Tag Archives: configuration

calculated-metrics

A Powerful Use Case for GA Calculated Metrics

By now everyone should be excited about the release of Calculated Metrics within Google Analytics. This has the potential to be yet another powerful tool for analysis, although it will only prove useful to companies who are investing the time and resources in a good GA set-up. As a bonus, it shuts down another line in the arguments between Google & Adobe Analytics.

Details on how to create Calculated Metrics can be found in some great blog posts, notably those by LunaMetrics and AnalyticsPros, including a solid list of suggestions to get you started. It is a staggered roll out of the new feature so don’t be alarmed if you don’t have access yet, it shouldn’t be far away from appearing in your GA Account.

But I was confused by these and other blog posts as they appeared to be missing the most obvious and powerful use of Calculated Metrics. Most businesses have some form of funnel at the core of their website. In nearly every GA set-up that L3 Analytics performs, we create a goal for each stage of this process. Our clients can then create a horizontal funnel, with this being an incredibly useful tool for analysing performance.

With Calculated Metrics, you can now create the completion rate between each stage of the funnel. It is as simple as Goal Y Completions / Goal X Completions. This set of calculated metrics can then be used with any session or user based dimension to see where visitors are dropping out of the process. We have been doing this within Excel for years and it is great to finally be able to do it directly within GA. It will speed up the analysis process immensely and offer more flexibility in which dimensions to drill into.

Step by Step Instructions

Step 1 – Create a Goal for each stage of the funnel

As mentioned, most websites have a funnel as the core component of their customer journey. It is obvious for any ecommerce website but also true for booking engines and lead generation websites. As a first step, identify each stage in the funnel, ensure it is being tracked and create a Goal based on the page name or event being fired.

The following set of goals reflects the funnel for a retail website (where the visitor is not taken directly to the basket after creating it). Note that an Ecommerce Session is one where a visitor is interested in a purchase.

Calculated Metrics - Goal List

Step 2 – Create the Calculated Metrics

The next step is to create a Calculated Metric for the completion rate between each stage of the process. This uses Goal Completions. So the calculations are:

  • {{View Product (Goal 2 Completions)}} / {{Ecommerce Session (Goal 1 Completions)}}
  • {{Create Basket (Goal 3 Completions)}} / {{View Product (Goal 2 Completions)}}
  • {{View Basket (Goal 4 Completions)}} / {{Create Basket (Goal 3 Completions)}}
  • And so on…

Calculated Metrics CreationThe formatting type needs to be percent as per above. I discovered that as long as you are creating good Calculated Metric names, the external names will take care of themselves.

Calculated Metrics List

Step 3 – Use these Calculated Metrics within Custom Reports

All of these Calculated Metrics can then be used within a custom report. In this example, we will be creating a Funnel metric group. Start the sequence with “Sessions” and “Goal X Conversion Rate” to show total traffic and % of sessions that progress to stage 1 of the funnel. Then list the calculated metrics for completing each stage of the funnel process, finishing with the number of total conversions.

Calculated Metrics - Custom Report Set-up

Multiple Metric Groups could be used in these custom reports, for traffic metrics, the funnel, ecommerce metrics, etc. However the powerful thing here is the range of dimensions to choose from. Common options would include:

  • Device Category
  • Channel
  • Browser
  • Country
  • User Type

If you are capturing Visitor Type (prospect vs customer) in a custom dimension and/or Page Type in a Content Group (use Landing Page group to get Entry Points), this all gets more amazing.

Below is what you get as an output: a simple breakdown by stage of the funnel for whatever dimension/s you have selected. As a custom report, you would be creating it so you can drill down through dimensions to make it even more useful.

Calculated Metrics - Custom Report Output

Knowing that your Conversion Rate is lower for segment X vs segment Y is not that valuable. Knowing that two dimension values behave exactly the same except for one stage in the funnel pinpoints where you have to take action.

Additional Points, Notes and Caveats

It must be noted that this technique works on the assumption that visitors must progress through each stage of the funnel. We know that this is not the case, especially when, for the above example, visitors could be entering the website with a persistent basket or creating a cart without viewing a product page. It is the job of the analyst to take these factors into account with any recommendations they make.

Further note that this is all session based analysis, as it is using Goals. For many businesses, visitors will take multiple sessions to convert. This approach is still useful though, in terms of seeing how far through the funnel the visitors proceed each time.

