Missing SAINT Classification Data [SiteCatalyst]

Recently, the Adobe SiteCatalyst product team “hit it out of the park” (to follow Ben’s analogy) with the latest SiteCatalyst point release!  There are many awesome features that people like me have been waiting for. This release has things like segment comparisons, increased self-service capabilities via the Admin Console, Classifications on List Variables, Hourly Trending and improved test search filtering.  Probably the biggest point release I have seen going back to version 9!  Kudos to all involved!

However, the biggest feature enhancement was the SAINT Classification Rule Builder. This has been a long time coming and I am excited to start using it. I highly recommend you read more about this in the SiteCatalyst help section (login required). This new feature will go a long way towards helping clients maintain and clean up their SAINT Classifications. While I was giddy about the concept of SiteCatalyst customers having updated SAINT Classifications, I decided to share some other tips I have used to help clients minimize their missing SAINT data. When I work with clients to audit their Adobe SiteCatalyst implementations, one thing I review is how many of their eVars and sProps are missing SAINT classification data. Hopefully, these tips, combined with the new SAINT Classification Rule Builder, will lead you into SAINT Classification bliss!

The “None” Row in SAINT

In the past, I have explained how the “None” row in SiteCatalyst is annoying (at times), but actually a good thing, and not something to be feared. The “None” row can be extremely useful in Campaign reports and many others. If you see a “None” row in any eVar report, it simply means that when the chosen Success Events took place, there was no value for the current eVar. After a while, most SiteCatalyst users begin to understand this. Traffic variable (sProp) reports don’t have “None” rows since if there is no data, it just doesn’t show it instead of lumping the reminder into a “None” row.

However, when it comes to SAINT Classifications, for the most part, the “None” row tends to be a bad thing. The reason is that when you see a “None” row, it can mean one of two things:

  1. The root eVar variable that you are classifying did not have a value
  2. You are missing SAINT Classification data, causing unclassified data to appear in the “None” row for the eVar (or sProp) classification

To better illustrate this, let’s look at an example. Let’s say you work for a company that sells video games. You are passing Product ID’s to the Products variable and also have a few SAINT classifications of the Products variable including the one shown here (Game Genre):

As you can see, there is a significant percentage of Orders and Revenue appearing in the “None” row of this classification report. But how do you know if the cause is #1 or #2 above or a mixture of both? Did someone launch new products and forget to pass in a Product ID to the products variable and is that why there is no assigned Game Genre? Or do we have all of the Product ID’s correctly assigned to the Products variable, but forgot to add the Game Genre meta-data via SAINT? Unfortunately, it is difficult to know the answer to this question without doing some research.

Isolating the True “None” Row

If you are a SiteCatalyst guru, you probably know that the fastest way to figure this out is to do what I call the “breakdown by the root” trick. What I do is to click the breakdown icon next to the “None” row and choose to break that row down by the variable that it is a classification of (its root). In this case, you would break down the Game Genre “None” row by the Products variable to see if there are any product ID’s that show up. If you see Product ID’s in the breakdown report, you know that you are missing SAINT classification data. If you only see a “None” value, then you have done all that you can do via SAINT and have to figure out why such a high percentage of Orders and Revenue are not being associated with a Product ID. The latter is often a tagging issue.

In this example, when you create this breakdown, you can see that both problems exist. About 4% of the Orders taking place are missing a Product ID in the Products variable, which means that we have no way of knowing which Game Genre they would fall into. However, the rest of the items appearing in the breakdown report have Product ID’s. This means that they are simply unclassified. Therefore, if we were to successfully classify all of these Product ID’s, we could bring our overall percent of unclassified Orders down from 22.1% to 0.8% (1,095/128,916 Orders), which makes a huge difference! I have found that having large “None” rows for classifications can confuse your users and lead to the perception that your data isn’t sound. To stay on top of this, another trick I suggest is that you schedule the preceding breakdown report to be mailed to you weekly for your most important variable classifications.

Using a “Dummy Value”

Next is what I call the “dummy value” trick. There are sometimes cases in which you know that you will be missing meta-data. For example, in the gaming scenario above, there could be a case in which you know the Product ID, but for some reason don’t yet have the Game Genre right away. Looking at the second report above, there may be a legitimate reason why Product ID 7777 and 7767 don’t yet have a Genre assigned. If that is the case, my suggestion is that you set a “dummy value” in your SAINT file to act as a placeholder for the actual value that will be coming later. To do this, simply add the “dummy value” in any blank spots of your SAINT file. For example, let’s say that you download your products SAINT file and it looks like this:

All you have to do is fill in the blanks with a “dummy value.” I like to put “dummy” values in all caps and/or brackets so they are easy to identify and filter out of reports if needed. The preceding SAINT file would now look like this:

Once this file has been uploaded and processed, you can re-open the first report shown above and see this:

Obviously, not much has changed since all we did was move most of the “None” values to a new “dummy” row. However, we now can see that the actual “None” row is only about 0.8% and more importantly, this report communicates to SiteCatalyst users that it is known that 21.3% of the Game Genre’s are currently missing (so don’t call and pester us!). You can put any message you want in the brackets such as “[GAME GENRE COMING SOON...]” or whatever you think makes sense to your users. Additionally, it is easy to see this report without the “dummy value” by simply using a search filter to remove anything with a “[" or "]” symbol, which is easier than removing the “None” row from reports.

Final Thoughts

If you have to deal with SAINT classifications on a regular basis, knowing how to do the following can make your life a lot easier:

  • Isolate the true “None” values from those missing SAINT classifications
  • Get a report of those SAINT items that are missing meta-data through scheduled reports
  • Communicate which SAINT values are known to be missing vs. ones that are true “None” values through a “dummy value”

Together these tips should save you some time and headaches when it comes to SAINT. If you have any questions on these tips or additional ones, feel free to leave a comment here.

P.S. If you would like to take my advanced SiteCatalyst class or take classes related to Adobe ReportBuilder, Adobe Discover and many other web analytics topics, check out Web Analytics Demystified’s upcoming Midwest training classes: http://www.webanalyticsdemystified.com/accelerate/training-2013.asp

Published on June 3, 2013 under Conversion Variables, Feature Request, Products Variable, SAINT Classifications

Big vs. Little Implementations [SiteCatalyst]

Over the years, I have worked on Adobe SiteCatalyst implementations for the largest of companies and the smallest of companies. In that time, I have learned that you have to have a different mindset when it comes to each type of implementation. Implementing both the same way can lead to issues. Big implementations (which can be either large due to complexity or traffic volume) are not inherently better or worse, just different. For example, an implementation at a company like Expedia is going to be very different than an implementation at a small retail website. Personally, I find things that excite me about both types. When working with a large website, the volume of traffic can be amazing and your opportunities to improve conversion are enormous. One cool insight that improves conversion by a small percentage, can mean millions of dollars! Conversely, when working with a smaller website, you usually have a smaller development team, which means that you can be very agile and implement things almost immediately.

Hence, there are pros and cons with each type of website and these are important things to consider when approaching an implementation or possibly when considering what type of company you want to work for as a web analyst. The following will outline some of the distinctions I have found over the years in case you find them to be helpful.

Implementation Differences

The following are some of the SiteCatalyst areas that I have found to be most impacted by the size of the implementation:

Multi-suite Tagging
Most large websites have multiple locations, sites or brands and use multi-suite tagging. When you bring together data from multiple websites into one “global” suite, you have to be sure that all of the variables line up amongst the different child report suites. Failure to do this will result in data collisions that will taint Success Event metrics or combine disparate eVar/sProp values. If you have 10+ report suites, it almost becomes a full-time job to manage these, making sure that renegade developers don’t start populating variables without your knowledge. If you use multi-suite tagging and have a global report suite, my suggestion is to keep every report suite as standardized as possible. This may sound draconian, but it works.

