Guide: Facebook Pixel with Google Tag Manager

How to set up the Facebook Pixel using Google Tag Manager.



Recently I was tasked to deploy a Facebook Pixel on an ecommerce site. While deploying tags with Google Tag Manager is normally a relatively straightforward thing to do, I found the documentation around Facebook’s Pixel to be sub-par, a bit confusing, and in need of improvement. So I decided to write this guide both to help out those readers who are perplexed about the how to deploy the pixel, as well as spill some digital ink critiquing the FB Pixel documentation.

Also, I very humbly have found that this blog listed in “top analytics blogs” type articles, oftentimes with a caveat that “Analytics Ninja doesn’t write very often, but when he does….” A blog post from yours truly was well overdue, and I wouldn’t mind boosting up this site’s SEO as a result (Google being all keen on fresh content and such). Feel free to link to this post with an anchor-text rich “do follow” text. Maybe something like [Google Tag Manager Consultant]. You’ll be helping me cover the costs of raising 5 kids. ūüôā

So, here we go….

Step 1 – Create a Facebook Pixel


Create a Facebook Pixel by clicking the button

Once you click the create pixel button, you’ll need to give your pixel a name and agree to Facebook’s Pixel Terms. As an informed reader, I went through the TOS that they have on their pixel pretty in depth and I still don’t really fully understand what’s going on in terms of the data they collect. As with most things in the modern digital advertising world, you basically accept that Facebook is probably going to track as much as they can via their pixel and hoard all of that data so that they can build better advertising algorithms for to make as much money for the company as possible. Facebook, like Google, is in many ways primarily a Big Data company whose largest strategic business asset is data. Indeed, the Facebook data set is really second to none because of the nature of the personal information that is given to them freely by almost 2 BILLION people.

One additional point about the Facebook Pixel which makes me raise my eyebrows is the following quote from their Help Center. “To improve your ads delivery, how Facebook measures the results of your ads, and in an effort to enhance the relevancy and usefulness of ads, we’re enhancing the Facebook pixel. The Facebook pixel will start sending more contextual information from your website to better understand and categorize the actions that people take on your site to optimize for ads delivery.
The additional information sent through pixel will include actions on your page, like ‚Äúadd to cart‚ÄĚ or ‚Äúpurchase‚ÄĚ clicks, and will also include information from your page’s structure to better understand context associated with these actions.”


Facebook then makes you dig into their developer docs to get an understanding of the impact of that statement. Because marketers are
A). Going to dig into developer docs and understand what they mean and
B). stop using the Facebook pixel because it is collecting too much information or
C). update their code to opt out of the data collection. PLEASE somebody remind me to update this post with an explanation of what this *really* means on a technical level once we can start inspecting hits to the FB servers. My guess is that FB is going to collect a HUUUGE amount of information about every click on the site. Kinda like Heap Analytics auto-tracking without providing the end-user access to the data.

To quote my own tweet:


And to quote Facebook:


Click data and page metadata? And you need to add a “set” command to your code if you don’t want to provide that info? Wow! To be honest, I’d love to be a part of that analytics team that has access to that much data just gobbled up from so many websites. If you’re listening, Facebook, for $220,000/yr I’d consider joining your team.

But I digress…

Step 2 – Install Your Pixel Code



As a part of the process for installing the pixel, you can give Facebook access to GTM via API and they’ll create a tag and trigger for you. Let’s “not” do that, and install the tag manually so that we have more control over configuration. Continue reading Guide: Facebook Pixel with Google Tag Manager

Tracking Brighcove Videos with Gooogle Analytics

This post is authored by David Vallejo.

Brighcove is an¬†online video platform that allows to embed onDemand videos on your websites. It seems there’re out of there some people looking for a way to track it, so we’re going to learn how to track those videos within Google Tag Manager.

In order to be able to track our videos from BrightCove we need to have some things in mind:
  • API needs to be enabled within BrightCove Interface.
  • Embeds needs to have¬†includeAPI and¬†templateLoadHandler params in place.

