Yes, you can use GA data to understand customer behavior.
Contrary to what some replies tell, I tend to go the different way round: I try to NEVER (important to me!) first look at the datasets and THEN ask the question "What could I read here?". This approach basically plays into the hand of the big data problem: If I do not have actual questions, then it does not matter how much data I gathered - I will always find different or no answers for the same question.
What I try to do instead is to ask "What do I want to understand about our user?" and THEN see, what data is available.
To do this properly, I usually divide my question into two parts. Let's assume I want to check behavior on the checkout, the following two questions are important from UX perspective:
- Is the user interested in what she sees?
- Does the page perform? If yes/no - why?
I try to clearly separate the two parts: User Engagement & User Performance. Why? We need that to identify levers.
Coming back to the checkout, the questions would be:
- "Is the user going to the checkout at all?"
- "Is the user going to the next step(s)?
- "Does the user finish the process?"
The result for question 1 answers the "Is the user interested in...?" question. If users WANT to buy, they start the process.
Results for the second question answer the question "did the step before work out properly?" - so the same question suddenly answers a performance question - and question 3 also aims for performance.
This being said, we might learn interesting things. Some time ago, while optimizing a checkout, we found that people tend to register, go through 2 of 3 steps, then left. Wow - What an insight: We immediately went to the confirmation step and included trust elements, cleared up the overview of the items, put better focus on USPs and "final" CTA - and bam, there we go: CVR & satisfaction increase.
Summed up, we first checked "What is the user interested in?", then asked "Are we able to satisfy user's needs?". No, we could not - THEN we looked into data deeper, found that they spend looooong time on the confirmation page, THEN found that we had to optimize this very step. I think you get the point: Looking at CVR of checkout never would have gotten us there, neither would have page visits on step 3. The order of questions is important.
So my shot would be:
- Define micro conversions (Add to cart, Checkout start, Newsletter signup, ...)
- Check user engagement on these Micro CVRs
- Check performance of Micro CVRs on different segments (is mobile different from desktop? Are especially new users exiting the checkout? Understand the situation of the user and why it is harder for HER than for another user)
- Identify levers by looking at "Users were INTERESTED but did not PERFORM. Why?"
- Go as deep into data as you need to pin point the problem. If you can not find it, go for research. Check best practice NOW - not before. Think first, then go ask for help. It will increase quality of YOUR product.
- Fix it.