For example, let’s say you have five report suites that are using eVars 1-45 and a few other report suites that require some new eVars. Even if the latter report suites don’t intend to use eVars 1-45 (which I doubt), I would still recommend that you use eVars 46 on for the new eVars for the additional report suites. This will ensure that you don’t encounter data conflicts. Taking this a step further, I would label eVars 1-45 as they are in the initial report suites using the Administration Console. I would also label eVars 46 on with the new variable names in the original set of report suites. At the end of the day, when you highlight all report suites in the Admin Console and choose to see your eVars, you should strive to see no “Multiple” values. That means you have a clean implementation and no variable conflicts. Otherwise, you will encounter what I call “Multiple Madness” (shown here).

If you really have a need for each website to track its own site-specific data points, one best practice is to save the last few Success Events, eVars and sProps for site-specific variables. For example, you may reserve Success Events 95-100 and eVars 70-75 to be different in each report suite. That will provide some flexibility to site owners. You just have to recognize that those Success Events and eVars should be hidden (or disabled) in the global report suite so there is no confusion. Another exception to the rule might be sites that are dramatically different than the core websites. For example, you may have a mobile app or intranet site that you are tracking with SiteCatalyst. This mobile app or intranet site may be so drastically different from your other sites that you want to have it in its own separate report suite that will never merge with your other report suites. In this case, you can either create a separate Company Login or just keep that one report suite separate from the others and use any variables you want for it. Keep in mind that the Administration Console allows you to create “groups” of report suites so you can group common ones together and use that group to make sure you don’t have any “multiple” issues. You can also use the Menu Customization feature to hide variables in report suites where they are not applicable. Even if you don’t currently have a global report suite, I still recommend following the preceding approach. You never know when you might later decide to bring multiple report suites together, and using my approach makes doing so a breeze (simply changing the s_account variable) versus having to re-implement variables and move them to open slots at a later date. The latter will cause you to lose historical trends, modify reports and dashboards and confuse your end-users.

When you have a smaller implementation, it is common to have just one production report suite. This avoids the preceding multi-suite tagging issues and makes your life a lot easier!

Variable Conservation
As if coordinating variables across multiple report suites isn’t hard enough, this issue is compounded by the fact that multi-suite tagging means that you only have ~110 success events, ~78 eVars and ~78 sProps to use for all sites together vs. being able to use ~250 variables differently for each website. This means that most large implementations inevitably run out of variables (eVars are usually the first type of variable to run out). Therefore, large implementations have to be very aggressive on conserving variables, which can handcuff them at times. As a web analyst, you can often make a case for tracking almost anything, since the more data you have the more analyses you can produce and the more items you can add to your segments. Unfortunately, when dealing with a large implementation, for the reasons cited above, you may need to prioritize which data elements are the most important to track lest you run out of variables. This isn’t necessarily a bad thing as it helps your organization focus on what is really important across the entire business and tracking more isn’t always better.

If you contrast this with a smaller implementation that has no multi-suite tagging and no global report suite, the smaller implementation is free to use all variables for the one site being tracked.  This provides ~250 variables to use as you desire. That should be plenty for any smaller site, so variable conservation isn’t as high of a priority. A few times, in my SiteCatalyst training classes, I have had both large and small companies sitting next to each other, and have witnessed the big company drooling over the fact that the smaller company was only using 20 of their eVars (wishing they could borrow some)! While it may sound strange, there are many cases in which I would tell a smaller organization to set success events and eVars that I would conversely tell a large organization not to set. For example, if I were working with a small organization that had only one workflow process (i.e. credit card application) and they wanted to track all six steps with success events, I might say “go for it!” But if that same scenario arose for a large website (i.e. American Express), I would encourage them to only set success events for the key milestone workflow steps to conserve success events. This is just one example of why I tend to approach large and small implementations differently.

One final note related to variable conservation. Keep in mind that you can use concatenation combined with SAINT Classifications to conserve variables. For example, instead of storing Time of Day, Day of Week and Weekday/Weekend in three separate eVars, you can concatenate those together into one and apply SAINT Classifications. This will save a few eVars and a similar process can be replicated for things like e-mail attributes, product attributes, etc.


Uniques Issues
If you have a large website, there is an increased chance you will have issues with “uniques.” Most eVar and sProp reports have a limit of 500,000 unique values per month. I have many large clients that try to track onsite search phrases or external search keywords and exceed the unique threshold by the 10th day of the month. This makes some key reports less useful and often results in data being exported via a data feed or DataWarehouse report to back-end tools for more robust analysis. For some large implementations, since the data points can’t be used regularly in the SiteCatalyst user interface due to unique limits, I sometimes have clients pass data to an sProp to conserve eVars, since in DataWarehouse, Discover and Segmentation, having values in an sProp is similar to having it in an eVar.

Smaller implementations normally only hit uniques issues if they are storing session ID’s (i.e. ClickTale, Tealeaf) or customer ID’s.

Large # of Page & Product Names
Many large websites have so many pages on their site (i.e. one page per product and over 100,000 products) that having an individual page name for each page is virtually impossible. In these cases, you often have to take page names up a level and start at a page category level. The same concept can apply to individual product names or ID’s as well.

Smaller implementations rarely have these issues since they tend to have fewer pages and numbers of products.

Page Naming Conventions
Another area where I see those running large implementations make mistakes is related to page naming across multiple websites. If you are managing a smaller implementation, you can name your pages anything you’d like. For example, while I don’t recommend it, if you want to call your website home page, “Home Page,” you will be ok. However, this approach won’t always work with a large implementation. If you have five report suites and one global report suite and you named the home page of each “Home Page,” in the global report suite, you would see data from all five report suites merged into one page name called “Home Page.” While there may be reasons to do this, you will probably also want to have a way to see things like Pathing and Participation for each of the home pages from each site individually in the global report suite. In this post, I show how you can have both (“have your cake and eat it too!”), but this example highlights the complexity that can arise when dealing with larger implementations.

SAINT Classifications
Large websites can often have a variable with more than a million SAINT classification values. Updating SAINT tables can take days or weeks unless you are methodical about your approach. Smaller sites with lower numbers of SAINT values can often re-upload their entire SAINT file daily or weekly to make sure all values are classified. Large implementations don’t have this luxury. They have to monitor which values are new or missing SAINT values so they can only upload the new or changed items so it doesn’t take weeks for SAINT tables to be updated. If you work with a large implementation, keep in mind that you can update SAINT Classifications for multiple report suites with one upload if you use the FTP method vs. browser uploads.

Time to Implement
In general, large implementations tend to move slower than smaller ones. While tag management systems are helping to remedy this, I still find that adding new variables or fixing broken variables takes much longer with large implementations (often due to corporate politics!). This means that you have to be sure that your tagging specifications are right the first time, since getting changes in after a release may be difficult.

Conversely, with smaller websites, you can be much more nimble and update SiteCatalyst tagging on the fly. For example, you may doing a specific analysis and realize that it would be helpful for you to have the Zip Code associated with a form. If you work with a smaller site, you may be able to use a SiteCatalyst Processing Rule or call your developer and have them add Zip Code to eVar30 and have data the same day!

Globally Shared Metrics, Dashboards, Reports, etc.
When you work with a small implementation, you may have a few calculated metrics, dashboards or reports that you share out to your users. This is a great way to collaborate and enforce some standards or consistency related to your implementation. However, when you have a large implementation, sometimes with 300+ SiteCatalyst users having logins, this type of sharing can easily get out of control. Imagine each SiteCatalyst user sharing five reports or dashboards. The shared area of the interface becomes a mess and you are not sure which reports/dashboards you should be using. Therefore, when you are working with a large implementation, it is common to have to implement some processes in which reports and dashboards are sent to the core web analytics team who can then share them out to others. This allows the SiteCatalyst user community to know which reports/dashboards are “approved” by the organization. You can learn more about centralizing reports and dashboards by reading this blog post.

Final Thoughts

As I mentioned in the beginning of this post, bigger isn’t always better. As shown from the items above, I often find that bigger implementations lead to more headaches and more limitations. However, keep in mind that with great volume, comes conversion improvement opportunities that often dwarf smaller sites.

One over-arching piece of advice I would give you, regardless of whether you work with a large or small implementation, is to review your implementation every six months (or at least yearly) and determine if you are still using all of your variables. It is better to get rid of what you no longer need periodically than to have to do a massive overhaul one day in the future.