First Step: Enabling the JS API for our videos


This needs to be done within BrightCove Site for all the different players that we may have, follow these steps:
  1. Go to your Video Cloud Studio Publishing Module and then select your player
  2. Click on the Setting link
  3. Under Web Setting option within the Global tab, select the option “Enable ActionScript/JavaScript APIs’
brightcove_tracking_1

Second Step: GTM Configuration

For being able to track BrightCove videos, we’re going to use 1 Custom HTML tag, 1 Trigger and 1 Variable. First let’s create the Variable, this one will allow us to know when a BrightCove Video is present on a page, to we just fire the tracking script when it’s needed. We don’t want to inject the tracking code were is not going to be used: brightcove_tracking_2   Now, we’re going to create the Trigger that is going to fire our tracking code. Just to be safe, we’re going to fire it on DomReady and when our previusly created Variable equals to “yes”. brightcove_tracking_3
Ok, now let’s add the tracking tag (you’ll be able to copy the js code from the post bottom):
brightcove_tracking_4
  We’re done. Remember to test this code in preview before going live. This script will track the following events:
  1. Play
  2. Pause
  3. Complete
  4. Progress (25% steps, it can be personalized within the code)
  5. Seek
We’ve used an agnostic dataLayer push, with a lot of extra info about the video in the case you need it, like the related thumbnail, the video length, the video coded, the video name, the publisher info, check the following screenshot for a guide of all info you may use for your custom dimensions:
brightcove_tracking_5     Watch Full Movie Online Streaming Online and Download

Google Analytics Custom Metrics & Calculated Metrics

 

Metrics, Metrics, Metrics, oh my!!


With the recent (very welcome) release of calculated metrics in Google Analytics, I thought that the time was ripe to write a post about custom metrics in GA. ¬†Towards the end of this post you will see why I believe that custom metrics are quite a germane topic to discuss in relationship with calculated metrics. ¬†I will also touch on how the launch of calculated metrics is another big value prop when it comes to GA Premium (even though they aren’t a GAP only feature).

I believe that custom metrics are painfully underused in Google Analytics implementations. ¬†Believe it or not, my browser console is¬†oftentimes open when I’m browsing the web (you should believe it). ¬† I do a lot of spying investigating of websites’ implementations by looking at the data that they send to GA , and I do not see custom metrics¬†being deployed almost at all.


Dimensions and Metrics

Before we dive into custom metrics and calculated metrics, let’s take a step back and define “metrics”. ¬†There are a number of different articles which define dimensions and metrics. ¬†I¬†have chosen to¬†quote Paul Koks¬†and then go on to discuss in my own words (recognizing that my own understanding of dimensions and metrics comes from reading those who precede me in the industry). ¬†In Paul’s words:
  • A dimension is a characteristic of an object that can be given different values ‚ÄĒ> a dimension describes data
  • A metric is an individual element of a dimension which can be measured as a sum or ratio ‚ÄĒ>¬†a metric measures data
So, there we have it. ¬†Dimensions describe data and metrics measure data. ¬†Dimensions will make up the rows in a table report whereas metrics will populate the columns. ¬†Metrics¬†are the data. ¬†Metrics increment, they¬†count things.¬†¬†Metrics will invariably be a number, be it an integer, a ratio, a percentage etc… ¬†As of today (if I counted correctly), there are 189 metrics available in the Google Analytics API.



Custom Metrics & Event Tracking (Measuring User Interactions)


So here is where metrics begin to get interesting. ¬†Or, as my friend Jacques Warren once said to me, custom metrics are “basically where reports really start making sense to a business.” ¬†Standard metrics in Google Analytics are meaningless by themselves. ¬†This is mostly¬†true of custom metrics as well. ¬†Remember, they are just counters, so when they are stand-alone they lack any context. ¬†Meaning must be derived through segmentation, and that means drilling down into the dimensions which describe¬†your data. ¬†While custom metrics won’t solve the context problem for you completely, they do so partially, which is why I love them so much.

