Believe it or not, you are not done yet! Once you have found a way to distinguish the Traffic Source and are passing that into an sProp on the first page of every visit, you are 90% of the way there. The last step is a bit confusing (techie alert!). In order for SiteCatalyst to know if a visitor made it beyond their first page of the visit (hence, did not “Bounce”), it needs to see a different value in the sProp at some point during the Visit. If it doesn’t see another value passed to the sProp, it will assume they didn’t see any other pages and exited the site (your boss won’t want to see a 100% Bounce Rate for every channel – trust me!). Therefore, when a visitor navigates to a second page in the visit (any page – doesn’t matter which one), you need to force a “dummy” value into the same sProp that you previously passed Traffic Source. My clever developer, passes in the value “Did Not Bounce” as the dummy value. I will let those more technical than me discuss the best way to pass this dummy value, but once you have done this, you will have a new sProp that has one value for each of your Traffic Source Types and one extra one for the dummy value. Since this sProp has Pathing enabled, you will have a Single Access and Entries metric for each of your Traffic Sources and can now calculate Bounce Rate (I recommend using Advanced Search to hide the dummy value and save it as a Custom Report so you don’t confuse your users).

For the most part, this sProp won’t have much value beyond calculating the Bounce Rate since it is really only set on the first page of the visit, but here are some additional goodies:

1. Use Trended reports to monitor Traffic Source Bounce Rates over time
2. Enable Daily, Weekly, Monthly, etc… Unique Visitors on the sProp to see Uniques for each Traffic Source
3. Correlate it to any other sProps that are most important on the first page of the visit (i.e. Referrer, Visit Number, etc…)
4. There is one more cool thing you can do with this, but it is so cool, I need to do a full post about it so stay tuned for my next post…

Final Thoughts
Well there you have it. I wish it was not so convoluted, but don’t shoot the messenger! If anyone else knows an easier way to do this, I am all ears. I apologize for this being a bit more technical/complicated than most of my posts, but I don’t know of a non-technical way to explain this. Let me know what you think…

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…

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 Salesforce.com Chatter, 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…

In my last post, I described some of the benefits of using a Page View Success Event. In this post I will continue along the same theme by describing the benefits/uses of a Page Name Conversion Variable (eVar). I recommend you read my last post on the Page View Success Event prior to reading this post as the two go hand-in-hand.

Setting a Page Name eVar
Setting the Page Name in an eVar, while somewhat nontraditional, can be used for many different purposes. In this post I will cover just a few, but I am sure those reading this can come up with many more. The implementation of this couldn’t be easier. Simply pass the s.pagename value to an eVar and you are done! The following sections will outline how I use this variable once it is set.

Campaign Pages
Let’s say that you are running a bunch of online marketing campaigns and you want to see how many pages on the website people coming from each Campaign Tracking Code view. In SiteCatalyst, the main way to figure this out would be to use DataWarehouse, ASI or Discover unless you read my last post and had set a Page View Success Event. But now let’s take it a step further. What if you want to see the pages that visitors from each Campaign Tracking Code viewed on your website. Easy right? Not so fast. There is really no easy way to see this in SiteCatalyst using out-of-the-box reports. One way to do this would be to use the Get&Persist Plug-in to pass the Campaign Tracking Code to a Traffic Variable (sProp) on each page of the visit and then use a Traffic Data Correlation to correlate this new sProp with the Page Name variable, but that is a lot of work! The other way is to use a Page Name eVar. By default, your Campaign Tracking Code report will store and persist the Campaign Tracking Code for multiple page views (you choose your time frame in the Admin Console) so if you begin to store Page Names in another eVar, you will have an intersection between Page Name and Campaign Tracking Code on each page. That allows you to use a Conversion Variable Subrelations report to see all Pages viewed by visitors coming from each Campaign Tracking Code You can see this by opening up the Campaign Tracking Code report, selecting the Page View (Event) metric and clicking the icon next to a specific Tracking Code to break it down by the Page Name eVar. Once you have done this, you should see a report like this:

Channel Pages Tracking
If you role up your Campaigns to higher-level Marketing Channels using SAINT Classifications you can use the concept from the Page View Event post to see how many pages are viewed on your site after visitor arrive from each Marketing Channel.

You can then break this report down by the Page Name eVar to see the most popular pages for each Marketing Channel:

While this is not as granular as viewing Pathing by Campaign (as I demonstrated in this post) , it can give you a high-level view of what pages are popular for each different marketing channel. If you are using the Unified Sources DB VISTA Rule or Channel Manager plug-in, it gets even better as you can see what pages people coming from another website or SEO are viewing on your website by breaking down a particular SEO keyword or external website link by Page Name:

Internal Search Follow-On Pages
If you are properly tracking Internal Search on your website, you should have Internal Search Terms stored in an eVar so you can use this concept to break down Internal Search Terms by this new Page Name eVar (while using the Page View Event) to see what pages visitors view after they search on each specific Internal Search Term:

What Page Does Success Take Place?
Another side-benefit of setting a Page Name eVar is that you can see on which page a Success Event takes place. For example, if you set a “File Download” Success Event and a file is available on several pages, you can subrelate each file name with the Page Name eVar to see which page is the most popular for downloading each file.

Conversion Variable QA
Finally, there is a completely different use for the Page Name eVar – Quality Assurance. Often times, you will run into situations where you have eVars that have bad data or no data at all (the dreaded “None” row!). Often times, these issues are hard to troubleshoot. However, if you have a Page name eVar, your life is much easier.