While this post covers just a few of the differences between large and small implementations, they are the ones that I tend to see people mess up the most. If you have other tips for readers, feel free to leave a comment here. Thanks!

Published on May 6, 2013 under Administration Console, General Implementation, Multi-Suite Tagging, Participation, Pathing, Training

Revenue Bands [SiteCatalyst]

When it comes to tracking online purchases in SiteCatalyst, there are many different ways to report on Orders, Units and Revenue. There are the standard shopping cart metrics and an easy way to create calculated metrics using those cart metrics, such as Average Order Value (AOV). However, a question I get from time to time is related to looking at website data by how much money visitors spend in an Order. In this post, I will share some thoughts on how to add Revenue Bands to your SiteCatalyst implementation.

Revenue Bands

So what do I mean by Revenue Bands? I think of Revenue Bands as groupings of revenue amounts by which you can view any of your SiteCatalyst Success Events. For example, let’s say that your boss comes to you and wants to know what percent of Orders taking place last week were between $200 and $300. That seems like an easy question for SiteCatalyst to answer right? But how would you actually answer it? In the past, I have shown how you could use a Counter eVar to store and accrue Revenue to Date, but that answers a related, but different question than the one at hand.

One way to answer this question would be to use Segmentation. You could create a segment in which Orders were greater than $300 and less than $400 and then apply this to any SiteCatalyst report. However, you may get future questions asking for different amounts, such as Orders greater than $400 or greater than $500, etc. This would necessitate creating multiple different segments, which might be annoying after a while.

Another approach would be to classify your Order ID eVar report. As a best practice, you should be storing each unique Order ID an a custom eVar as described in this blog post. Once you are doing this, you could classify all Orders into buckets so items in each of the rows shown here would be grouped into the correct Revenue Band using SAINT Classifications:

However, this would be a pain to keep updated so I would steer away from this option.

So what would be the easiest way to see SiteCatalyst data by Revenue Bands? My advice is to simply identify the Revenue Bands that you care about, and use some tagging (or a processing rule) to pass these Revenue Bands to an eVar on the order confirmation page. For example, let’s say you want one Revenue Band for “Under $50,” another for $51-$100 and then after that for each one hundred dollar range. You can work with your developers to map this out and then set the appropriate value to an eVar on the order confirmation page. Regardless of how it is set, the end result is an eVar with various Revenue Bands such that you have a report like this:

Obviously, you can also capture the raw revenue amounts in an eVar and use SAINT Classifications to group into Revenue Bands. This would provide more flexibility, but also adds a bit more work. If you are set with your Revenue Bands, I would use the preceding approach, otherwise just pass in the raw Revenue Amounts. However, if passing in raw Revenue amounts, I highly suggest you remove the “cents” portion of the revenue amount so your SAINT Classifications are much easier!

Regardless of which approach you choose, by simply adding the Orders metric to the resulting report, you can see Order percentages for each Revenue Band. Since this is an eVar, we can also break this report down by any other eVar such as Visit number, Product or Marketing Channel. Conversely, we might want to take a report like Marketing Channel and break it down by this new Revenue Band eVar to see a report like this:

This new eVar can also be used for segmentation purposes and actually makes the building of segments a bit easier (in my opinion).

So there you have it. A simple way to add Revenue Bands to your SiteCatalyst reporting…Enjoy!

Published on March 25, 2013 under Conversion Variables, Counter eVar, SAINT Classifications, Segmentation

Conducting Cohort Analysis with Adobe SiteCatalyst

One type of web analysis that you hear about from time to time is cohort analysis. In general, the concept of a cohort analysis is that you look at a population of visitors and see how that population performs over time. Unfortunately, there is no out-of-the-box way to perform cohort analysis in Adobe SiteCatalyst, but in this post, I will share the steps you need to take to perform this analysis using SiteCatalyst (and which will also work in Discover).

Establishing Your Cohorts

The first step in performing this analysis is to identify the business question that you’d like to solve. There are an unlimited number of cohort analyses that can be done, so I will use a simple retail example here. Let’s imagine that your boss wants to know how much money those who place their first order in month #1, spend on the website in month#2, #3, #4, etc. For example, how much did those who originally purchased in January, spend in February, March, etc. and how does that compare to those who originally purchased in February and then spent additional money in March, April, etc.? In this example, we will start with January 2013 as month #1. To do this, we first need to identify visitors who made their first purchase in the first month, in this case, January 2013.

If you have been reading my posts about setting a Date eVar or Segmenting on Key Dates, you may recognize that we are going to want to set a date to an eVar as part of this solution. However, in this case, what we need to do is to set the date eVar only at the point that the visitor makes their first purchase. An easy way to do this is to create a new eVar and call it “Original Purchase Date” and in the Admin Console, set it to be Original Value (First) allocation and expire it “Never” as shown here:

Once this “Original Purchase Date” eVar has been created, you would set it on the order confirmation page every time, since the allocation will tell SiteCatalyst to ignore it if a value already exists (obviously, cookie deletion will cause it to be reset, but there isn’t much we can do about that). Keep in mind that if you have an existing implementation, the “first purchase” may not be the real first purchase since visitors may have purchased before this eVar was around, but over time it should work for new visitors. If this bothers you, I suggest asking your developers to write some code to only set the eVar if it truly is a first time purchase.

Now that you have an Original Purchase Date eVar, we need another eVar to compare it to in our cohort analysis. This second eVar would be the date that each purchase takes place. This eVar is easy, as it is simply set to the current date on each visit as described in my Date eVar post and will bind to the Revenue metric when the purchase success event is set. For the first purchase, the “Original Purchase Date” eVar will be the same as the Date eVar, but as visitors make subsequent purchases, the “Original Purchase Date” eVar will stay constant while the Date eVar will contain dates in the future.

Setting Up The Analysis

After you have the two date eVars populating, the next thing you should do is to group dates into larger buckets, either weeks or months. While you can use specific days, I find that doing analysis at a weekly or monthly level is a bit cleaner and may be a bit easier to explain here. To do this, I will apply SAINT Classifications to both of my Date eVars. This is very straightforward, as you simply upload the week or month associated with each date as shown here:

Next, let’s assume that a few months have passed and that it is now early April of 2013. We will open the Original Purchase Month eVar report (classification of Original Purchase Date) and add Revenue as the success event. Once this report opens, we will make sure our date range covers January 2013 through April 2013 and then should see a report that looks like this:

Unfortunately, the preceding report isn’t terribly exciting (yet!) since it just shows revenue by the month of the original purchase. However, when we apply a subrelation to break down this report by the Month Classification of the Date eVar, something magical happens:

As you can see here, revenue for cases in which the original purchase took place in January 2013 and February 2013 is now broken out by each month in which purchases actually took place. This allows us to see our first cohort analysis and see how much $$$ those who purchased in each month spent in subsequent months. If desired, you could also add other elements to a segment to do more in-depth analysis. For example, you could add Marketing Channel = SEO to a container in the segment builder to see how this analysis changes for those sourced from SEO. You could add visit number to the segment container to see how this analysis changes for visitors whose original purchase was also their first website visit. The possibilities are truly endless.

As you can see, setting up cohort analysis in SiteCatalyst is a bit of work, but not impossible. One downside is that you need to use one eVar for every type of cohort you want to do. In the preceding example, I created an “Original Purchase” eVar, but if I wanted to see a cohort analysis around Orders, I would need to have an “Original Order” eVar. If you are running low on eVars or want to do many types of cohorts, this can be an issue. Maybe one day SiteCatalyst will allow you to see an “original” version of each eVar out-of-the-box.

Creating Your Cohort Analysis in Excel

Unfortunately, the report above doesn’t present the data in the ideal “cohort” framework. To do this, you are going to have to do a bit more work, and I recommend using Microsoft Excel. We will have to extract the preceding data to create a table that shows the current month in the “Month 1″ column and then subsequent months in the next few columns so it looks like this:

For example, if we assume that the preceding report was run in April, we can see how much is spent in each month and the subsequent months. Another way to visualize this is to calculate cohort percentages by dividing these revenue amounts by total monthly revenue to create report like this:

In this example, it looks like we may be doing a bit better in February than we did in January since a higher percentage purchased in the following two months.