Monthly Active Users


Let’s start by taking a look at how some standard¬†metrics in Google Analytics are used to measure¬†user¬†behavior. ¬†I’m going to start with Event Tracking, using an ecommerce site as my example. ¬†Event tracking is used to track interactions with a website or app. ¬†Those interactions are usually some form of click, form submit, tap (for mobile), etc which translate into discreet actions that a user is taking. ¬†¬†


event action on product page

In the above example, my Event Category (a dimension) is “Product Page”. ¬†It serves as a descriptor for the Event Action¬†(which also a dimension), which describes the¬†metric (total events). ¬†Notice how total events is simply a number. ¬†It counts how many times an action in rows 1-6 happened.

Now let’s say I want to get some more context about the¬†Add to Basket¬†event is being tracked. ¬†One way that I can get at this information is via Custom Reports. ¬†With a custom report, I can drill down into different dimensions as a way of getting more granular context in my data.

google analytics custom report

custom report drilldown




Similarly, by using the Pivot feature in reports will allow you to provide additional context to your metrics.


pivoted report



But in all of these examples, the metric itself, “total events”,¬†still lacks context. ¬†In order to know “total events” of what, I need to refer to my dimensions. ¬†

Custom Metrics in Google Analytics are unique in the fact that they are “named values”; they can also¬†describe the¬†interaction that they are measuring. ¬†Just like custom dimensions, the name of the custom¬†metric is set in the GA Admin section. ¬†

custom metrics on hit level


Notice how each of the actions that the user can take on the product page is now being tracked as a metric.  This is really powerful as now I can build a custom report and pair these custom metrics with almost any dimension (limitations to be discussed below).



awesome custom report
Translation from Google Translate. ¬†I don’t¬†speak French

Another example is from a site that has has restaurant reviews.  On an individual restaurant details the user is able to view a menu, check hours, add a calendar reminder about their reservation, or even order food online.


place related actions

As you can see, custom metrics make the data model in Google Analytics¬†much more flexible. ¬†As far as event tracking goes, I highly recommend taking¬†10 or 15 most meaningful events (user interactions) and tracking them with a custom metric as well. ¬†In my humble opinion, custom metrics can be seen as “event tracking on steroids” (almost the title of this post). ¬†Context gives meaning to data. ¬†These data points, since they are named, are now much more meaningful than just being a “total event”. ¬†While the pivot function in certain GA reports can get you kind of close to what you see in the table above, it is by far not a replacement for have a native metric in GA. ¬†


Tracking $$$



In addition to super charging your tracking of user interaction with your site, I find custom metrics to be wonderful when it comes to tracking financial data.  My favorite custom metric in this regards is gross profit, though with some hard work tracking net profit is possible in Google Analytics as well (though that would like require some calculated metrics also).
product discounts


In almost all of our ecommerce implementations, we try to track the following metrics when available.
  • Original Price / List Price
    • This is $119.99 for the item above
  • Displayed Price
    • This is $109.00 for the item above
  • Product Level Discount
    • This is¬†product level savings, $10.99
  • Order Coupon Value per Product
    • If a 15% coupon is applied to the whole cart, that 15% is applied equally to every product. ¬†For our product above that would $16.35
  • Product Revenue
    • This is a standard metric¬†(not custom), that represents the actual value of transacted revenue per product
  • Cost of Goods Sold
    • FWIW, I bet a bunch of folks are making a killing on that Lego set.
  • Gross Profit
    • Product Revenue minus COGS


The results are totally terrific. ¬†ūüôā

 money in google analytics

The above image shows the breakdown of sales by brand (including how much was discounted at point of sale by brand).   With the above custom metrics in place, I can quite clearly see the impact of discounting / sales / promotions etc on any product level dimension or on session and user level dimensions.  In other words, I could run reports to see how much money was being left on the table by channel, by geography, by customer type, by gender, by age group.  The list goes on and on.

Some Technical Points



WARNING: ¬†If you don’t like¬†reading about technical aspects of Google Analytics and implementations, just skip to the next section.

