Advanced Remarketing with Google Analytics & Google Tag Manager

From Data Layer to Dollars…

Some visitors are more profitable than others, and thoughtfully created Remarketing Lists can help businesses focus their ad spend on the most valuable visitors.  This can improve revenue, reduce costs, or both.

The key is discovering the common characteristics of visitors which make them more valuable than an average visitor, and then preferentially delivering ads (i.e. bid higher) to users who have these characteristics.  In other words, if you can segment your visitor base to identify which users have a higher potential value, you’ll be able to make smarter decisions with your advertising budget.  Utilizing features of Google Analytics and Google Tag Manager provides the opportunity to do this.

One of my favorite features of the Google Analytics / DoubleClick integration is the ability to add users to Adwords Retargeting lists with the click of a button.  Here’s an example of how I might come up with a good remarketing list:

Let’s start with a curious question –> How long does it take users to convert on the site?  The first place I would go to begin answering this question is by applying a “converted” segment (in this case, a purchase) to the Session Duration report.

transaction segment

transactions duration segment

Right away I notice that it takes a large percentage of users over 10 minutes in order to make a purchase, and over almost 13% require a half an hour or more.  While I very much like segmenting the Engagement Reports, in this particular case I’m going to look at the User Timings report as I believe the data visualization is more helpful there (you can expand the histogram).

user timings conversion duration

You will not find this report “out of the box” in GA.  Rather, we decided to capture the amount of time from session start until purchase and pass that data back to Google Analytics using their User Timings API.  (As a side note, I believe that the User Timings API is quite under utilized by many companies as well as other GA folks out there).  In this particular example, the astute amongst you will quickly notice that values in User Timings histogram don’t match up to the values in the segmented Session Duration report.  That is because we chose to configure out User Timing data in a way that differs from the standard GA session timeout so that we’d have a bit of a different view of data.  But that’s just a side point… The main point that I want to get across is that segmenting my users who converted by the amount of time it took the to convert can be quite useful (as we shall see).

Layering on one more segment, I noticed that the amount of time to purchase differed significantly by Average Order Value.

conv avg order value 

A quick view peek at “unsegmented” average order value reveals that this business has an AOV around $100.  But of course, we can’t look at unsegmented data!  So let’s create a simple segment of purchases that are above and below the AOV.

conversion duration order value

Interestingly, while more than 2/3rds of sales on the site are for small orders, the larger orders on the site are responsible for almost 3/4ths of total revenue.  Now let’s apply these segments to our histograms of how long it takes users to make a purchase.

  conversion duration segmented


engagement segmented by aov

We’re now able to see a quantitative expression for a user behavior that makes a lot of sense –> namely, it takes people more time to make a purchase decision when they are intending to spend more money.  Now that we have some data to back up our intuition, we can allocate our marketing budget in a much smarter way.  For starters, let’s create a powerful remarketing list with just a few clicks and no complicated code required to power our business logic.

remarketing with ease

In the above example, if a user has a session that is longer than 30 minutes AND has not made a purchase before they are added to the remarketing list.  In other words, when we have evidence that the person is more likely to be a high value customer we’ll do our best to make sure our products and brand don’t get forgotten via targeted remarketing.

But let’s say that we want to add this user to a Remarketing Lists for Search Ads (RLSA) list.  RLSA lists work in a very similar way to Google Display Network audience lists, but differ in a few important ways.  As the name suggests, the purpose of the RLSA list is to create a bid modifier for search as opposed to the remarketing display ads which we’re all so familiar with (if you browse the web, that is).   So, if we have a user on our site that we has indicated to us in some way that they are likely to be a valuable customer it makes sense to try to bring that person back to our site if they are still in the process of searching for products or services.  

Impressions to Last Clicks Ratio    

But RLSA lists cannot be generated directly through Google Analytics.  In order for an RLSA remarketing pixel to work, there needs to be a “conversion label” added to the tag.  Here’s where Google Tag Manager comes into the picture.  In almost all of the advanced Google Analytics implementation work we do here at Analytics Ninja, we’re using Google Tag Manager to power the implementation.  There are many, many reasons for this and I’ll do my best to share why using a TMS is a must for most organizations in future blog posts.  For now, we’ll use the example of creating advanced remarketing lists using Google Tag Manager to whet your palate.