But how do we extract this data in an easy, scalable way? To find out, stay tuned for my partner Kevin Willeitner’s post on how to build Cohort Analyses using ReportBuilder which will show how you can use ReportBuilder to automatically  create the above tables…

My friend Jan Exner wrote a related blog post about performing cohort analysis in SiteCatalyst which you can read here (if you speak German!). While I swear I wrote my post before translating his to English, you will see that our approaches begin in a similar way, but diverge thereafter. Another cohort-related post by Tim Elleston can be found by clicking here. As is often the case with SiteCatalyst, there are multiple ways to do similar types of configurations so it is beneficial to review both options. If you just can’t get enough cohort analysis reading to satisfy you, check out Justin Cutroni’s post on how to do cohort analysis in Google Analytics. In the meantime, I hope this helps and if you have any questions/comments, feel free to leave them here. Thanks!

Published on March 7, 2013 under Cohort Analysis, Conversion Variables, ReportBuilder, SAINT Classifications

Segmenting on Key Dates [SiteCatalyst]

Recently, while working with a client, I got into an interesting discussion about doing web analysis around key dates in their marketing program. There are many cases in which milestone marketing events take place on specific dates and clients ask me if there is an easy way in SiteCatalyst to slice and dice data by those key dates. What web analyst hasn’t had a situation where metrics spike up or down on a date or date range and you have no idea why! This is a topic I have dabbled with over the years, but this situation forced me to think about it a bit more deeply. The following will share some ideas I had related to this in case this is a question your organization has as well.

Key Date Reporting

As I thought about this scenario, it dawned on me that there is not a great way to report on key dates in SiteCatalyst. Obviously, you can look at any metric report and see spikes in website activity by date. For example, when I worked at Salesforce.com, around the time of our Dreamforce conference, we would see a tremendous spike in Form Completions around the conference dates that might look like this:

From this report, we can surmise that something happened around these dates. If you work in Marketing at Salesforce.com, I guarantee that you would know that these dates coincide with the Dreamforce conference, but what if the marketing event is something much smaller. A targeted e-mail blast or a social media campaign? What if there were only a modest increase in traffic and metrics on a specific date? I think back to how many times I was called into some executive’s office asking why a particular metric or conversion rate changed on a specific date. I also remember how many times we had to copy a SiteCatalyst chart to a PowerPoint presentation and annotate it with a bubble indicating why there was an increase or decrease. Eventually, SiteCatalyst began adding some ways that you could annotate charts in SiteCatalyst using the Calendar Events feature. This feature allows you to specify a date or date range and add a note to reports in that time period as shown here:

However, adding notes to reports doesn’t allow you to do much in terms of reporting data. Let’s say that you wanted to see the Average Order Value (AOV) for your website during the Black Friday period and compare it to the AOV during other key shopping periods (i.e. Valentine’s Day). Unfortunately, Calendar Events won’t help you very much. It isn’t even easy to compare conversion rates for two date ranges using Segmentation since it is difficult to create a segment on date ranges in SiteCatalyst (unlike Adobe Discover) and even if you could, there is no easy way to compare segments or compare date ranges for Success Events or Calculated Metrics in SiteCatalyst (can only be done for eVars and sProps). Your best bet would be to use Adobe ReportBuilder and pull a data block for the Valentine’s date range and a separate one for the Black Friday date range and compare the two. But what if you want to do this type of comparison natively within SiteCatalyst? Are you out of luck? Have no fear, Omni-Man is here to show you how to do this!

Key Date Segmentation

Back in 2011, I wrote a blog post recommending that each SiteCatalyst implementation have a Date Stamp eVar. The purpose of this eVar was to record the date that Success Events and eVars were set and its primary use was for segmenting on dates. At the time, I was using this eVar to look for actions that took place in the past within SiteCatalyst since only Discover provided the way to segment on dates natively. As I thought about the preceding key dates issue, the idea struck me that my client could leverage this Date eVar to enable additional web analysis for key dates. To do this, you can apply SAINT Classifications to the Date eVar and denote key marketing dates for items normally found in a marketing campaign calendar. Once these items have been uploaded to SAINT, you have an eVar value that can be used to segment data by date ranges of your choosing.

Let’s walk through the creation of this solution. First, you would set the current date to an eVar in each website visit as described in this post. Next, you would use the Administration Console to apply a SAINT Classification to this Date eVar. In this case, we will do just one classification and call it “Key Marketing Dates.” Next we will fill out the SAINT Classification file with some of our key marketing dates. Note that you can leave non-key dates blank or set a dummy value of “No Key Events” on dates having no key marketing events. Here is a sample SAINT file:

Once this SAINT file has been uploaded and propagated to the SiteCatalyst servers, you can open the classified report:

In this report, we now can see a row for each “Key Marketing Date” which is an aggregation of the specific dates associated with that key marketing date label. From here, we can add any metrics we’d like and can compare metrics for those dates. Keep in mind that these rows can contain one or multiple dates depending upon how you have classified the Date Stamp eVar. In addition to the above “ranked” report, you could switch to the trended view to see one metric trended by up to five Key Marketing Date values. It is also possible to break this report down by any other eVar report using Subrelations. For example, you might like to see the above report broken down by Products.

Another powerful use of this concept is the ability to filter Conversion Funnel reports for these key date ranges since it is now treated like any other eVar:

Finally, you can use these Key Date ranges as segmentation criteria since all SAINT Classifications can be used as segmentation criteria:

A Few Gotchas

As is often the case, no solution is perfect. If you have marketing campaigns or key dates that overlap, things get tricky. One way to address key date overlaps is to list both values in the classification value. Alternatively, you could also create more than one SAINT classification and have each SAINT column designated for a specific type of campaign. For example, the first column might be reserved for e-mail campaigns, the next column might be reserved for social media campaigns, etc. That would allow you to have multiple “Key Dates” for the same date stamp value. However, my hunch is that the above solution will work for most companies.

Another potential issues is that you will only see data in the Key Marketing Date report if the date range you have selected includes the dates that were classified using SAINT. Therefore, when running these types of reports, it would be advantageous to use a longer timeframe (i.e. year).

Well, there you have it. What do you think? Have you done something similar? If so, please share your ideas here as a comment…

Published on February 28, 2013 under Administration Console, Calculated Metrics, Conversion Variables, Segmentation, Success Events

Alternative Conversion Flows

Many online marketers have a desire to test out different conversion flows on their website. Whether those flows are for an alternative checkout process or a new application process, the overall desire is the same. By testing out an alternative conversion flow, you can see how website conversion differs and find opportunities to optimize your website and boost conversion. In this post, I will share how you can track these alternative conversion flows in Adobe SiteCatalyst.

Conversion Flow eVar

Luckily, tracking alternative conversion flows is easy in SiteCatalyst. As you probably already know, SiteCatalyst provides Conversion Variables (eVars) that area meant to be set and used to break down various website conversion events (Success Events). Therefore, eVars can be used to store the names of your various conversion flows. For example, let’s imagine that you work for a credit card company and have a standard 4 step application process, but want to test out a streamlined 3 step process. To do this, all you need to do is create a new “Conversion Flow” eVar and pass the appropriate value to it at the start of each process flow. If the current website visitor has been shown the 4 step process, you would pass in a value of “credit-card:4-step” and if the visitor was shown the 3 step process, you would pass a value of “credit-card:3-step” to the eVar. This simple action allows you to segment your website success events into two buckets and see how each conversion flow plays out with respect to conversion:

In this example, we can see that the 3-step process looks to be converting better than our default 4 step process. As always, this new conversion flow eVar can be broken down by other eVars (i.e. Campaigns) and can be used as part of a segment in SiteCatalyst. If you want the results of the test to be limited to one visit, you would set the eVar expiration to “Visit” but if you have cases where you want to retain which flow they were in beyond the visit, set the eVar expiration accordingly (i.e. Month).

