What Is Google Analytics?
In essence, Google Analytics is a tool that allows us to aggregate, filter, and visualize data to understand how users interact with a website. Instead of relying solely on intuition and experience to influence business outcomes, we can access a data trail that people leave behind on the websites they use. This data trail gives businesses valuable insights into user behavior, interests, and more to take action to improve their product or service.
Why Use It?
Google Analytics is a crucial tool for any contemporary business with transaction data and/or have an online presence (which nearly all businesses do). Google Analytics helps businesses measure the effectiveness of marketing strategies, the quality of online content, the volume of “hits”, user experience, and website functionality, to name a few. Often times, companies identify and track KPIs (Key Performance Indicators) to monitor growth and conversion rates. In the data-rich 21st century, the success of a business has become a rigorous exercise in data collection, analysis, and testing to meet company goals.
How Does It Work?
- What device and browser you’re using.
- If you are new to the website or have visited before.
- What you clicked on and how many times you clicked on it.
- What you downloaded on the website.
- How you entered the website.
- How much time you spent on the website (length of session).
- What you put in your cart, what you abandoned, and what you purchased.
Accounts — Properties — Views
The structure to use Google Analytics is defined through a 4-tiered hierarchy: organizations, accounts, properties, and views. This hierarchy is explained through the diagram below.
Organization — The organization is our COMPANY. Here, we can control all of the organization’s Google product accounts, manage who uses Google products, and what their permissions are.
Account — An account is where we can ACCESS the Google Analytics product. We select an account to access Google Analytics and determine the properties we want to track. We can associate one account with only one property, or we can use one account to manage multiple properties, whatever makes the most sense to organize our workflow.
Property — A property is a LOCATION where we are pulling data from. This can be a digital location — like a website or app, or it can be a physical location — like a cash register or card reader. When we add a new property, Google Analytics automatically creates the tracking code (cookies) used to collect data from that property. A unique “ID” in the tracking code allows Google Analytics to identify the data coming from that property. All we have to do from there is incorporate that tracking code into our website/app.
View — A view is how we SEE the data. In a view, we can parse, filter, and organize the data from a property. We can assign users access to a view to control which people can see certain data and which people cannot. We often have many views within a property.
Getting Set Up
First, we want to create a Google Analytics account. Lucky for us, we can do this for free! Google will set us up with a “demo” account with real data from the Google Merchandise Store. This is a great experimental environment to give us a look at real data and let us try some of the features that Google Analytics has to offer. To set this up, I recommend following this article found at support.google.com.
Once you are all set up, you can start to create new views to analyze and visualize your data. First, to make a new view, click on the “Admin” tab. Then, select the “Account” and the “Property” that you want to add the view to. Finally, in the “View” column, click on “Create new view”. See the step-by-step screenshots below as an example.
Views are a lens through which to see our data. We often filter our data to see what we want to see. However, it’s important to understand that filtering data permanently includes, excludes, or alters incoming data that view. We should always have an unfiltered, raw view of all our data so we have access to our full data set at all times.
Note that once we delete a view, all data associated with the view will be gone, permanently. So we should always be very thoughtful in creating new views, and be very cautious before deleting any views.
Finally, note that once we create a view, the dashboard for that view will show data from the creation date of the view, and onwards. Views do not show data before its creation date.
We will want to create at least 3 essential views for each property in Google Analytics to see our data:
- All Website Data — This view is created by default in Google Analytics and we shouldn’t mess with it. This view shows us ALL of the raw data that is coming in from our website/app. We can think of this view as a BACKUP of our data.
- Test View — If we want to apply new filters or make changes, we can use this view first to TEST it out. By using the “Test View”, we can see how new filters impact our data collection without risking the loss of data. If we are confident about the results in the “Test View”, then we can apply these changes to our “Master View”.
- Master View — The master view is the primary view that we use to transform the data into critical insights. Here, data is processed by the Google Analytics filters that we experimented with previously on the “Test View”. Remember — if for some reason something goes wrong, we can always rely on our backup view: “All Website Data”.
Example: Google Merchandise Store
Once we have our views set up, we need to ask questions of the data. Asking questions allows us to navigate through the data with intent and purpose, in order to parse, filter, and visualize the data that we have collected.
For example, let’s say that we are working on the data analytics team at the Google Merchandise Store. We have been tasked to find out if there is a way to increase merch purchases on the website. So we might ask the data these three questions:
“How many users are buying merchandise on the website per week?
How many users put merch in their cart, but don’t buy them?
At what point do users decide not to buy them?”
To address these questions, we can use a feature in Google Analytics called a “Goal Flow Report”. Using this report, we can understand the point at which users abandon the items in their shopping cart before checking out. From here, we can use dynamic remarketing to get more customer conversions (purchases) on shoes put into the shopping cart.
On the far left-hand side, we can see all of the different sources that show how users came to the Google Merchandise Store over the past week. From left-to-right, the checkout process is broken down into five steps: Shopping Cart, Billing + Shipping, Payment, Review Order, and Purchase.
Graphically, Google Analytics makes it easy for us to identify where we are losing most of our purchases. There is a huge drop in volume from the “Shopping Cart” to the “Billing +Shipping” step. From 1,600 sessions, we drop to under 300 sessions. The Google Merchandise Store lost over 80% of customers at this first step!
As customers take more steps towards the purchase, we can see that we gradually lose more and more potential buyers. At the final step of the diagram, we can see that only 30 people ended up purchasing something from the store. The store lost about 98% of its customers through the purchasing process! Think about that: for every 100 people that put things in their shopping cart, only 2 of those people end up buying. Wow!
So from these findings, we might recommend a tweak to the Google Merchandise Store user interface to make the “Shopping Cart” to “Billing+Shipping” transition less intimidating, since this is the stage where we are losing the most customers. As one idea, we might ask UX researchers to recommend a more effective background color on these webpages, and then perform A/B testing and experiment with these colors over time to see what keeps people from abandoning their shopping cart items most, in order to maximize purchases.
In this vein, there is a continuous feedback loop between Google Analytics and the businesses that use it. From analyzing the data, companies can understand how customers behave on certain webpages. This informs what the company should change on their website in order to achieve their business goals, whether its more purchases, more clicks, or just more traffic in general. This is the power of data analysis.
Learn More About Google Analytics!
If you are curious and want to learn more about Google Analytics, there are lots of resources to learn from! I took a few days to study the Beginner and Advanced hands-on courses that Google offers for free on Google Analytics Academy to sharpen my skills. These courses are super easy to use and interactive — quizzing you on information that was just covered so you don’t get lazy and glaze over the content.
After completing these courses, I passed the Google Analytics Individual Qualification (GAIQ) exam, which demonstrates proficiency in using Google Analytics. It is a 70 question multiple-choice exam with a 90-minute time limit. You can take this exam for free here.
Data analytics is a really exciting and valuable skillset in the 21st century. It will likely continue to rise in demand as companies rely less on intuition and experience, and instead rely more on data to make decisions and evolve their business. So check out Google Analytics and see how data can provide insights to transform the products, services, and online presence of your business.
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