  While the current example is for Remarketing Lists for Search Ads, any remarketing pixel (for example, Adroll) can be deployed through Google Tag Manager.  

Choose Remarketing Tag  
First, select create a new Tag and choose your Tag Type.  In our case, we’re going to use an Adwords Remarketing tag.  You’ll need to get the conversion label from Adwords when you create your tag so that you can copy and paste it into GTM.

get the conversion label for rlsa

RLSA Tags in Google Tag Manager    

Now the fun part.  Choose when your tag fires by creating a rule.  Remember, in order for a rule to fire a tag in GTM, either the condition of the rule needs to be true when GTM loads or the rule contains an event.  So in order to get our “more than 30 minutes” tag to fire, we’ll need to send an event into GTM.  With a robust implementation, however, this should be a piece of cake because all of the infrastructure that is driving your digital analytics data collection can be translated into a list of business rules to power “smart” remarketing.
Here are a few examples:

30 minute session  
With a little bit of javascript, we’ll fire an event to track if the user is on our site for more than 30 minutes.  The tag will only fire if the user has not purchased before (with limitations related to cookies and logged-in status).

Watched a product video
This fires the tag when a user watches a than half of a product video.  Since we’re already tracking all video views (including progress) as well as the Page Category, setting up the rule is simple.

Cross sells and upsells remarketing

In this example, we’re beginning to leverage the data we have on cross-sells and up-sells to build out our remarketing lists.  Once a rule is created in GTM, it can be used to fire (or block) any tag.  In other words, the above scenario can be used to build remarketing lists for Adroll, Google RLSAs, etc etc.

  From the data layer to dollars


Google Tag Manager can (and should) be used to power your marketing and not just your digital measurement.  A robust Google Analytics Implementation allows you to create marketing segments that will product meaningful results, and Adwords Remarketing Lists can be created directly from GA segments.  But the Google Display Network is certainly not the only game in town when it comes to remarketing, so take advantage of a Google’s free tag management platform to fuel smart business decisions and deploy your marketing tags in a way that will give your business a competitive advantage over your competitors.

Google’s Universal Analytics is Out of Beta – Time to Switch?

Universal Analytics

The big news last week (at least for folks like me) was that Universal Analytics finally came out of beta.  Is it time for you to switch?  

Short answer  –> yes, soon.  :-)

What exactly is the big deal about Universal Analytics? My current take on the product’s features is what follows:


One of the most touted feature improvements over Google Analytics Classic is the introduction of UserID.  Google lists 4 benefits of using UserID.

  1. More accurate user count
  2. Analyze the signed-in experience
  3. Access the User ID View and Cross Device Reports
  4. Connect your acquistions, engagement, and conversions.

While I see the move towards a most person / customer centric view by the GA team to be a big step in the right direction, I think that at the current juncture the UserID reporting (and data model) falls flat.  Full disclosure: I’ve only had access to the UserID reports for a few days.  AND I am acutely aware that the GA team is constantly innovating and improving their product at a dizzying pace.  That means that the only thing I can truly count on when it comes to GA is that the product will continue to improve (and hopefully not make this blog post completely irrelevant in the next 3 days).

So, why does UserID currently fall flat?  Doesn’t the ability to connect all the dots sound like a marketer’s dream?

Continue reading Google’s Universal Analytics is Out of Beta – Time to Switch?

Shopify Google Analytics Integration

The following is the tale of some investigative work I did for a company which approached me to help them with their Google Analytics tracking for their online store. This company, like so many others, sells items on the Internet and wants to be able to properly attribute their sales to the correct channel using Google Analytics. Not surprisingly, in addition to having subdomains they were using a third party shopping cart and needed to have cross domain tracking configured.  Pretty simple.  Or so I thought…