Another thing to keep in mind when using this conversion flow eVar is that it can be used over and over again. Once you are done with the preceding conversion flow test, you can re-use the same eVar for other conversion flow tests. When re-using this eVar, you will just want to make sure that preceding tests are completed. I have seen some clients who try to cram too much into a conversion flow eVar and forget that subsequent values will overwrite preceding ones if values are passed to the same eVar.

Concurrent Flows or Tests

So what do you do if you have multiple conversion flow tests taking place simultaneously? For example, let’s say that in addition to the 3 vs. 4 step conversion flow test above, you are also testing landing page A vs. landing page B? This presents a real quandary, since SiteCatalyst does not have a great way to deal with this.

The easiest way to track multiple conversion flows or tests is to use multiple eVars. I suggest that you identify the general types of flows or tests you will have and assign an eVar to each. For example, if your website routinely does landing page tests and conversion flow tests, you might reserve one eVar for each. Each visitor would be assigned a value in both eVars and you can break one down by the other. For example, in the preceding example, if visitors were assigned a landing page value in an additional eVar, the above report might look like this when broken down:

Obviously, this approach has some limitations since, if you do a lot of different types of tests, you will use up many eVars, but this is probably the most straightforward approach.

The other approach, albeit one that I have not yet tried with a client, is using a List Var to store the various test values. As you may recall, SiteCatalyst provides three List Vars that allow you to store multiple values in one eVar. I don’t see why you could not use a comma-separated list of values and put all of the various tests that a visitor is part of in that eVar. However, since I have not yet tried this, there may be some unforeseen downsides to doing this. For example, there may be cases in which you need to remember which flows/tests visitors have been in and persist those values to the List Var to avoid a string of two or three test values being overwritten by a single test value deep within your website. If you are going to try this approach, I suggest you pre-pend each value with the type of test it relates to such as “landing:control” and “app-flow:4-step” so you can differentiate each in the List Var report. However, for now, I suggest that you begin with the multiple eVar approach.


Published on January 7, 2013 under Conversion Variables, Shopping Cart

Products & SKU’s

When I work with retailers who use Adobe SiteCatalyst, one topic that often emerges is the best way to handle the tracking Product ID’s and SKU’s. In this post, I will outline the challenges that exist and share some ways to handle product and SKU tracking in SiteCatalyst.

The Product vs. SKU Dilemma

The primary challenge that arises when it comes to Products and SKU’s is that there are often cases in which you have to set conversion Success Events at the point you know only the Product ID and other cases in which you know the Product ID and the detailed SKU. This is best illustrated by an example. Imagine that you are a retailer and one of the products you sell is a sweater. At the point that a website visitor views the product page for the sweater, you would want to set a Product View Success Event and the Products Variable (s.products). In this case, you most likely have a Product ID for the sweater being looked at by the visitor so you might pass that to the Products Variable such that your tagging looks like this:


So far so good. However, now let’s assume the website visitor chooses a color for the sweater (i.e. blue) and adds it to the shopping cart. In this case, you probably still know the Product ID, but also have a more detailed SKU that represents the sweater with the color being “blue.” Now you have two tagging choices. During the Cart Addition (scAdd) Success Event, should you pass the Product ID # (ProductID-111) or the more detailed SKU as shown below?


The issue with the preceding code is that your Products report will be disjointed since Product Views will be tied to Product ID’s and Cart Additions (and presumably Orders and Revenue) will be tied to the SKU ID. Here is what a sample Products report would look like if the above visitor were the only visitor to the website:

This is clearly not ideal since you’d like to see a full funnel report for each Product or SKU. While we have the option of cleaning up this report by applying SAINT Classifications to roll it up by Product ID, this can be time consuming. Therefore, let’s look at a few ways to improve upon this reporting.

Solution #1 – Product ID Only

If our goal is to produce a clean Products report such that metrics are consistent for each Product ID, one approach is to only pass the higher-level Product ID to the Products variable for all shopping cart Success Events. In the preceding example, this would mean passing a value of “ProductID-111″ with the Product View event and all other shopping cart events. This will allow you to see drop-off between these shopping cart Success Events by Product ID as shown here:

This is the most basic solution, but has one major drawback – it is not possible to see detail below the Product ID. Since you are only setting Product ID’s, there is no way for SiteCatalyst to magically allow you to breakdown the shopping cart Success Events by SKU since you haven’t provided the SKU. This approach works if your products don’t have detailed SKU’s, but if they do, you might find this option limiting and consider moving onto the next approach.

Solution #2 – SKU Merchandising

If your organization subdivides Products into SKU’s at the Cart Addition step, I suggest you use a different approach. In the past I have discussed Product Merchandising, which is a way in SiteCatalyst to associate an eVar value with a specific Products Variable value. Product Merchandising can be used in this situation to bind a SKU to each Product ID using a new SKU Merchandising eVar. In this case we will ask ClientCare to enable a new Merchandising eVar using the “Product Syntax” approach.  Once this is done, we can pass the Product ID to the Products Variable as shown in the above solution, but additionally pass the SKU to a new Merchandising eVar whenever it is present:

During the Product View:

s.events="prodView, event1";

During the Cart Addition:


Keep in mind that if we wanted, we could pass in the actual SKU value (i.e. “Blue” as the color) instead of using the SKU #, but passing the SKU# is ok since we can use SAINT to classify it later.

So far this may not seem to get us much further than we were previously, but as I will show, this set-up does make a big difference. First, you can see a complete funnel by Product ID as shown above by using the Products Variable. But now we have an additional eVar that can be used to see the conversion funnel by SKU. To do this, simply add shopping cart metrics to this new SKU eVar report and you can see everything except Product Views (since those don’t have a SKU):

Since you have two different variables, you can also use Conversion Subrelations to break the Product ID down by the new SKU eVar to see the Product ID metrics broken down by SKU for all shopping cart metrics except Product Views:

Final Thoughts

As always, there are many different approaches to things in SiteCatalyst, but hopefully the preceding gives you some things to consider when dealing with Products and SKU’s. If you have other cool approaches you have used, please leave them as a comment here. Thanks!

Published on December 17, 2012 under Merchandising, Products Variable, SAINT Classifications, Shopping Cart

Adobe SiteCatalyst – ClickTale Integration

About a year ago, I wrote a blog post discussing ways that you could integrate Adobe SiteCatalyst and Tealeaf. In that post, I talked about some of the cool integration points between the two products. In this post, I’d like to talk about how the same integration would work with ClickTale and share some cool new things that are possible that go even beyond what is possible with Tealeaf.

What is ClickTale?

For those unfamiliar with ClickTale, it is an in-page analytics tool that allows you to record website sessions, filter them and play them back. It is often used to see heat maps of pages and to “watch” website visitors and includes even their mouse movements. It is pretty cool technology since often times the best way to get internal stakeholders to understand website issues is to have them watch real users encountering issues.

In a similar manner to what I described in my previous Tealeaf post (which I suggest you read before continuing with this post!), it is possible to pass a ClickTale ID to SiteCatalyst via an sProp or eVar:

Having this ClickTale ID in SiteCatalyst allows you to use the standard segmentation capabilities of SiteCatalyst to isolate visits or visitors who exhibit specific behaviors in which you are interested. For example, you might be interested in isolating visits where visitors reached checkout, but didn’t purchase:

Once you do this, it is possible to open the preceding ClickTale Session ID eVar and see a list of all of the ClickTale session ID’s that match this segment.

Adobe Genesis Extend (BETA) Integration

But as I noted in my preceding Tealeaf post, one of the frustrations of this type of integration is that once you isolate the session ID’s that you want to watch, you are stuck. You have to copy each one individually and then switch to the other application (i.e. Tealeaf) and then start the process of watching the session. My wishlist item in my previous post was that this process could be simplified so you can simply click and view the session, right from within SiteCatalyst. Believe it or not, doing this is now possible! Thanks to the creation of Genesis Extend (still in Beta), you can add a Genesis Chrome browser extension to your version of Chrome and get the ability to streamline this process for ClickTale (not Tealeaf unfortunately).

