Category Archives: Web Analytics

Google Analytics, 4 of 4: Summary

Over the past few weeks, I’ve explored how to incorporate Google Analytics into the the planning, execution, and continuous improvement of web projects. While I’m still concerned about the extent to which web traffic analysis and visitor tracking/identification invades user privacy, I feel better informed in my opinions after going through the process of adding analytics to a single site for myself, from technical implementation to analysis to dissemination of findings.

Something that does give me some comfort is better understanding all the ways in which users can “opt-out” of participating in Google Analytics.  While troubleshooting the addition of Google Analytics to my site, I was able to confirm that not only were the browser extensions I use effectively (to my own eyes) blocking the tracking and sending of data to Google Analytics, but that a content inspection policy in place for my home network was also filtering this activity.

While I may not use it for purely personal projects, I can see myself using Google Analytics in the future for my employers and clients due to its ease of use and potential insight it delivers. I’d like to work with it more on a site with heavier traffic, since web analytics seems more appropriate for data on a larger scale (and with less “iffy” sampling methods) than what’s in place for this project. In the future, I would also really like to work on projects where Google Analytics are employed as part of a larger investigation into a website’s usability. I think it would be interesting to compare findings from Google Analytics with what’s learned using other user testing methodologies.

Since a proper Content Experiment wasn’t carried out on my site and I received so few visitors, I wasn’t able to pull out any clear indications of what to do in order to improve the site’s goal conversion rate.  The goal I chose to track progress towards required users to submit a recipe to the website’s recipe repository.  I’ve been thinking about this goal and something I realize about it is that it does require some significant effort and thought from the visitor. I kept this in mind while analyzing the data and it helped to temper my expectations. In cases like this where users are asked to complete a significant task, I think even more effort should go into understanding user motivation, so an appropriate reward can be offered.

Google Analytics, 3 of 4: Content Experiments

My third post is about how I completely misunderstood Google Analytics Content Experiments and learned to still be okay with myself. After very briefly / braindeadly looking over explanations about what Content Experiments are and reading through the vue-analytics documentation, I with way too much certainty assumed my incomplete set of marching orders and set to marching. I then wily-nily started creating new elements and alternate pages, adjusting my GA event trackers, all while imagining all the sweet insights I was about to rake in based on the experiment I’d set up for visitors to unwittingly walk into. But I did it all wrong. (It’s okay, I forgive myself.)

What even are “Content Experiments”?

Content Experiments are similar to A/B testing. A/B testing, also known as split testing or bucket testing, is a form of statistical hypothesis testing comparing two versions of a single variable. Content Experiments use a similar model, A/B/N. In this model, you aren’t dealing with just two versions of the same page, you are testing up to 10 full versions of a page, each with a separate URL.

The Google Analytics console offers a wizard of sorts to set up your page variants and generate code which must be added to your website in order to enforce the direction of traffic to your variant pages based on your specific configuration. Heh, this may sound simple enough but I learned it all too late.

Screenshot of the Content Experiments wizard

Screenshot of the Content Experiments wizard

Experiment Objective

This tale starts off well. I think my planning steps were pretty solid even if my implementation was flawed. Good intentions and all, fwiw. Since my overall objective for the site is to get users to actually contribute a recipe to the repository, the Content Experiment objective I planned was to get users to click on a “Contribute” button, fill a form, and submit it.

The variable I decided to isolate was the location and color of the “contribute” button. Once able to divide traffic into visitors who accessed the submission form via one button or the other, I figured I’d have (what I now realize are extremely uninteresting and not useful ) metrics on whether blue or coral button pushers are more likely to submit a recipe.

Screenshot of Karl Sayagin home page showing different buttons

What color button pusher are you?

I set up my event trackers and goals, thinking that what I was orchestrating was a Content Experiment™.

I did whaaat?!

You may by now note the mistake I made. My set up involved two different components on a single page, both of which take you to the same submission form. Er, actually, each button takes you to a different submission page which looks exactly like the other (how incredibly boring, right?).

It would have been more appropriate for me to create two different submission forms, add them as variants within the google analytics console, and add the necessary code so that visitors would be delivered to one or the other at random.  Something I could try would be to give submitters the option of crediting themselves when they add a recipe, since some people may prefer not being anonymous.