  Shopify Homepage

As it turns out, the third party shopping cart they were using is called Shopify.  As far as being an intuitive user interface that makes it easy for non-technical users to set up their store, I certainly see a lot of positives with Shopify.  Unfortunately, Shopify also tried to make setting up Google Analytics “dumb proof.”  In the end, trying to figure out why I couldn’t properly set up a simple, working basic GA tag led me to be “dumbfounded.”   Shopify has a very simple interface where one simply needs to copy and paste their Google Analytics tracking code in order to get started.  

shopify pasted GA code  

However, as soon as you save the file, Shopify takes the Google Analytics code and rewrites it to match their own settings. In particular, they are choosing to _setDomainName to “none”, adding _setAllowLinker (which would indeed be  required for cross domain tracking) and switched to dc.js.  (I am not sure if the folks at Shopify paid attention or not, but to use the DoubleClick cookie, users are supposed to Continue reading Shopify Google Analytics Integration

Where did my traffic go?

A question that is commonly asked of analysts is “WHERE DID MY TRAFFIC GO?!”  (Yes, even occasionally emailed in all caps).  :-)  Indeed, this is a question that I received from a publisher recently, though they were very polite and didn’t use all caps.  For publishers especially, this question is directly tied to their bottom line as advertising revenue is linked directly to pageviews (CPM models etc).  So, I rolled up my sleeves and got ready to do a bit of analysis to see where their traffic went.

The following is simply a recounting of a bit of my process.  The purpose of this post is to share some of my methods with a target audience of beginner to intermediate level analysts.  This is a “how to” oriented post; nothing particularly new or groundbreaking here.  Just some good old fashioned analysis of a common client question.

spoiler-alert (SPOILER ALERT:  They had a tagging problem, not a traffic problem).

Continue reading Where did my traffic go?

Measuring Profit using Google’s Universal Analytics

Leveraging Custom Dimensions and Custom Metrics to gain insights into Merchandise and Profitability.

profit report lead

I spend a bit too much time on Twitter. It’s not a terrible thing, as in addition to Twitter being a forum that truly keeps me informed about what is happening in my industry allowing me to stay on the cutting edge for my clients, it is also a social outlet that helps keep me from completely getting swallowed by work.  :-)   That said, sometimes it is hard to wade one’s way through all of the chatter in order to find the good stuff.  One of the people out there who is almost always tweeting quality things is Kevin Hillstrom, @minethatdata.  It just so happens that yesterday seemed to be a minor “@minethatdata appreciation day” with some other industry peeps giving Kevin a well-deserved thumbs up.

minethatdata appreciation

One of the things that Kevin consistently wants others in the digital measurement industry to think about is merchandise and profit. A simple search on his timeline for July 2013 shows that he mentioned profit no less than 43 times and merchandise at least 28 times.
kh profit tweet

Continue reading Measuring Profit using Google’s Universal Analytics

DudaMobile Google Analytics FIX

DudaMobile + Google Analytics = #Fail 

Sometimes I find myself really bothered by 3rd parties who claim to have Google Analytics integrations not take the care to make sure that is done correctly.  Indeed, one of those companies is DudaMobile.
mobile analytics
image from the DudaMobile site

The strange thing about the DudaMobile situation is that they have some sort of official partnership with Google Analytics.  Indeed, when you search for Google Analytics and DudaMobile, there are lots of articles about this relationship out there.  I didn’t take much time to read into exactly how this partnership works, but my understanding is that it is different from the actual DudaMobile product.

DudaMobile makes it really easy for webmasters to create a mobile site which is hosted by DudaMobile.  That’s a great thing.  I love it.  But in order to get a user to the mobile site, they provide webmasters with some javascript which does a 302 redirect.  Oy vey. 302…  <shutter>  In addition to the SEO problems that are bound to pop up, especially as there is now lots of duplicate content problems for webmasters since they have a 2nd site hosted on a DudaMobile domain.  But I digress… Continue reading DudaMobile Google Analytics FIX

The Importance of Clean and Meaningful Google Analytics data

Ever since returning from Superweek in beautiful Galyateto, Hungary, I’ve been thinking a lot about data and the utility of Google Analytics as a tool.  Yes, I know, I spend a lot of time thinking about those things, but the conference was particularly inspiring in those regards.  Google Analytics is not different than any other digital analytics tool insomuch as it is critical to understand what the values that get reported actually mean and how they get there in the first place.  But that’s not enough.  When we analyze data, we need it to be presented in a meaningful way.  Data visualization is tremendously important in this regards, and I believe that one of the reasons why Google Analytics has such great adoption and market penetration (besides the enticing $0.00 entry price point) is because the UI is crisp, FAST, and easy to use.