For reasons I understand, but don’t particularly like, custom metrics¬†are “scoped” (as are custom dimensions). ¬†In other words, they need to be set to a particular data scope in¬†the Google Analytics¬†admin in order to be processed into reports. ¬†Custom metrics support two scopes, Hit and Product. ¬†Metrics will be processed and then reported on as either integers, currency, or time (measured in seconds).

scope and formatting type

When hit level data is sent to Google Analytics, the parameter / value combination matches the following format: ¬†cmXX={{value}} where XX is the index of the custom metric. ¬†The actual name of the metric that you see in reports is configured in the admin, just as you would configure a custom dimension (as in the image earlier in this post). ¬†So let’s say I wanted to¬†track an¬†Add to Wishlist interaction with a custom metric, I would use the following code:
ga(‘send’, ‘event’, ‘Product Page’, ‘Add to Wishlist’, ‘Expensive Stars Wars Lego Set’, ¬†{‘metric2’: 1});


data endpoint for hit level custom metric

In Google Tag Manager, you can configure the event set a custom metric index and value directly in the event tag.



custom metrics in GTM


Unfortunately, GTM is using a “set” command for a custom metric, which means that the value will persist for the same tracking object. ¬†If you’re using a named tracker, you’re in trouble because all future hits will have that value set. ¬†Even though most people default to not using a named tracker,¬†this still creates¬†problem for custom metrics that are sent with pageviews, as page timing hits will use the same tracking object even when no default tracker name is chosen. ¬†It can get really nasty with single page sites (think angular.js) ¬†ūüôĀ ¬†

custom metrics set in GTM

Hashtag #booooo.  (There are workarounds using hitcallbacks, but I still #boo the need to resort to those implementation acrobatics, when pure hit level manipulation in GTM should be available).


Product scoped custom metrics are coded as part of the¬†product actionFieldObject. ¬†This makes perfect¬†sense as the metric is meant to¬†track a particular value associated with a particular action. ¬†For example, the value of the product added or removed from cart¬†(a good article if you’re interested in a practical walkthrough of how product level custom metrics are implemented). ¬†Or¬†any of the¬†discount / profit metrics that I mentioned above for the “transaction” action (say that five times fast). ¬†Since multiple products can be associated with a particular hit, you can pass through the product level discount for each product on a “purchase” action that uses a pageview hit as the data transport mechanism.


The data is sent to GA as in a &prXXcmXX format, which stands for product {{number}} and cm {{number}}


product scoped custom metrics data sent


Limitations of “product scope”



One particular thorn in my side when it comes implementing custom metrics for Enhanced Ecommerce implementations is the inability to track interactions with products outside of the predefined “dictionary” that Google supports when¬†it comes to interactions with products. ¬†Those actions are¬†(I’m quoting):

  • click
    • A click on a product or product link for one or more products.
  • detail
    • A view of product details.
  • add
    • Adding one or more products to a shopping cart.
  • remove
    • Remove one or more products from a shopping cart.
  • checkout
    • Initiating the checkout process for one or more products.
  • checkout_option
    • Sending the option value for a given checkout step.
  • purchase
    • The sale of one or more products.
  • refund
    • The refund of one or more products.
  • promo_click
    • A click on an internal promotion.


That means that Adding to Wish List, Adding to Registry, Viewing Product Video, Clicking on Cross-sell, Social Sharing, Writing a Review, Asking A Question, etc etc cannot be tracked on a product level in the same way that the other items are tracked. As of this post, changing the actionFieldObject to some use other name (so that you don’t inflate “adds” or “clicks”) and setting a product level custom metric to capture the interaction with the product will simply fail. ¬†This means that in order to track information about product interactions that are not part of the Enhanced Ecommerce dictionary, you’ll need to use hit level tracking (events). ¬†As such, collecting important data such as Wish List Adds per product or per brand request will require the use of “hit level” custom dimensions, not product level. ¬†


Calculated Metrics



So far we’ve seen how custom metrics are super useful, that they add a particular flexibility to the data you collect in GA (pivoted data galore), and how they are indeed critical for tracking the metrics that really¬†matter (profit!). ¬†The reason that the release of calculated metrics was a catalyst for me¬†to write about custom metrics is because in order create¬†calculated metrics, you need to use metrics. ¬†“HUH?” ¬†Yes, that’s correct. ¬†I did just say that. ¬†Let me illustrate what I mean with a few¬†examples.

My first example is from¬†my friend Peter O’Neill’s post about using calculated metrics to measure conversion funnel completion¬†rates. ¬†

Peter O'Neill is awesome. You should hire him.