Let’s say that you have forms on your website and when visitors complete a form, they are required to enter a “Company Size” field which is stored in an eVar. However, there are many cases where you are seeing the Form Company Size eVar with no data. This might mean that IT forgot to make the field required on some of the Forms (would never happen right?). How do you figure out which forms are causing the issue? All you have to do is the following:

1. Open the eVar report that has data issues with a relevant Success Event metric (Form Company Size and Form Completes in this example)
2. Find the row that has bad data or no data (“None” row)
3. Click the breakdown icon to break the report down by the Page Name eVar
4. The resulting report (see below) will show you a list of Page Names where SiteCatalyst set the Form Complete Success Event, but did not have a corresponding Form Company Size eVar value

You can then send this report to your IT team to help them find pages where there may be tagging issues. You could even schedule this as a recurring report to you and IT so you are alerted when similar issues arise in the future, which helps with overall data quality. Keep in mind that this will only work if the eVar you are looking at has Full Subrelations or you add Full Subrelations to the Page Name eVar (see below).

Final Thoughts
As you can see, there are many different uses of this functionality. The following are some final pointers related to this topic:

1. As previously noted, if you plan to use the Page Name eVar extensively for testing, I would recommend that it have Full Subrelations so you can QA all eVar reports, not just those that already have Full Subrelations.
2. In one of the rare times I ever tell clients to do this, I would recommend that you set the Page Name eVar to expire at the Page View in the Admin Console. Expiration beyond that will probably add little value and only slow down reporting. There are some special things you need to do here if you use Custom Links so I would advice you speak to Omniture Consulting about this.
3. Consider Classifying the Page Name eVar by Page Type, Page Product Category, etc… to increase the value you get from this eVar.

In my last post, I discussed a topic which I called Segment Pathing, which allows you to see how Pathing on your site differs by Visitor Type or Campaign Tracking Code. In this post I will build upon this concept with one of the most popular topics in the Web Analytics field: Bounce Rates. While I am not as enthusiastic about Bounce Rates as many others in the field, I do understand their importance and why people like them them. However, one of my gripes with the Bounce Rate metric (which I have always defined as Single Access/Entries) is that there is not an easy way in SiteCatalyst to see Bounce Rates for different types of visitors or Campaigns. Unless they have Omniture Discover or are experts at ASI Segments, most of the Omniture clients I worked with were primarily looking at Bounce Rates for the entire population. While this is OK, I think we can do better than that. In this post I will show you how I create Segment Bounce Rates. However, to get the most out of this post, I strongly encourage you to read my prior post on Bounce Rates and my previous post on Segment Pathing before reading this post.

Segment Bounce Rates
As I just described, my goal when looking at Bounce Rates is to be able to tell my peers how visitors are bouncing off key pages based upon both the page and the segment. In my previous post, I highlighted two segments that I commonly use: 1) Visitor Type (i.e. Customer vs. Non-Customer) and 2) Campaign Tracking Code (i.e. visitors from Google keyword A vs. Yahoo keyword B). If I can dissect how each segment bounces off pages, I can determine if I need to create different versions of pages for each Visitor Type or Campaign Code or I can use this information to build future A/B Tests using a tool like Test&Target. As I mentioned in my last post, this is a moot point if your organization already has Omniture Discover, but as is always the case in my blogs, my goal is to show you how to do things if you only have access to SiteCatalyst.

Implementing Segment Bounce Rates
The good news is that if you have already followed my instructions from my previous post on Segment Pathing, you are 95% of the way to being done with implementing Segment Bounce Rates! As a quick recap, in my last post I described a process in which you concatenate the Page Name with another Traffic Variable (sProp) that contains a segmentation that you care about (i.e. Visitor Type). Once you have these values concatenated on every page, you enable Pathing so you can see paths or pages by segment. However, when you enable Pathing on this new sProp, you immediately gain access to the two metrics that you need to calculate Bounce Rate: Single Access & Entries. Therefore, without even knowing it, by implementing Segment Pathing, you have also implemented Segment Bounce Rates! All you need to do is to create the Bounce Rate Calculated Metric (which hopefully you already have as a Global Calculated Metric) and you are done.

So how do you see the results of your work? All you need to do is to open the new concatenated sProp and add the Bounce Rate metric to the report. In the example shown below, I will use the Campaign Pathing sProp which shows Campaign Tracking Codes concatenated with Page Names. I will add Visits, Single Access, Entries and Bounce Rate to the report:

As you can see, the Bounce Rate for each Tracking Code/Page Name combination is displayed and you can sort by any metric you wish.

As a best practice, I like to conduct a text search filter to isolate one Page Name so I can see how the Bounce Rates differ for the same page with different Campaign Tracking Codes. In the following example, I filtered on the phrase “:Home Page” and limited my results to see only Home Page Entries and the associated Bounce rates of each Campaign Tracking Code:

Keep in mind that I am only showing a few simple examples here and that this functionality can be extended to any segment of your choosing. If you want to get really advanced, you could even concatenate multiple items together, such as Visitor Type + Campaign Tracking Code + Page Name. This would allow you to see how different Visitor Types, coming from specific Campaign Tracking Codes, landing on specific Pages, navigate your site or Bounce off pages (i.e. Customer:ggl_1:Home Page). Just don’t go too crazy since there are character limits on sProps and you don’t want to exceed the 500,000 monthly unique limits on sProps.

Final Thoughts
As you can see, you get a “two for the price of one” deal if you do all of the steps in this post and the previous post. If you don’t have access to Omniture Discover and want to see how people navigate through your site or bounce off your site pages by specific segment, I suggest you give this a try and see if it helps you.