The overall technique is similar to an approach suggested by LunaMetrics back in June 2010. Their suggestion was to create a series of two step GA funnels for each stage of the website funnel and use Goal Abandonments for reporting. It would produce a similar report, although I prefer completion rates. It also means each stage needs to be based on pages, whereas this approach means you can use goals created from events.

Funnels do not have to be complex journeys. If you have a Contact Form on your website, it is more useful to know the number of form completions than to know the % sessions in which the form was submitted. This requires a goal for View Contact Form, a goal for Submit Contact Form, and a Calculated Metric for the completion rate.

Finally, we are looking into other Calculated Metrics as well. There is a list for content websites to calculate: Read Rate, Share Rate, Entry Rate, Engagement Score, etc. Watch this space for more ideas in the future…

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Generating Custom Dimensions from Page Names

Custom Dimensions

I discovered a few months back that you can use Custom Dimensions within Google Analytics filters. This was great as it allowed us to created segmented Views based on session/user level custom dimensions (created using GTM via a Data Layer) for segments like Subscriber Traffic, Free Traffic, Desktop website and Mobile website.

But I had an idea in the back of my mind and finally found an excuse to test it out a couple of weeks ago.

We have a client that we are working with to implement enhanced Google Analytics tracking. Like most organisations, there is a development cycle and the new Data Layer is scheduled to be worked on in a couple of months’ time (ideally it is worked on sooner but we have experienced far longer waits as well). So we were looking for quick wins in the meantime.

Reviewing page names, their core page is a product details page. And the URL includes an ID, a code for the previous page and even the index for the location of this product within the product list on that previous page. All great information but excessive for these page names (they were actually breaking the GA table limit).

Back to my idea. Could we use GA View Filters to create hit level custom dimensions? This client is still on classic GA so can’t create custom dimensions via the code. Only one way to find out…

The current page name uses the structure below. It is not the sort of URL I would construct for SEO purposes but happy to take it for analytics as it was about to become very useful.

/product-<product id>/<source index>/<source description>
e.g. /product-456827/5/hpg

The first step is to create the Custom Dimensions within the Google Analytics configuration. Just enter the name you want to use and leave the scope as Hit (as per the screenshot at the top of this post)

Then you will discover that these custom dimensions appear in the list of dimensions that can be used within the GA View Filters. They are right at the bottom of the list.

Selecting Custom Dimensions in View Filter

To populate these new custom dimensions, you need to create three View Filters. As always, use a good naming convention so it is easy to identify the purpose of the filter. The filter type we need is a Custom Advanced filter.

With this example, Field A needs to be the Request URI (which is the page name). I used a regular expression to identify the page naming convention that I need. A key point in this is to enclose any element that you wish GA to remember within brackets. All three key elements are wrapped in brackets for this example.

^/product-([0-9]+)/([0-9]+)/([a-z]+)

Then set the Output Field to the desired custom dimension and populate it with the element extracted from the page name. In this example, we have used $A1 e.g. the first element in brackets within Field A. The other two custom dimensions will need to use $A2 and $A3.

Populating a Custom Dimension using View Filter

Checking the data a day later showed success. The custom dimensions are populated with the product ID, access method and access index values. Through another filter, we even renamed all the product pages to /product-page, bringing the number of unique page names within GA table limits without losing any data.

With custom reports or secondary dimensions, we can now review performance of these pages in more detail than previously possible. All without any need for dev involvement or even the use of Google Tag Manager (and I know you could do all this via GTM but this was easier still). Long term we will do this properly via the Data Layer but short term, a big win with a small amount of work.

So what about you, do you have any nuggets of information hidden within URLs/page names that could be extracted into Custom Dimensions?

road-arrows

Accurate Sessions by Traffic Source

Possibly the most annoying piece of “business logic” within Google Analytics (in my opinion) is that Direct Entry sessions are attributed back to the original traffic source.  I want to know what is really happening!!  I want to know if people are really coming from organic search, emails, affiliates, etc or if they are using remembered URLs, bookmarks, typing directly into the address bar.  But the acquisition reports in Google Analytics don’t tell us that.

Yes, I know the alleged reasons as to why this business logic is in place.  To give Google as much credit as possible for paid search marketing (and a better ROI on spend).  And this might be true.  The claimed reason – that people should be looking at the channel that caused the session to occur – is just as valid in a sense (although a bit flaky).  Let’s ignore that discussion and get into the data.

And yes, the last click method for claiming credit for sales/leads/conversions is bad (don’t get me started on attribution) but I am not going into that now, I just want to know the split by traffic source for sessions.

