A better way to measure content engagement with Google Analytics

This post is inspired by a conversation that I had with my friend and colleague Simo Ahava at Superweek as well as a recent work request from a well-established Italian publisher. In short, the publisher was quite challenged by the fact that they had an 85% bounce rate, and that their time on site was so low. Their articles tend to the get many hundreds, if not thousands, of Facebook likes, so “how could it be that users were spending so little time on site?!” Their average time on page was around the three minute mark, so how could be that average session duration was significantly lower?

• Challenge 1:  Google Analytics tracks time on page / on site by measuring difference between time stamps of hits.  If the page is a bounce, no time will be recorded.
• Challenge 2:  Even if the page viewed is not the bounce/exit page (and thereby has a time greater than zero), GA doesn’t distinguish between time on page/site if the browser window is in a hidden or visible tab

 

After a lengthy explanation to the client informing them of the way the Google Analytics tracks time on page (and by extension, time on site), they were still stuck without a way to accurately measure content engagement.  First of all, there are a number of different ways to measure engagement besides time on page / site.  Many posts have been written about this and I urge readers to seek those out since time metrics gain too much undue focus as it is.  As things stand, since this publisher’s site was not configured with any event tracking (a scroll tracking module would be great), they were seeing many users come to their site, view one page, and then leave. Unfortunately for them, “out of the box GA” does not provide very good insights into the nature of how users are interacting with their content. “Are they even reading the content?”
Continue reading REAL Time On Page in Google Analytics

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.







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

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:

UserID

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?

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…

 


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.  


 


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

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:  They had a tagging problem, not a traffic problem).

Continue reading Where did my traffic go?

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

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.



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.


Continue reading Measuring Profit using Google’s Universal Analytics

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.

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

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

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:   (Update: to learn about measuring content engagement and accurately calculating time on page in Google Analytics, see this post).

  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 currently 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)

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. Continue reading Google Analytics Bounce Rate (actually) Demystified

“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.   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.


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.