Image ripped without written permission from Peter



To see the percentage of sessions where users viewed a product out of the total sessions where users viewed the ecommerce portion of the site (i.e. the store as opposed to the blog), you need two metrics, sessions with views of store and sessions with views of product page.  The simplest way to get these two metrics is to create goals for each of these steps (something that you should be doing in any case).  You then divide Goal X by Goal Y. Pretty straightforward, relatively benign. Create goals to get measures of number of sessions where a core action was taken

My second example is from my friend Charles Farina, who has a nice article with 25 calculated metric examples which includes some basic “how-to” in terms of setup. ¬†The example is “video completion rate.” He mentions, as an aside, that in order to create this calculated metric, you’ll need custom metrics. ¬†Why are custom metrics needed, you ask? ¬†Well, let’s look at how I would normally calculate video completion rate.

video completion rate
Note the numbers here. ¬†The number of sessions where the video was completely viewed was 20,451 times. ¬†That metric, 20,451, in Google Analytics is called “unique events”. ¬†It is¬†not “number of video completions”. ¬†A calculated metric in GA cannot divide the value in row 7 by a value in row 1. ¬†In can¬†only calculate values (add, subtract, multiply, divide) between columns. I also need to stress that custom metrics, since they can be used for a wide variety of purposes (including creating custom visit scoring!!), will not have any “uniques” applied to them,¬†pageviews, content views, or events do. ¬†This is a pretty big limitation, though I don’t see it see it changing any time soon.

In any case, the ability to calculate metrics in Google Analytics has made the platform much more powerful, but a huge amount of that power now lies in properly implementing custom metrics.


Custom Metrics, Calculated Metrics, and Google Analytics Premium



The launch of¬†calculated metrics creates another major¬†differentiation between Google Analytics Standard and Google Analytics Premium. ¬†For a long time, I have believed that the number of custom dimensions available for GAP (200!) vs. GA Standard (20) was one of the most¬†core selling points for larger companies who would be considering Premium. ¬†Then I began to fall in love with custom metrics and I’ve added that to the list too (again, 200 vs. 20). ¬†With calculated metrics, the GA Premium folks get a healthy 50 metrics to get them going, while the rest of world must settle with a meager¬†5. ¬†Yup, just 5. ¬†:-/


Closing Thoughts



Over the past 2 years or so, I have become a bigger and bigger fan of custom metrics. The release of calculated metrics in GA makes the possibilities of what the tool can do only more awesome. To be clear, the release of calculated metrics doesn’t change the final output that analysts such as myself or my colleagues would be providing to our clients. And I don’t expect tools like Excel to drop significantly in their utility just because GA launched calculated metrics. But I totally adore the direction Google Analytics is going in allowing for better and better analysis capabilities natively in the tool. To whatever extent GA ends up developing into a one ring to rule them all central BI hub, well… I, for one, welcome our analytics overlords.

Using Google Analytics to Grow Your Business

The following post is an email that I wrote for the Traffic1M course presented by SumoMe.com. ¬†It is geared to folks who are just getting started with Google Analytics, and online marketing in general. ¬†A big thanks to Noah Kagan and team for the opportunity to participate and provide some content to a wide readership. ¬†If you are a reader of this blog and don’t know Noah, I highly recommend checking out okdork.com and sumome.com; there’s great stuff there.


Introduction to Google Analytics

If your website is like millions of other websites, then you have Google Analytics installed.  

When I say “millions”, I’m referring to data from from BuiltWith.com that estimates almost 30 million (!) websites are using GA. ¬†

I also note that Google Analytics’ accounts are numbered sequentially, so as of the writing of this email more than 67 million accounts have been created. ¬†Not all are in use, so the number of active accounts is probably somewhere in between.

New Google Analytics Account


So you have GA on your site, and tons of data is being collected.  What now?  How can you use that data to help grow your business?


Why Use Google Analytics?



This Traffic1M course is all about getting that first million visitors to your site. ¬†Put simply, Google Analytics is the most popular tool you can use to measure those visitors. ¬†You shouldn’t use a tool because it is popular, though. ¬†

Most folks who choose GA do so because

 
  • It is really powerful (“enterprise-class”) software. ¬†
  • The Standard version is free.
  • Google’s tremendous cloud infrastructure allows them to rip through very large data sets rather quickly.
  • It has a nice, clean User Interface.
  • They want to use data to make informed business decisions.