One catalyst for this post is a response to this post entitled “Are You Being Misled by Google Analytics?”  While I am about to critique the post, I do want to point out that one of the ideas that Tien Nguyen has (who Chris mentions in his article as the source of this idea)  is indeed insightful.  Namely, that without configuration Google Analytics may not provide as much visibility into traffic sources that one needs.   While I urge you to take a look at the article, I’ll briefly summarize the main idea here. Continue reading The Importance of Clean and Meaningful Google Analytics data

Google Analytics Bounce Rate (actually) Demystified

Bounce Rate in Google Analytics

Every few months of so, I see a (re)tweet pointing to this infographic from KissMetrics.

Here’s a snippet:   Kiss Metrics Bounce Rate Infographic

The thing that frustrates me the most about this infographic is that the definition of Bounce Rate is wrong.   (Well, at least for GA).  Yes, I know that the definition is directly from the Google Analytics Help Center.  But a bounce in Google Analytics is NOT a visit with a single pageview.  A bounce is a visit with a single engagement HIT.  (Justin Cutroni has a great post explaining these hit types and how to understand Google Analytics time calculations based upon undertstanding how these hit types work).  To briefly summarize here, there are 6 types of hits that can be sent to the Google Analytics server.

  • Pageviews (sent via _trackPageview)
  • Events (sent via _trackEvent)
  • Ecommerce Items (sent via _addItem)
  • Ecommerce Transactions (sent via _trackTrans)
  • Social (sent via _trackSocial)
  • User Defined deprecated, though functional (sent via _setVar)

As Justin explains, 5 of these hit types are used in calculating some form of engagement, thereby impacting time on page / time on site calculations as well as bounce rate.  With regards to bounce rate in particular, an additional Pageview, Event which hasn’t been set to non-interactionor Social Media share (that is configured to be tracked in GA) are all things that can impact your bounce rate.
Here is an example of why understanding this technical principal is important when it comes analysis.  In the example below, we see that this client’s Paid Search campaigns have a particularly low bounce rate.

bounce rate analysis 1 Continue reading Google Analytics Bounce Rate (actually) Demystified

How “Unique” are Unique Visitors in Google Analytics

“Unique” Visitors 25%-39% inflated

I’ve been working on implementations with a number of clients who have a need for visitor level tracking in Google Analytics so that they can start using GA to measure things like customer loyalty and (try to) calculate Lifetime Customer Value. I understand that there are a number of data models available to approach these sorts of questions, and that either Custom Variables or Events can be used (using either visitor level vars, or session level vars populated from server-side values).

In general, I like pushing the _utma cookie value back into Google Analytics, as it uncovers every single visit in the API. There are lots of benefits to doing this. Justin Cutroni wrote a nice post about merging GA data with a data warehouse. This is just one (powerful) thing that can be done using this method.   Google Analytics UTMA Cookie What interests me in this post is “how unique are ‘unique’ visitors?” We all know that a unique visitor is nothing more than the value of the _utma cookie’s unique ID. I don’t know about you, but I certainly access websites from multiple browsers from multiple computers. I don’t find it unreasonable for a “person” to indeed have 6 or 7 unique “visitor” values. Start adding in users who clear their cookies and/or Private Browsing and the meaning of “uniques” really begins to degrade.

Luckily, I have access to data where, in addition to capturing _utma values, we’re also capturing obfuscated member ID values. These values are set as a custom variable upon login. This means that a user will maintain their ID whether or not they switch browsers or clear cookies. Here are some of the numbers that I pulled.
Unique Visitors vs Actually Unique Visitors

Number of Unique Members is 61% of "Unique Visitors"

The data set that is decently large and no data sampling has been applied to these numbers. At first glance, it appears that the number of unique logins is 61% of the number of GA’s unique visitors. Two things that stuck out at me were the number of visits in a 28 day period by some of the most active users. The top ten most active users average 11.68 visits per day. Also, line 7 had a large ratio of unique visitors to visits. Was this one user who cleared cookies often? Was this shared login information?