I thought of a trick one day…

And it’s a really simple one too…

Create a destination goal where the page matches via regex with /.  This should be triggered by every single visit.  And as goals can only be triggered once per visit, the number of goal completions will match the number of sessions (with a very very small level of difference).

Goal setup for all sessions goal

Then select the Conversions => Multi-Channel Funnels => Assisted Conversions report.  Change the Conversion Type so the report is only for the “All Sessions” goal (obviously this won’t work until data has collected for this goal).  The metric of “Last Click or Direct Conversions” now equates to sessions for each MCF Channel Grouping or Source/Medium or Source or Medium or any of the other dimensions (and secondary dimensions) you can select.

Question answered…

The trick here (in case you weren’t aware) is that the Multi-channel reports use the true traffic source, they don’t revert Direct Entry sessions to the previous traffic source.  Sessions is not an available metric by default but we just changed that.

Read the Google Analytics documentation under “How Direct Traffic is Treated” to confirm the way in which direct traffic is treated within the normal acquisition reports and the multi-channel funnel reports.

Enjoy…

Advanced-Segments1.png

20 Useful Google Analytics Segments

Google Analytics provides you with 13 default segments in their Advanced Segment feature.  These segments are pretty good and they do tick a few of the boxes for the segments you would use most regularly.  But for me, they aren’t enough, there are more segments that are relevant to every website which need be added to your toolkit.

Below is a list of segments that I have set up within my GA login that I use with all clients.  Some will work for every website while others require some customisation to make appropriate to you.  And I admit freely there are many more “standard” segments that could be created, I did try and restrict this to the more common ones.

Note all segments start with a . to ensure they are listed at the top of the Custom Segments list.  When creating custom segments, I do recommend being careful with your naming convention so it is easy to rediscover useful segments.  And if anyone at Google is listening, please change the default setting to save a custom segment in this profile only, not in all profiles.

Device

These are based on the new dimension of Device Category (I am assuming that it is fairly accurate).  I am considering splitting Desktop by operating system (Windows, Macintosh and Other) but haven’t reached that stage yet.

Browser

These are the four key browsers, you could add Opera, etc plus you could get more granular with segments for each Browser version (particularly useful for IE versions).

Channel

The default segments provided by Google already include Organic Search, Paid Search, Direct and Referral.  These additional segments rely on campaign tracking having been implemented with a standard naming convention – although Social Media should work as is.  Again it is easy to extend for more channels or to get more granular e.g. each social media network.

Location

I have recently started using a simple segmentation of Domestic and International for websites based in a single country.  These segments here are for the UK but could easily be modified for any other country.  Plus you can create a segment for each of your key countries as I have done for the US.

Entry Point

These segments depend on your page naming convention.  The homepage one should work for most websites (as long as the homepage name is either /, /homepage or starts with /index).  The other two are examples, one for a retail website, one for any website containing a blog.  I would recommend setting up these segments for all key page types within your website.

As an example, for a retail website, I would use entry points of Homepage, Category pages, Product List pages, Product pages & Other (potentially adding Search Results pages, Basket page, Blog pages, etc).

Fake Traffic

If you aren’t aware, you can sometimes get fake traffic being recorded within your Google Analytics data.  The first relates to bots crawling your website or something, I have never discovered exactly what.  The second is when the Safari Top Sites feature views your website to grab an image.

If you want to know if either issue is affecting you, apply the first two segments to your data.  If you are getting traffic from bots, you can exclude them from your profiles using a profile filter, although this will only impact traffic going forward.  Use the 3rd segment to view historical data.

There are some blog posts recommending how to eliminate the issue caused by Safari Top Sites.  Please let me know if you have a solution which works.  Unfortunately a segment that allows you to view clean historical data cannot be created within GA.

Suggestions?

Anyone want to suggest some more standard GA segments?  Just remember they should be applicable to any website, not just a really cool segment that is incredibly useful to you.

URL Query Parameters List Builder for Google Analytics

Screenshot of GA configuration for excluding URL Query Parameters

An important step in configuring a Google Analytics account is to clean up page names.  Users should be able to identify a page based on the page name and be able to group pages in a logical manner.  The tools for doing this are excluding URL Query Parameters and renaming pages using Profile Filters.

The purpose behind excluding URL Query Parameters is so each page on the website has a unique page name within Google Analytics.  Some URL Query Parameters may actually be useful, maybe they differentiate between product detail pages for different products or contain the article name on a content website.  But in my experience, most add zero value to the page name and the analysis.  This post on the Google Analytics blog provides some more information on why and how to make content reports more useful.

