Most of what Google Analytics has to offer is between the lines of our normal data reporting. This means in order to make data work for you, we must understand what the “between the lines” data is saying. In Synergy’s experience, the best way to help oneself interpret the data is by dividing the data up into segments. With anything in life when it comes to problem-solving the best way to understand anything is to not have all of our thoughts overwhelmed. Rather have a base to start with.
Let’s say we’re eating a bag of skittles. Some eat them a handful at a time and others will divide the colors according to which they like the best. Breaking the colors up according to what we like best is a form of segmentation. Red is an awesome taste most enjoy or “useful data”, whereas yellow can be the useless “information” or the not tasty color. This problem-solving skill will be useful in getting our tasty data out of our Google Analytics, and help us throughout our not so tasty data.
I will start by defining the main different types of segments used by Google Analytics:
- User Segments: This is a technical term for “people”. Think of this segment as actual people visiting your site.
- Session Segments: This is the segment for where Google shows what, when and how a single user interacts with your website.
- Hit Segments: This is best defined as the close-up view of “what” a single user is doing on your website. An example would be a user clicking on a Google add, or an article on a web-page within the website.
Example time, we open our Google Analytics tool trying to determine how many users spent $300 in one session. If we run a user segment we will see every user that spent $300 as a complete total. As in if user A spent $50 in one session and $250 in another while user B spent $300 in one session both users will show up in a user segment. However, if we were to run a session segment for users that spent $300 in a week only user B will show up.
Knowing some of this information found in our Google Analytics tool, we can effectively weed out problem areas on our website. See things we are getting right and reproduce the behavior. Lastly, throughout our un-tasty data that won’t help us much when it comes to improving our site.