To do this, simply search for the Genesis Chrome browser extension and install it. When that is done, you will see a new icon in your Chrome browser which you can click to see the settings:

You will notice that there is a ClickTale box you can check (and also one for Twitter which allows you to see actual Tweets in referrer reports). From here you can enter your ClickTale authorization credentials and you are ready to go.


Back in SiteCatalyst, there is a free Genesis “labs” area you can visit to launch the wizard that helps you generate the code you need to capture the ClickTale ID in an eVar of your choice:

After you have completed the wizard and are collecting ClickTale recording ID’s in an eVar, you can open that report in SiteCatalyst, you will see a new link in each row…

…which allows you to click to view the actual recording in ClickTale:

It is also possible to use this new SiteCatalyst eVar to copy a list of ClickTale ID’s and paste them right into ClickTale to create a segment and look at heat maps for just those ID’s.

Final Thoughts

As you can see, this is a cool interface integration that is possible since both SiteCatalyst and ClickTale are “cloud” products. I would expect that you will see more of this in the future in more browsers or even natively as part of SiteCatalyst. If you are a ClickTale customer and use SiteCatalyst, you should definitely try this out!

Published on December 10, 2012 under 3rd Party Integrations, Voice of Customer

SiteCatalyst Tip: Corporate Logins & Labels

As you use Adobe SiteCatalyst, you will begin creating a vast array of bookmarked reports, dashboards, calculated metrics and so on. The good news is that SiteCatalyst makes it easy for you to publicly share these report bookmarks and dashboards amongst your user base. However, the bad news is that SiteCatalyst makes it easy for you to publicly share these report bookmarks and dashboards amongst your user base! What do I mean by this? It is very easy for your list of shared bookmarks, dashboards, targets and other items to get out of control. Eventually, you may not know which reports you can trust and trust is a huge part of success when it comes to web analytics. Therefore, in this post, I will share some tips on how you can increase trust by putting on your corporate hat…

Using a Corporate Login

One of the easiest ways to make sense of shared SiteCatalyst items at your organization is through the use of  what I call a corporate login. I recommend that you create a new SiteCatalyst login that is owned by an administrator and use that login when sharing items that are sanctioned by the company. For example, if I owned SiteCatalyst at Greco, Inc., I might create the following login ID:

Once this new user ID is created, when you have bookmarks, dashboards or targets that are “blessed” by the company, you can create and share them using this ID. For example, here is what users might see when they look at shared bookmarks:

As you can see, in this case, there is a shared bookmark by “Adam Greco” and a shared bookmark by “Greco Inc.” While based upon his supreme prowess with SiteCatalyst, you might assume that Adam Greco’s bookmark is credible, that might not always be the case! Adam may have shared this bookmark a few years ago and it might no longer be valid. But if your administrator shares the second bookmark above while logged in as “Greco Inc.,” it can be used as a way to show users that the “Onsite Search Trend” report is sanctioned at the corporate level.

The same can be done for shared Dashboards:

In this case, Adam and David both have shared dashboards out there, but it is clear that the Key KPI’s dashboard is owned by Greco, Inc. as a whole. You can also apply the same concept to SiteCatalyst Targets:

If you have a large organization, you could even make a case for never letting anyone share bookmarks, dashboards or targets and only having this done via a corporate login. One process I work with clients on, is to have end-users suggest to the web analytics team reports and dashboards that they feel would benefit the entire company. If the corporate web analytics team likes the report/dashboard, they can login with the corporate ID and share it publicly. While this creates a bit of a bottleneck, I have seen that sometimes large organizations using SiteCatalyst require a bit of process to avoid chaos from breaking out!

Using a “CORP” Label

Another related technique that I have used is adjusting the naming of SiteCatalyst elements to communicate that an item is sanctioned by corporate. In the examples above, you may have noticed that I added the phrase “(CORP)” to the name of  a Dashboard and a Target. While this may seem like a minor thing, when you are looking at many dashboards, bookmarks or targets, seeing an indicator of which items are approved by the core web analytics team can be invaluable. This can be redundant if you are using a corporate login as described above, but it doesn’t hurt to over communicate.

This concept becomes even more important when it comes to Calculated Metrics. It is not currently possible to manage calculated metrics and the sharing of them in the same manner as you can for bookmarks, dashboards and targets. The sharing of calculated metrics takes place in the Administration Console so there is no way to see which calculated metrics are sanctioned by the company using my corporate login method described above.

To make matters worse, it is possible for end users to create their own calculated metrics and name them anything they want. This can create some real issues. Look at the following screenshot from the Add Metrics window in SiteCatalyst:

In this case, there are two identical calculated metrics and there is no way to determine which one is the corporate version and which is the version the current logged in user had created. If both formulas are identical then there should be no issues, but what if they are not? This can also be very confusing to your end users. However, the simple act of adding a more descriptive name to the corporate metric (like “CORP” at the end of the name) can create a view like this:

This makes things much more clear and is an easy workaround for a shortcoming in the SiteCatalyst product.

Final Thoughts

Using a corporate login and corporate labels is not a significant undertaking, but these tips can save you a lot of time and heartache in the long run if used correctly. You will be amazed at how quickly SiteCatalyst implementations can get out of hand and these techniques will hopefully help you control the madness! If you have similar techniques, feel free to leave them as comments here…

Published on November 26, 2012 under Administration Console, Calculated Metrics, Dashboards, General Implementation

De-Duped Success Metrics

When working with SiteCatalyst clients, I often see them ask questions related to how often a particular Success Event takes place at least once during a visit. Examples of this might include the following questions:

  • In what percent of visits do visitors add an item to the shopping cart?
  • How often to visitors who add items to the cart reach checkout?
  • What percent of visits do visitors conduct an onsite search?

At first glance, these seem like easy questions to answer, but I see clients making mistakes with these questions. For example, let’s say that you want to answer the first question above and see the percent of all visits that add items to the shopping cart. Most clients would approach this question by creating a calculated metric that divides Cart Additions (scAdd) by Visits. While this seems logical, it will not give you the correct answer, since visitors can add multiple items to the shopping cart within the visit. If Visitor X adds three items to the cart in the visit, the formula in our calculated metric would be:

The issue is that since most people look at this metric for all visits, the individual Cart Addition numbers are obfuscated and you are often seeing an inflated percentage for Cart Add/Visit %. In fact, the same issue applies to all of the questions listed above. If you are looking to compare Cart Additions to Checkouts, multiple Cart Additions or Checkouts taking place in a visit could inflate your ratio.

So how would you resolve this issue? There are several ways in SiteCatalyst to accurately report on the preceding questions so I will share the various methods at your disposal.

Using De-Duped Success Metrics

The easiest way to resolve the preceding dilemma is to set an additional “de-duped” version of metrics that you want to see in calculated metrics like the ones above. Personally, I wish Adobe provided an easy way in SiteCatalyst to see a de-duped version of every Success Event, but that is not currently available. Therefore, you will have to create a second Success Event for those metrics that you want to use in these types of Calculated Metrics. Keep in mind that you are limited to around one hundred Success Events so you won’t want to do this for all of your Success Events, so use your best judgment.

In this case, let’s assume that you are interested in seeing an accurate percent of visits in which a Cart Addition took place. To do this, every time you set the normal Cart Addition Success Event (scAdd), you should set a second, custom Success Event and call it something like “Cart Adds (De-Duped).” For this second Success Event, you will want to apply Success Event serialization to prevent the event from being counted more than once in a visit. I would recommend using “Once per Visit” serialization since it requires less tagging and can be enabled by ClientCare. By setting this new Success Event, you will have a count of how often visitors add items to the cart, but it will only be counted once, regardless of how many times the visitor adds items to the shopping cart within the visit. When this is complete, you can create a calculated metric that divides this “Cart Adds (De-Duped)” metric by the Visits metric to see an accurate ratio for visits in which at least one Cart Addition took place:

To see the impact of this, let’s imagine that you had five website visitors that performed the following actions:

In this scenario, if we used a calculated metric that used Cart Additions and Visits, our ratio would be 120% for these five visitors. Obviously, this isn’t representative of what really happened. However, if we use our new “Cart Additions De-Duped” Success Event, we will only count one Cart Addition per Visit and see the following data:

Doing this provides a more accurate representation that 60% of visits contained at least one Cart Addition. And since you now have a Calculated Metric that is trustworthy, you can see the answer to this question trended over time using a report like the one shown here:

This Calculated Metric can be easily added to a SiteCatalyst Dashboard and can be used like any other Calculated Metric.