Identifying the complete list of URL Query Parameters can take a while.  I seem to find the first few very quickly but then miss others and have to go back a couple of days later and so on.  So I had an idea the other day to automate the process.

The outcome is this simple Excel tool.  It is not pretty (sorry about that) but it is effective.  Simply paste in a list of GA page names that contain URL Query Parameters and run a macro.  It may take a minute or two to run but the output is a list of all URL Query Parameters, the number of page names in which they appeared and even a string containing all URL Query Parameters which can be copied straight into the Google Analytics configuration.  More complete instructions can be found within the tool itself.  Note that macros need to be enabled for the tool to work.

The tool does not recommend which URL Query Parameters should be removed.  That is down to the web analyst to decide, remembering that web analytics is at least as much an art as a science.  A big step towards this would be to work with the IT team to identify each parameter and its purpose in the URL.

Please let me know any questions and I would appreciate any feedback if you have found this tool useful.

Update

The formula in column B on the Processed Data worksheet has been altered to account for the case where the URL Query Parameter name is blank e.g. =56372 so that a blank cell is not generated.

I have discovered that a URL Query Parameter of ?? will cause an inflated count of occurrences, no fix has been made for this.

Download Excel 2007 version

Download Excel 97-03 version

You can too do Content Grouping in Google Analytics

Seeds grouped by type

I have noticed various posts and comments about how you can’t do content grouping in Google Analytics, that you need to use SiteCatalyst or a similar paid tool in order to do this.  These comments surprise me as one of my first actions with a new client using Google Analytics is to set up content groupings.  So I thought I would write a post detailing what I do and how easy it is.

But I did a quick bit of research before I started and I found that Allaedin Ezzedin of E-Nor had got there first a couple of years ago with this excellent post on content grouping in GA, written for exactly the same reasons.  The first comment is even from the guru Avinash commenting on how you can group functional pages for ecommerce websites.

Reading this post has made me question when I started grouping pages in Google Analytics, whether it was based on my general approach to web analytics and techniques used with other web analytics tools (SiteStat, SiteCatalyst and HBX) or whether it came from this very blog post – I honestly can’t remember.  But explaining what I do may still help some people.

When working on the set-up of Google Analytics for a new client, I am getting into the habit of setting up three or more profiles.  Not for different traffic sources or the reasons people seem to usually create multiple profiles but instead for different levels of content grouping.  So you might end up with these profiles:

  • Level 1 – Page Types only so likely less than 20 page names in use.  A Navigation Summary report for an ecommerce website using this profile could easily see if visitors access the Product Details pages by landing on them or via Product List pages, Search Results pages, etc.
  • Level 2 – Pages would follow the naming convention of /<directory>/<sub-directory> allowing for an additional level of detail.  So article pages on a content website would be grouped by the category and then by topic.
  • Level 3 – Would contain complete page names following a sound and logical page naming convention.  Pages would have been renamed where necessary and this would be the primary profile used in everyday analysis.
  • Level 4 – This profile would still have irrelevant URL parameters stripped out but pages would not be renamed.  It would be used for auditing data similar to a profile that does not have internal traffic filtered out.

If you have a good hierarchical page naming convention in place using a format like /<directory>/<sub-directory>/<page name>, the filters are very easy to set up to create these profiles.  For a Level 1 profile, all you would need is this filter.

Directory Level Page filter

So really, as Allaedin said, there is no reason why you can’t do content grouping in Google Analytics and there are plenty of benefits to be gained from doing so.   GA may not designed for content grouping like SiteCatalyst and other tools but on the plus side, the customisation is possible without any code changes required.

Please add a comment if you have any questions regarding this technique or suggestions for how it could be used.

Search Term Categorisation in Google Analytics

Digging around to understand search marketing performance can be difficult.  A site may receive traffic from tens of thousands of unique search terms, too much data to effectively analyse.  As a result, it is difficult to know what search terms or types of search terms are important and performing well for your business (even with Weighted Sort).

Analysing this performance can be vital for understanding customer intent and making business decisions such as which search terms to invest in with PPC/SEO or where landing pages need to be developed.  So what are your options?

What people do currently

Web Analytics tools includes standard reporting that split search marketing performance into Paid and Natural Search or by Search Engine.  This is a great start but ultimately only provides limited insights as both options are very high level segmentations.

It is already fairly common practice to split search terms into brand and non-brand.  There are numerous blog posts out there describing how you can track brand search terms using Filters or create Advanced Segments for brand and non-brand search terms.  This is usually done through the use of often quite simple Regular Expressions as demonstrated in this post on tracking non-brand keywords.

