Recently I’ve been asked how to use Google Analytics. There are two ways to learn Google Analytics:
- As a tool, which Google provides an abundance of resources at their Conversion University, and
- How to interpret data.
To explain how to interpret data can be complex and overwhelming question for an analyst (where to do I start?!), but it can be simplified by applying lessons from grade school.
First define what it is that you would like to prove by utilizing the steps from the scientific method:
- Define a question. What is it that you want to learn by looking at this data?
- Form an hypothesis from previous knowledge, or what you expect will the outcome will be.
- Collect data, testing the hypothesis. This is where utilizing Google Analytics as a tool comes in.
- Analyze the data and draw a conclusion. Comparing the metric to a type of benchmark can provide more context.
- Publish results or retest if necessary or evaluate supporting data to further analyze.
To understand the terminology used in Google Analytics I have explained them using the 5 W’s…plus an H. Who, What, When, Where, How and Why.
- WHO is the subject. In Analytics, this is known as the Dimension.
- WHAT is the variable. In Analytics, this is known as the Metric.
- WHEN is the date range.
These are the basics. Metrics are applied to each dimension. Some examples of Dimensions are:
- Traffic Source – where did the user come from and find your website?
- Landing Page – what was the first page that the user saw when they came to your website?
- Transaction – what was the transaction number of the user? This applies only to e-commerce websites.
Dimensions can be seen on multiple levels. An example would be looking at all Transactions within a specific Traffic Source.
- WHERE could be the second dimension to compare (but not always necessary).
Analyzing data and comparing to other metrics or trending over time can provide more insight. The rest of the W’s create context for the data set.
- HOW is the correlation or deviation of trending over time or a comparison to another metric.
- WHY is the causation, your analysis to what happened within that date range to cause that effect or to dig deeper with supporting reports and analysis. Remember correlation does not always imply causation.
WHO – Dimension
WHAT – Metric
WHEN – Date Range
WHERE – Secondary Dimension
HOW – Correlation or Deviation
WHY – Causation