In order to make sure that this made sense among users who visited the site less often, I filtered by logins that had less than 3 visits per day, 2 visits per day, and visits once every 2 days. The numbers were pretty consistent for members who came 3 times a day or less, though users who visited once every 2 days or less saw a higher percentage of unique visitors to login IDs.          

The Bottom Line:
The “unique visitor” metric was never meant to describe the number of “unique people” that visited a site. Admittedly, this terminology can be confusing for the average person. While web analysts have known that ‘unique visitors’ refers to a count of unique cookie values in the browser, I find it quite nice to be able to quantify this in numbers.  Indeed, from the data above, it appears that the number of unique visitors reported is somewhere between 25% – 39% greater than the number of people who visit a site. If you have any additional data, please feel free to share below.  

Google Analytics Updates How Visits are Calculated

Google Analytics Updates How Visits Are Calculated

In a recent blog post, the Google Analytics team made announced that they are changing the way that visits (sessions) are calculated. Interestingly, they said that, “Based on our research, most users will see less than a 1% change.” Unfortunately (imho), they didn’t cover their bases with that statement as the comment section of the above cited blog post shows that lots of people are going pretty crazy about these changes.

**Update** On August 16th, the Google Analytics Team announced that there was a bug in the way visits were recorded after they launched the change. Now, people should be going less crazy as numbers are making a bit more sense. Nevertheless….

Bottom line, this is really a significant change and it seems that people aren’t understanding what is going on. The main things that people seem to be complaining about are:
  • Increase in visits
  • Increase in bounce rate
  • Decreased average time on site
  • Decreased pages per visit

Surprisingly (maybe), I didn’t see a lot of people complaining about a decrease in conversion rate. Hmm… In any case, one comment that I saw rise above the negative spew in the GA blog comment section was by Peter at L3 Analytics. He linked to his blog post which does a nice job discussing some of the implications of the change to the way sessions are calculated. I decided to add to the discussion with this post. Some of what I’ll be saying has already been formulated by Peter. Other things will hopefullly be new , including a number questions I have based on some data in GA that I am still not understanding based upon my current knowledge of the change.

**Side Note** It upsets that people can get so negative in their comments made in forums and blog posts, especially since most of their complaints stem from a lack of understanding. Simple questions in the comment section such as “I don’t understand why 123xyz is happening….” would be nicer to address than “this data is useless, (sarcastic) thanks alot!!”

Understanding the change.

Google Analytics receives hit level data and then calculates all metrics based upon that hit level data. Every time there is a pageview, event, or transaction, a gif request is sent to the Google Analytics servers with information about that hit. Part of that gif request includes session information, and other parts of that gif request include visitor level information. I’m not going to go into the UTM gif requests in depth here, but if you really want to know what is going on check out the RUGA (Really Understanding Google Analytics) series of posts from Cardinal Path. (Kent – it would be great if you could add inner linking between posts on the blog, it’s a great series).

Here is a graphic I quickly put together. (As you can tell, I’m not much of a graphic designer).

The idea that this is trying to illustrate is a visit (session) is made up of hits. A visitor can visit the site multiple times. When a “visitor” has two or more visits, they change from being counted as a “New Visit” to a “Returning Visitor.”

**Side note: We use the term “visitor,” but technically this means “__utma Cookie.” Cookies are browser specific. So if I, Yehoshua Coren, visit in a 5 minute span from 3 different browsers, GA reports that 3 “unique visitors” came to the site. Similarly, if 3 different people in my household visit at different times throughout the day, this is 1 “unique visitor.” Lastly, if I visit a website repeatedly using Private Browsing (Firefox) or Incognito Mode (Chrome), etc, my cookies are cleared on browser close so I’ll be an additional “unique visitor” (with a ‘new visit’) on every subsequent visit.

So how does Google Analytics calculate visitors and visits?

Continue reading Google Analytics Updates How Visits are Calculated