Once you have your filters or advanced segments in place, you have another level of segmentation for understanding the performance of your search marketing.  But still, it is another binary segmentation and can be slightly unwieldy to apply.  What if there was a better way???

My recommended approach

I recommend taking these existing approaches further, creating not two but between four and eight categories.  And to populate a campaign variable such as Campaign Name with the names of these different search term categories instead of creating each category individually using filters or segments.

By doing this, comparisons are much simpler as all categories can be viewed in a single report and data can easily be viewed for a single category only.  All of the existing features of Google Analytics can be applied to the data – drilling through or displaying secondary dimensions, creating custom reports, pivot tables, data visualisation, filters, etc.  Basically you get a mid level of segmentation for your search terms that allows you to understand performance and extract insights to drive your business performance.

The method I am recommending is to create a profile filter for each search term category, based on the values for the search term, to populate another variable with the search term category name.  For organic search, the Name/Campaign and Ad Content fields are not currently populated and can be used for this purpose.  It is more difficult for paid search if you wish to keep Adwords campaign information but there are workarounds.

A regular expression (LunaMetrics has published an excellent e-book on Regular Expressions for Google Analytics) needs to be created for each category but I have found this to be fairly straight forward in most cases – just list the relevant terms for that category and use the pipe “|”as an OR separator.  Other times, you can get around the multiple mis-spellings and variations issue using something simple e.g. “L3” captures all versions of L3 Analytics brand term.  Occasionally you need to get more creative but remember, this is web analytics and data doesn’t need to be 100% accurate to be useful – if some search terms aren’t in the right category, it is not the end of the world.

You do also need to have a clear hierarchy in place as search terms can meet the definition for multiple categories.  Just define in advance that if a search term is both a branded and a product search term, which category it is placed in.

Using this approach with WooThemes

I was reviewing the Google Analytics configuration for the WooThemes website – this is a business built around supplying themes for WordPress and other CMS platforms.  They get decent search engine traffic levels with over 10k unique search terms a month.  The search terms for this website could be categorised as follows:

List of Search Term Categories for WooThemes

Note that with this categorisation hierarchy, any search term that contains a theme name will fall into the Theme bucket, even if it also contains “WooThemes WordPress” – it is set up this way as I believe this to be most useful for WooThemes.  I don’t think there is ever only one best solution for categorisations, it just comes back to what data you can use for making business decisions.

Keeping things simple, I am creating these categories for organic search terms only and using the Campaign Name variable as the destination for the category names.  For this approach, you start with the category lowest in the hierarchy first and work your way up.  That way, search terms that match multiple categories end up in the highest level category.

The first filter doesn’t need a regular expression, as the default option you just populate the Name variable with the name of the category (e.g. Generic Terms) for all organic search terms.

Filter for Generic Terms

Subsequent filters though require a regular expression and for the filter to look at both the Search Term and another variable, e.g. Medium = Organic.  The filters used for WooThemes can be seen below.

Filter for Unbranded CMS Terms

The regular expression used here is an OR statement for the different CMS platforms – expression|wordpress|drupal|magento.  It doesn’t capture mis-spellings but should include the majority of relevant search terms.  Note how I have used “expression” rather than the complete “ExpressionEngine” to capture variations on this search term.

Filter for Brand Terms

This is a simple regular expression, basically every variation of a Brand search contains woo but there are very few other words that would match this pattern of letters, at least not words that people would use to access the WooThemes website.

Filter for Branded CMS Terms

It almost feels like I have cheated a little bit here.  This filter is not built off the Medium of “organic” but instead is a filter applied to search terms that were already captured in a previous filter for Unbranded CMS Terms.  If these search terms also contain “woo”, then the search term is reassigned to the Branded CMS Terms category.

Filter for Theme Terms

The final filter contains all of the different theme names, although in some cases I have just used elements of the theme name.

Note that if you want to test the results for any regular expression that you create, just use it as a filter on a keyword report.  If you select “containing”, these are the keywords included (do they all look appropriate?).  If you select “excluding”, these are keywords that won’t appear in this category (are they any there that you want to include?).

Once all filters are set up, check the filter order for that profile.  I have found that filters don’t always follow the order in which they were set up so make adjustments as required.  Then sit back and wait for the reports to populate with categorised search term data.