Note: Some companies implement the Carts (scOpen) Success Event at the first shopping cart addition and de-dup it using “Once per Visit” serialization. This is a similar approach, so if you are doing this, you can use the Carts Success Event divided by Visits to see the same cart rate.

As you can see, the addition of one more Success Event allows us to greatly improve our reporting for cases in which you want to see if something happened at least once in a website visit. If you look at the other questions posed above, you will see that the same concept can be applied. For example, if you want to see an accurate ratio of times that visitors do at least one Cart Addition and one Checkout, you might create a “De-Duplicated” version of Cart Additions and Checkouts and use those versions in your Calculated Metric.

Keep in mind that these new “De-Duplicated” metrics will not be accurate when used in Conversion Variable reports (i.e. Products Report, eVars, etc…) since they will only be counted the first time the Success Event takes place. This means that if a visitor adds three products to the shopping cart, only the first product will be associated with a value in the conversion variable (i.e. Product XYZ). These new “De-duplicated” metrics should only be used in global website calculated metrics and the normal metrics (i.e. Cart Additions) should be used in detailed Conversion Variable reports.

Segmentation Approach

If you are averse to using more Success Events to answer the questions above, it is possible to answer them using Segmentation. I think this method is more cumbersome, but will describe how to do it for educational purposes.

To use Segmentation to answer any “how often did X happen in a visit” question, you will have to create a Visit-based segment that isolates visits in which the Success Event in question took place. Using the preceding example, if you wanted to see how often visits contained a Cart Addition, you would create a Visit segment and add the Cart Addition Success Event to the segment as shown here:

Once you have this segment, you can open the Visits report and see how many Visits took place in the desired timeframe. For this example, let’s use the data for the five visitors we described above. In this case, three of the five visits would qualify to be included in the segment so our Visits report would show a total of three. Now you can write that number down and then remove the segment (go back to “All Visits”) and look at the same Visits report for the entire population. In this case, you would see a total of five visits, so you can divide the three Cart Addition visits by the total visits to get the same 60% we saw above. If this is something you will be doing on a recurring basis, you can automate this process using Adobe ReportBuilder. To do this, you would create two different data blocks in Excel – one for all Visits and one for Visits with the above segment applied. Then you can create a formula that divides the totals of these two data blocks and trend it over time using a custom graph.

As I mentioned previously, I think this approach is more time consuming, but it does save Success Events if that is a concern.

Page Name Approach

In theory, there is another way to answer these types of questions, though I don’t recommend it. This approach involves using Page Names. To do this, you can use Adobe ReportBuilder to isolate the specific page on which a Success Event takes place (i.e. Cart Addition page) and look at that pages’ Visit count and divide it by total Visits. However, since page names can be unreliable and it still requires work in Adobe ReportBuilder, I don’t recommend this approach.

Final Thoughts

If you ever have questions in which people ask you how often something took place at least once in a website visit, I hope that you will think about these concepts and make sure that you are accurately answering them for your organization. While some of these concepts are a bit complex, they can save you the embarrassment of reporting inflated conversion metrics to your organization.

Published on November 19, 2012 under Segmentation, Serialization, Success Events


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Three Foundational Tips to Successfully Recruit in Analytics
Michele Kiss, Partner

Hiring in the competitive analytics industry is no easy feat. In most organizations, it can be hard enough to get headcount – let alone actually find the right person! These three foundational tips are drawn from successful hiring processes in a variety of verticals and organizations.

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Slack Demystified
Adam Greco, Senior Partner

Those of you who follow my blog have come to know that when I learn a product (like Adobe SiteCatalyst), I really get to know it and evangelize it. Back in the 90′s I learned the Lotus Notes enterprise collaboration software and soon became one of the most proficient Lotus Notes developers in the world, building most of Arthur Andersen’s global internal Lotus Notes apps. In the 2000′s, I came across Omniture SiteCatalyst, and after a while had published hundreds of blog posts on Omniture’s (Adobe’s) website and my own and eventually a book! One of my favorite pastimes is finding creative ways to apply a technology to solve everyday problems or to make life easier.

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Profile Website Visitors via Campaign Codes and More
Adam Greco, Senior Partner

One of the things customers ask me about is the ability to profile website visitors. Unfortunately, most visitors to websites are anonymous, so you don't know if they are young, old, rich, poor, etc. If you are lucky enough to have authentication or a login on your website, you may have some of this information, but for most of my clients the "known" percentage is relatively low. In this post, I'll share some things you can do to increase your visitor profiling by using advertising campaigns and other tools.

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A Primer on Cookies in Web Analytics
Josh West, Partner

Some of you may have noticed that I don't blog as much as some of my colleagues (not to mention any names, but this one, this one, or this one). The main reason is that I'm a total nerd (just ask my wife), but in a way that is different from most analytics professionals. I don't spend all day in the data - I spend all data writing code. And it's often hard to translate code into entertaining blog posts, especially for the folks that tend to spend a lot of time reading what my partners have to say.

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Excel Dropdowns Done Right
Tim Wilson, Partner

Do you used in-cell dropdowns in your spreadsheets? I used them all the time. It's both an ease-of-use and a data quality maneuver: clicking a dropdown is faster than typing a value, and it's really hard to mis-type a value when you're not actually typing!

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The Downfall of Tesco and the Omniscience of Analytics
Michele Kiss, Partner

Yesterday, an article in the Harvard Business Review provided food for thought for the analytics industry. In Tesco's Downfall Is a Warning to Data-Driven Retailers, author Michael Schrage ponders how a darling of the "analytics as a competitive advantage" stories, British retailer Tesco, failed so spectacularly - despite a wealth of data and customer insight.

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Creating Conversion Funnels via Segmentation
Adam Greco, Senior Partner

Regardless of what type of website you manage, it is bound to have some sort of conversion funnel. If you are an online retailer, your funnel may consist of people looking at products, selecting products, and then buying products. If you are a B2B company, your funnel may be higher-level like acquisition, research, trial and then form completion.

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10 Tips for Building a Dashboard in Excel
Tim Wilson, Partner

This post has an unintentionally link bait-y post title, I realize. But, I did a quick thought experiment a few weeks ago after walking a client through the structure of a dashboard I'd built for them to see if I could come up with ten discrete tips that I'd put to use when I built it. Turns out…I can!

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Exploring Optimal Post Timing ... Redux
Tim Wilson, Partner

Back in 2012, I developed an Excel worksheet that would take post-level data exported from Facebook Insights and do a little pivot tabling on it to generate some simple heat maps that would provide a visual way to explore when, for a given page, the optimal times of day and days of the week are for posting.

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What I Love: Adobe and Google Analytics*
Tim Wilson, Partner

While in Atlanta last week for ACCELERATE, I got into the age-old discussion of "Adobe Analytics vs. Google Analytics." I'm up to my elbows in both of them, and they're both gunning for each other, so this list is a lot shorter than it would have been a couple of years ago.

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Top 5 Metrics You're Measuring Incorrectly ... or Not
Eric T. Peterson, Senior Partner

Last night as I was casually perusing the days digital analytics news - yes, yes I really do that - I came across a headline and article that got my attention. While the article's title ("Top 5 Metrics You're Measuring Incorrectly") is the sort I am used to seeing in our Buzzfeed-ified world of pithy "made you click" headlines, it was the article's author that got my attention.

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Bulletproof Business Requirements
John Lovett, Senior Partner

As a digital analytics professional, you've probably been tasked with collecting business requirements for measuring a new website/app/feature/etc. This seems like a task that's easy enough, but all too often people get wrapped around the axle and fail to capture what's truly important from a business users' perspective. The result is typically a great deal of wasted time, frustrated business users, and a deep-seated distrust for analytics data.