That last point is the most important one. ¬†Let’s not be naive; data on it’s own is meaningless. ¬†But when used properly, data will help you see what’s working well, what’s not working, and help you make decisions about “what to do next.”



Where do I start?


One of the biggest challenges I have seen for businesses / website owners is that they don’t know where to begin when it comes to analytics. ¬†People find themselves looking at an imposing mountain of data, and don’t know how to begin climbing.

Continue reading Using Google Analytics to Grow Your Business

Smooth Google Analytics Migrations using Google Tag Manager

This post is authored by David Vallejo.  At the current juncture, this blog is not configured to support for multiple authors.   I hope to remedy that in the future.  ~~Yehoshua

 

Since Google Tag Manager was released, here at¬†Analytics Ninja we have faced a number of¬†problems when migrating a “hard-coded” Google Analytics implementation.

The most common problem relates¬†to actual moment of deployment, when the old Google Analytics code has to be removed from the site. This is not much of a¬†problem if we¬†have access to the site and we¬†can directly remove the hard-coded snippet at the same time you publish your new container. ¬†But let’s be real, this is not the usual scenario. We usually rely on some other company or the client’s IT department to try to synchronize the deployment. This leaves us in a challenging¬†situation because if they remove the code and we don’t¬†publish the¬†container right away, some data may get lost. ¬†Or, on the other hand, if they don’t remove the code and we publish the¬†container the hits will be sent twice. ¬†Or even worse … if we are migrating¬†from Classic to Universal¬†at the same time we’re moving to Google Tag Manager¬†we could end even messing up the sessions/users/bounce rates and any other metrics/dimensions.

This problem is aggravated¬†if we are¬†working on a multi-domain implementation where each of the domains is being ran by different business groups…¬†if it was hard to synchronize with one team,¬†just imagine¬†if there are¬†2 or more business groups¬†in¬†different time zones and having everyone required to make changes¬†at the same time …

Even if we are able to get everyone involved in the migration, there will always be some little time gap between the code removal and the container publication.

One solution is to¬†use¬†a piece of code that will allow us to block all our new tags if the old code is still on the site. ¬†We will just need to schedule a date for the migration and using a simple macro and rule we’ll be just firing our new tags on the pages that have the old code already removed. This way even if there is a long¬†timeframe to get everything sorted out, the GA data won’t be affected at all.

For this to work, we¬†need a macro to get the current Google Analytics status on the page. ¬†For this we’ll configure in our Macro the UA property names (we’re using an array because we may have a dual tracking implementation, or the page may have a third party GA tracking and we don’t want to mess up with them). Then we’ll¬†loop through all the trackers available to get their configured UA account, and if it matches our properties arrays we’ll return true. ¬† See following flow diagram to see what’s the macro’s logic:

Google Tag Manager Migration Macro Flow Chart

Google Tag Manager Migration Macro Flow Chart

The next step we need to take is to setup a new firing rule, that will allow us to block our tags if the old trackers are still on the pages.

Captura de pantalla 2015-03-29 a las 1.51.46

Macro Code

We then add a blocking rule to our Google Tag Manager tags so they are not executed if any previous tracker initialized on the page.

We’ll need to keep¬†one more thing in mind. As GA/UA code is asyncronous it may happen that Google Tag Manager tags get fired before the old code get executed, so we’ll need to delay our current tags while we’re migrating . This can be done setting the firing tag to DomReady event ( gtm.dom ), or even better for Window Load ( gtm.load ). This is not the best way to run an analytics implementation as we normally want our analytics tag to get fired ASAP, but we’ll be changing our tags firing rule to gtm.js/All Pages when we check the old code is already gone from the pages.

Let us know what you think about this implementation method in the comments section below.

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). Continue reading Advanced Remarketing with Google Analytics & Google Tag Manager

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). ¬†ūüôā ¬†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‚Äôs 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. ¬†ūüôā ¬† That said, sometimes it is hard to wade one‚Äôs 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

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

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.