Something else you can segment by

This approach to grouping search terms triggered a memory though – that logic existed for grouping search terms by the number of keywords that they contain.  A bit of research and I found this post from LunaMetrics on Keyword Analysis by Number of Terms.  Just note though that the regular expression that I found to work was

^(W*w+bW*){3}$ NOT ^(W*w+bW*){3}$

The exact same approach as above applies here.  You do need to choose a level beyond which search terms are grouped however many keywords they contain e.g. 5+.  This is the filter that you create first without any regular expression required.

Filter for 5+ keyword Terms

Then create a filter for each level of keywords within the search term as can be seen in this filter for search terms containing 2 keywords.

Filter for 2 keyword Terms

In this example, I have used Campaign Content as the field to populate with the category name as this variable is also not currently used by organic search.  However, there appears to be a bug in Google Analytics and I have not been able to populate anything into this field using a profile filter.  But as I said earlier, there are workarounds for this.

Next Time

While I would have loved to include everything I have thought of on this topic within this single post, it reads long enough already.  I am therefore writing a follow-up post covering

  • tips & tricks around implementing and using search term categorisations
  • the sort of insights you can find in the data and business decisions this can drive
  • a request for Google to add this as standard feature in GA and how I envisage this working

This post on Insights from Search Term Categorisation is now available.

Of course, if this sounds amazingly useful for you and you would like some assistance setting up search term categorisations in your Google Analytics account, please contact L3 Analytics on 07843617347 or enquiries@l3analytics.com.

Useful Calculated Metrics for SiteCatalyst

Equation on a blackboard

I created a traffic source report in SiteCatalyst Discover recently and wanted to include some basic engagement style metrics e.g. Average Page Views per Visit, Average Time on Site.  However, with some high bounce rates, I wanted to ignore those visits that did bounce, with the metrics only calculated for visits that viewed at least two pages.

The general equation for this for any metric is:

(<Metric> minus <Metric> for Single Page Visits) / (Visits minus Single Page Visits)

The four required numbers are generally pretty easy to get at but it is complicated here as the reports are based on Last Touch Marketing Channels reports.  As discussed in this blog post on Why SiteCatalyst Visits don’t always equal Entries, not all Marketing Channels Visits are recorded in SiteCatalyst as an Entry and therefore cannot be recorded as Single Page Visits.

The solution is to create an estimated value for this by applying the known Bounce Rate for Entries to the number of Visits (assuming the Bounce Rate is consistent).  This equation is:

[Visits]*([Single Page Visits]/[Entries])

This leaves the only missing number as <Metric> for Single Page Visits.  Here, we can apply some logic to either eliminate it or use a substitute.  Common examples are below:

  • Average Time on Site (per visit) – no time is recorded if only one page is viewed so value is zero
  • Page Views per Visit – as only one page is viewed, the number of page views equals the number of visits
  • Orders per Visit (Conversion Rate) – ignoring an issue with the set-up, you can’t place an order on the first page you view therefore the value is zero

So the set of equations to use in Discover to create the Calculated Metrics are:

Average Time on Site

[Total Time Spent]/([Visits]-([Visits]*([Single Page Visits]/[Entries])))

Page Views per Visit

([Page Views]-([Visits]*([Single Page Visits]/[Entries])))/([Visits]-([Visits]*([Single Page Visits]/[Entries])))

Conversion Rate

[Orders]/([Visits]-([Visits]*([Single Page Visits]/[Entries])))

The final metric there is the conversion rate, again adjusted to eliminate those visits that were never a realistic chance of converting.  The conversion action used was to place an order but this could easily be changed to other conversion actions such as Submit Form.  It is important though that the action/event would only be triggered once within a visit.

So what do you do with these calculated metrics?  You can set up a report for Last Touch Marketing Channels and add metrics for Visits, Bounce Rate*, Page Views per Visit, Average Time on Site, Conversion Rate, Orders and Revenue.  And then analyse, interpret and take actions based on the comparisons between channels for these key metrics.  Or if you drill down (and have classifications applied to the Marketing Channel Detail values where required), take actions based on the comparisons between different campaigns, affiliate networks, search engines or landing pages.

Sample Marketing Channels Report layout

Note this report is only possible with Discover, not SiteCatalyst, as Single Page Visits is a traffic metric and not available for the Marketing Channels report.

If you would like to know more about calculated metrics, this is a good post on how to create calculated metrics.   And, within the Knowledge Base, can be found a list of commonly used calculated metrics for SiteCatalyst.

* If you don’t already have Bounce Rate set up as a calculated metric, bad you.  The calculation to use is [Single Page Visits]/[Entries].