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Welcome to Team Demystified: Nancy Koons and Elizabeth Eckels!
Eric T. Peterson, Senior Partner

I am delighted to announce that our Team Demystified business unit is continuing to expand with the addition of Nancy Koons and Elizabeth "Smalls" Eckels. Our Team Demystified efforts are exceeding all expectation and are allowing Web Analytics Demystified to provide truly world-class services to our Enterprise-class clients at an entirely new scale.

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When to Use Variables vs SAINT in Adobe Analytics
Adam Greco, Senior Partner

In one of my recent Adobe SiteCatalyst (Analytics) "Top Gun" training classes, a student asked me the following question: When should you use a variable (i.e. eVar or sProp) vs. using SAINT Classifications? This is an interesting question that comes up often, so I thought I would share my thoughts on this and my rules of thumb on the topic.

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5 Tips for #ACCELERATE Exceptionalism
Tim Wilson, Partner

Next month's ACCELERATE conference in Atlanta on September 18th will be the fifth - FIFTH!!! - one. I wish I could say I'd attended every one, but, sadly, I missed Boston due to a recent job change at the time. I was there in San Francisco in 2010, I made a day trip to Chicago in 2011, and I personally scheduled fantastic weather for Columbus in 2013.

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I've Become Aware that Awareness Is a #measure Bugaboo
Tim Wilson, Partner

A Big Question that social and digital media marketers grapple with constantly, whether they realize it or not: Is "awareness" a valid objective for marketing activity?

I've gotten into more than a few heated debates that, at their core, center around this question. Some of those debates have been with myself (those are the ones where I most need a skilled moderator!).

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Advanced Conversion Syntax Merchandising
Adam Greco, Senior Partner

As I have mentioned in the past, one of the Adobe SiteCatalyst (Analytics) topics I loathe talking about is Product Merchandising. Product Merchandising is complicated and often leaves people scratching their heads in my "Top Gun" training classes. However, many people have mentioned to me that my previous post on Product Merchandising eVars helped them a lot so I am going to continue sharing information on this topic.

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Team Demystified Update from Wendy Greco
Eric T. Peterson, Senior Partner

When Eric Peterson asked me to lead Team Demystified a year ago, I couldn't say no! Having seen how hard all of the Web Analytics Demystified partners work and that they are still not able to keep up with the demand of clients for their services, it made sense for Web Analytics Demystified to find another way to scale their services. Since the Demystified team knows all of the best people in our industry and has tons of great clients, it is not surprising that our new Team Demystified venture has taken off as quickly as it has.

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SiteCatalyst Unannounced Features
Adam Greco, Senior Partner

Lately, Adobe has been sneaking in some cool new features into the SiteCatalyst product and doing it without much fanfare. While I am sure these are buried somewhere in release notes, I thought I'd call out two of them that I really like, so you know that they are there.

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Hello. I'm a Radical Analytics Pragmatist
Tim Wilson, Partner

I was reading a post last week by one of the Big Names in web analytics…and it royally pissed me off. I started to comment and then thought, "Why pick a fight?" We've had more than enough of those for our little industry over the past few years. So I let it go.

Except I didn't let it go.

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Competitor Pricing Analysis
Adam Greco, Senior Partner

One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being "Amazon-ed," which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on Amazon.com. This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping.

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How to Deliver Better Recommendations: Forecast the Impact!
Michele Kiss, Partner

One of the most valuable ways to be sure your recommendations are heard is to forecast the impact of your proposal. Consider what is more likely to be heard: "I think we should do X ..." vs "I think we should do X, and with a 2% increase in conversion, that would drive a $1MM increase in revenue ..."

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ACCELERATE 2014 "Advanced Analytics Education" Classes Posted
Eric T. Peterson, Senior Partner

I am delighted to share the news that our 2014 "Advanced Analytics Education" classes have been posted and are available for registration. We expanded our offering this year and will be offering four concurrent analytics and optimization training sessions from all of the Web Analytics Demystified Partners and Senior Partners on September 16th and 17th at the Cobb Galaria in Atlanta, Georgia.

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Product Cart Addition Sequence
Adam Greco, Senior Partner

In working with a client recently, an interesting question arose around cart additions. This client wanted to know the order in which visitors were adding products to the shopping cart. Which products tended to be added first, second third, etc.? They also wanted to know which products were added after a specific product was added to the cart (i.e. if a visitor adds product A, what is the next product they tend to add?). Finally, they wondered which cart add product combinations most often lead to orders.

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7 Tips For Delivering Better Analytics Recommendations
Michele Kiss, Partner

As an analyst, your value is not just in the data you deliver, but in the insight and recommendations you can provide. But what is an analyst to do when those recommendations seem to fall on deaf ears?

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Overcoming The Analyst Curse: DON'T Show Your Math!
Michele Kiss, Partner

If I could give one piece of advice to an aspiring analyst, it would be this: Stop showing your "math". A tendency towards "TMI deliverables" is common, especially in newer analysts. However, while analysts typically do this in an attempt to demonstrate credibility ("See? I used all the right data and methods!") they do so at the expense of actually being heard.

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Making Tables of Numbers Comprehensible
Tim Wilson, Partner

I'm always amazed (read: dismayed) when I see the results of an analysis presented with a key set of the results delivered as a raw table of numbers. It is impossible to instantly comprehend a data table that has more than 3 or 4 rows and 3 or 4 columns. And, "instant comprehension" should be the goal of any presentation of information - it's the hook that gets your audience's brain wrapped around the material and ready to ponder it more deeply.

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Automating the Cleanup of Facebook Insights Exports
Tim Wilson, Partner

This post (the download, really - it's not much of a post) is about dealing with exports from Facebook Insights. If that's not something you do, skip it. Go back to Facebook and watch some cat videos. If you are in a situation where you get data about your Facebook page by exporting .csv or .xls files from the Facebook Insights web interface, then you probably sometimes think you need a 52" monitor to manage the horizontal scrolling.

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The Recent Forrester Wave on Web Analytics ... is Wrong
Eric T. Peterson, Senior Partner

Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries.

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Funnel Visualizations That Make Sense
Tim Wilson, Partner

Funnels, as a concept, make some sense (although someone once made a good argument that they make no sense, since, when the concept is applied by marketers, the funnel is really more a "very, very leaky funnel," which would be a worthless funnel - real-world funnels get all of a liquid from a wide opening through a smaller spout; but, let's not quibble).

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Reenergizing Your Web Analytics Program & Implementation
Adam Greco, Senior Partner

Those of you who have read my blog posts (and book) over the years, know that I have lots of opinions when it comes to web analytics, web analytics implementations and especially those using Adobe Analytics. Whenever possible, I try to impart lessons I have learned during my web analytics career so you can improve things at your organization.

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Registration for ACCELERATE 2014 is now open
Eric T. Peterson, Senior Partner

I am excited to announce that registration for ACCELERATE 2014 on September 18th in Atlanta, Georgia is now open. You can learn more about the event and our unique "Ten Tips in Twenty Minutes" format on our ACCELERATE mini-site, and we plan to have registration open for our Advanced Analytics Education pre-ACCELERATE training sessions in the coming weeks.

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Current Order Value
Adam Greco, Senior Partner

I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.

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A Guide to Segment Sharing in Adobe Analytics
Tim Wilson, Partner

Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I've found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.

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Currencies & Exchange Rates
Adam Greco, Senior Partner

If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.

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Linking Authenticated Visitors Across Devices
Adam Greco, Senior Partner

In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.

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The 80/20 Rule for Analytics Teams
Eric T. Peterson, Senior Partner

I had the pleasure last week of visiting with one of Web Analytics Demystified's longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.

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Ten Things You Should ALWAYS Do (or Not Do) in Excel
Tim Wilson, Partner

Last week I was surprised by the Twitter conversation a fairly innocuous vent-via-Twitter tweet started, with several people noting that they had no idea you could simple turn off the gridlines.

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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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