The cold hard truth

By the time I had realized my mistake, it was too late to set Content Experiments up properly for the purposes of this assignment. Despite not being properly set up as a true Content Experiment, since I’d added event trackers to the two different “contribute” buttons linking to different pages, I was still able to gain some insight into the performance of these two different buttons.  Based on this test, for each button there were two users who clicked on them and ultimately submitted a recipe (two-to-two).

Screenshot of GA Dashboard showing success of different contribute buttons

The button in the top-right corresponds to ‘contribute-1’ and the main button below the quote corresponds to ‘contribute-2’


Google Analytics, 2 of 4: Goals and Events

Defining my goal

Something that drove creating the Carl Sagan, “Karl Sayagain”, tribute site was a desire to collect recipes in the same way one might collect stories or jokes, meaning from other people, with prompting. For a previous version of this project, I requested recipes from people and added them all manually. This batch of recipes serves as the “starter” for the current project, which finally includes a form that can accept contributions and make them automatically available to display.

Setting this goal up on my site

I defined “submit a recipe” as a goal in Google Analytics and since I also hope that visitors refresh the page to see recipes others have submitted (and potentially their own), I defined another goal of “refreshing the page”.

The vue-analytics plugin makes it breezy to install google tracking for general tracking but a little bit more work is required for event tracking. Fortunately, the documentation covers how to do this pretty well and I didn’t have much trouble getting this set up.

There are four parameters which can be associated with event “hits”: eventCategory, eventAction, eventLabel, and eventValue. For the purposes of tracking events leading up to the goal of “submit recipe” I used just the first two parameters, eventCategory and eventAction.

I also added events to the two “Contribute” buttons on the home page. For this event I used the third parameter, eventLabel, to distinguish traffic to one or the other in the collected data. As always, naming things is hard and were I to go back I’m sure I would take a more considered approach to this task.

Defining this goal in the Google Analytics console

Back in Google Analytics, I created four goals with the type “event”:

1. Get new recipe (refresh page)
2. Submit recipe
3 Use main button to get to contribute form
4. Use button in nav bar to get to contribute form

How the Funnel worked out

According to Google Analytics, my conversion rate for the goal of “submit recipe” is 22.58%! This isn’t too bad, I think, but it is a little worse for my knowing which of those numbers are my own (I’ve learned my lesson here). A few (well, 3) people completed the goal of submitting the recipe, but since the path to the contribute forms is just one click away, the “funnel” isn’t especially exciting.

Confession: Only three of those goal completions are legit

Google Analytics, 1 of 4: Installation and Initial Findings

This is the first in a series of posts about my experiences using Google Analytics to track traffic. The website I added tracking code to is a small Vue.js web application built as a tribute to Carl Sagan.


On recommendation, I used the vue-analytics library to add Google Analytics tracking to my site. The plugin automatically can automatically load the Google Analytic script and when passed your VueRouter instance, can even perform page tracking without having to set this up more manually.
vue analytics configuration

Main.js: Code to import the vue-analytics plugin and load/configure Google Analytics tracking scripts

After acquiring and adding a Google Analytics ID to the the vue-analytics configuration for my site, I needed to ensure it was properly installed and sending data to Google. A browser extension I use called Ghostery gave me my first confirmation that Google Analytics was working. Vue-analytics also has a great debug mode which earned its salt during installation and later troubleshooting.

Expectations about Demographics

After adding Google Analytics tracking to my website, my plan was to share a link to my site with my family and friends, as well as with the greater Seattle University Web Development Certificate Slack channel. I expected visitors to primarily visit from in from Washington, where I currently live, and North Carolina, where I grew up. I also expected a wide age range from early twenties to late-sixties and for most of my visitors to be female.

Google Analytics Console

Google Analytics is a freemium web service offered by Google which used to track much of the traffic on the web (wasn’t able to locate any official figures but estimates by market researchers put it way up there). The capabilities it offers are extensive and I’ve barely scratched the surface. And it’s all for “free”!
The journey of getting up and running with Google Analytics involves navigating the creation of dashboards and views, as well as picking up terminology and concepts standard across the web analytics industry along some specific to Google’s implementation of their services. On initial property creation, a default, unfiltered view with “all web site data” and a number of dashboards are available by default, which can give you a head start analyzing your data even without further customization.

Initial Findings

The collection of a few days worth of data confirmed my hypothesis that the majority of traffic to my site would come from Washington and North Carolina. While I was surprised that all of the visitors to my site were male, I also don’t believe this to be true.
100% of site visitors were classified as male

100% of site visitors were classified as male

 That’s it for now — more to come!