Archive for 'SEO/SEM'

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.

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!

Recently, I was re-reading one of Avinash Kaushik’s older blog posts on tracking Internal Search Term Exit Rates and realized that I had never discussed how to report on this using Omniture SiteCatalyst.  In a past Internal Search post, I covered many different things you can do to track internal search on your site, but did not cover ways to see which terms are doing well and which are not.  In this post I will share how you can see this so you can determine which search terms need help…

Why Track Internal Search Term Click-Through & Exit Rates?
So why should you do this?  In the era of Google, we are all slowly being trained to find things through search.  Many of my past clients saw the percent of website visitors using search rise over the past few years.  In addition, Internal Search and Voice of Customer tools are some of the few out there where you can see the intent of your visitors.  Unfortunately, most websites have horrible Internal Search results which can lead to site exits.  In my previous Internal Search post I demonstrated how to track your Search Results Page Exit Rate, but that only shows you if you have a problem or not.  If you do have a high Search Results Page Exit Rate, the next logical step is to determine which search terms your users think have relevant search results and which do not.  Note that this is not meant to show you which terms lead directly to website exits, but rather, which terms cause visitors to use or not use the search results you offer them after they search on a particular term.

How Do You Track Internal Search Term Click-Through & Exit Rates?
Ok, so how do you do this?  Follow the following implementation steps:

• Make sure that you are setting a Success Event when visitors conduct Internal Searches on your website.  Hopefully you are already doing this so in many cases this step will be done!
• Make sure that you are capturing the Internal Search Term the visitor searched upon in an eVar variable.  Again, you should be doing this (if not, shame on you!).
• Here is where we get into uncharted territory.  The next step is to set a new Success Event when visitors click on one of the items on the search results page.  Depending upon the technology you use for Internal Search, this could be hard or easy.  Regardless of how you actually code it, the key here is to set the second Success Event (I call it Internal Search Results Clicks) only if visitors click on a search result item (not if they click on a second page of search results or go to another page through other navigation).  It is also critically important that you only set this Search Results Clicks Success Event once per search term!  Do not set it every time a visitor clicks on one of the search results after using the “Back” button.  If you don’t do this correctly, your Click-Through and Exit Rates will be off.  This could take a few iterations to get right, but stick with it!
• Once you have both the Internal Searches and Internal Search Results Clicks Success Events set, you can create a Calculated Metric that divides Internal Search Results Clicks by Internal Searches to see the Internal Search Click-Through Rate as shown here:

• From there you can create the converse metric which subtracts the Internal Search Click Through Rate by “1″ to come up with the Internal Search Exit Rate as shown here:

• After this is done, you can open the Internal Search Term eVar report and add all three metrics so you see a report like this:

In this case, it looks like the “Zune” Internal Search term might need some different search result content as it has a much higher exit rate as the others.  Another cool thing you can do is to create a report which trends the Internal Search CTR % or Exit % for specific Internal Search terms so you can see if they have been good/bad over time.  Also, if you use SAINT Classifications to group your Internal Search Terms into buckets, you can see the report above for groups of Internal Search terms.  If you vote for my idea in the Idea Exchange, you would be able to set SiteCatalyst Alerts to be notified if your top Internal Search Terms have spikes in their Click-Through or Exit Rates.  You can also segment your data to see how the Internal Search rates differ when people come from Paid Search vs. SEO, etc… and even use Test&Target to try out different promotional banners on your search results page…

Finally, don’t forget that when you create a new calculated metric like the Internal Search CTR % metric described above, you also get the bonus of seeing this metric across your entire website under the My Calc Metrics area of the SiteCatalyst toolbar.  Simply find this new metric and click on it and you can see your overall Internal Search Click-Through Rate regardless of internal search term.  Your report will look something like this:

For The True Web Analyst Geeks
If you were bothered when I mentioned above that you should only set the Search Results Clicks Success Event once per search term, then you are my kind of person (please apply for a job with me!)!  You were probably saying to yourself: “If I only count once per search term, how will I know which search terms get visitors to click on multiple search result links?”  Right you are!  That could be valuable information.  If you want to see that as well, all you have to do is set a second Success Event each time a visitor clicks on a search result [I call this Internal Search Result Clicks (All)].  Then you can compare how many times people click on any search result to how often click in total.  Here is a sample report:

In this example, you can see that the search term “api” had one click only in either scenario, but the search term “chatter” had people click on it 100% of the time and 5 times they clicked on two search result items.  If you want, you can create another Calculated Metric that divides the Internal Search Result Clicks (All) by the # of Internal Searches to see how many search result clicks each term averages.  In the case of “chatter” above, it would be 2.25 search result clicks per search!

Final Thoughts
If Internal Search is important to your site, make sure you are tracking it adequately so you can improve it and increase your overall website conversion.  Do you have any other cool Internal Search tracking tips I haven’t covered?  If so, leave a comment here…

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…

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.

I know many Omniture clients that spend much of their time using SiteCatalyst for SEO and SEM tracking.  If you are one of these clients, the following will show you a fun little trick that you can use to improve your Search reporting by setting custom Search Success Events.

That Darn Instances Metric!
As a Search marketer, you tend to spend a lot of your time in the various Paid and Natural Search Engine reports within SiteCatalyst.  While in those reports, you would normally use the out-of-the-box “Searches” metric for most of your reporting.  If you stay in the Search reports, life is good, as you can use the Searches metric and any other Success Event to see what success takes place after visitors arrive from a particular Search Engine or Search Keyword.  For example, here is a report that shows Searches and Form Completions coming from various Search Engines:

However, as I blogged about a while back in my Instances post, the Searches metric is really just a renaming of the dreaded SiteCatalyst “Instances” metric.  Why is that bad?  It means that if you need to see Searches in any other Conversion Variable (eVars) report, you are out of luck.  For example, let’s say that your boss wants to see a report that shows Searches and Form Completes (and possibly a Calculated Metric that divides the two) by Site Locale (each country in which you do business).  To do this, you would open the Site Locale eVar report and add Form Completes, but guess what…there is no “Searches” metric to add to the report since it only exists in the Search Engine reports!  Rats!

Let’s say you are an eternal optimist and you say, darn it, I can solve this!  After pouring over past blogs, you finally arrive at the perfect answer!  I can use Conversion Subrelations to break the Search Engine report down by Site Locale while the Searches metric is in the report!  So you go back to the Searches report shown above and realize that all you have to do is use the green magnifying glass icon to and break the report down by the Site Locale eVar (which BTW will only work if Site Locale has Full Subrelations enabled).  I’m a genius, you think to yourself!  Then you wait for the report to load…brimming with anticipation only to see this…

Yuck!  What’s up with all of the “n/a” values?  Foiled again by the darn Instances metric!

Don’t Panic!
Don’t be so hard on yourself since if you got that far, you are ok in my book!  Just consider this a well earned lesson on why you have to be careful around any Instances metric (don’t fall for the same thing with Product Views!).  As always, I don’t like to just present problems since the Omni Man is all about solutions!  To solve this enigma, we have to find a way to get around the Instances metric.  At a high level, the solution is to set custom Success Events when visitors arrive at your site from a Search Engine.  I usually set a Natural Search, Paid Search and Paid + Natural Search metrics.  This can be done in several ways, but the easiest way is through the Unified Sources Vista Rule or the JavaScript equivalent known as the Channel Manager Plug-in.  Regardless of how you implement it, once you have true custom success events set when visitors arrive from a search engine, you can use these success event anywhere within Omniture SiteCatalyst which means that you can now create the report you were looking for above like shown here:

The following are some other advantages of using a custom success events for Searches:

1. You can use these metrics in Calculated Metrics (i.e. Shopping Cart Additions/External Natural Search) without having to rely upon the ExcelClient
2. You can create Alerts on Paid or Natural Search metrics
3. You can add some cool SiteCatalyst Plug-ins or advanced features to the new Custom Search success events that make them even better than the out-of-the-box Searches metric (i.e. Avoid back button duplicate counting by using the getValOnce plug-in or Event Serialization).
4. You have an easy way to create a metric report for Searches (see below) and add it to a SiteCatalyst Dashboard

The only caveat I will give you is that the new custom Search metrics will probably never tie exactly with the out-of-the-box metrics, but in many cases you can make them more accurate and useful.  If SEO/SEM is something that is important to your organization, I suggest you talk to Omniture Consulting and give it a whirl…  Let me know if you come up with any other cool uses for this functionality…