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Getting answers from Google Analytics for Beginners


The first time I was staring into a Google Analytics dashboard I was absolutely perplexed. My mentor’s mouse was hopping from one report to another, he was spitting out insights like it was nothing. He saw my distress and commented: “It’s easy, you just need to remember where to find the right reports.” I was not convinced. It felt like there was much more to it.

Now that Google Analytics feels just as natural as Google Docs, I must say I still think a competent analyst is more than a dashboard operator. Finding the answers is just one half of a job well done – the last half, to be accurate. The first half is figuring out the right question and formulating it in a way, which has the power to initiate the needed action. 

The power lies in the question – A case study

We had a client come to us a little bit panicky, visibly stressed, and totally fixated: “Our one-page checkout is screwed! Our conversion rates are dropping no matter what we do. Please make it go away!”

It was true. Their total conversion rate had dropped from 1.8% to 0.6% in the last year. That’s a lot. One could even wonder how it is possible that they didn’t seek help sooner. But maybe that’s not so strange for a small company in an emerging industry, for which e-commerce is just a side business. 

So we rolled up our sleeves and started with the low-hanging fruit. Or at least that’s what we thought we were doing. We set up Hotjar, double checked tracking, did some testing. Basically did a general assessment. But we found nothing of value – no insights, nothing. 

Doubt crept in. How sure are we that the client knows where the issue lies? We had not confirmed our client’s assumption, so we started to dig for answers.

We threw out our findings and started from the beginning. We knew conversion rates have fallen—that was evident. What was not clearly understood was what role was being played by the one-page checkout step. How could we observe what was wrong with the funnel? Also, the reason for the client’s late discovery of this alarming trend was that revenue was not falling at all. Even weirder…

We came to a conclusion, that we need a chart where we could observe all of the e-commerce stage to stage conversion rates over time.

We created a dashboard in Google Data Studio (link to the how-to) and this dashboard itself turned the problem on its head! The issue was not at all connected with the clients assumption!

This is what we have discovered:

  1. The client thought they had a problem with one-page checkout.
  2. We analysed trends in conversion rates between shopping stages over time.
  3. A drop in conversion to sessions with product views was observed.
  4. We then found a substantial increase in organic traffic to their blog.
  5. The blog was not well optimized to lead visitors to products.
  6. CRO was done on the blog + the blog got its deserved spot as one of the channels with the most potential.

Remember – the question asked by the client was objectively misleading! If we had just given them the report with some metrics about their one-page checkout, we would have missed this huge opportunity to develop a channel they were undervaluing.

We have asked ourselves how we could find a way to improve our answer generating process?

Q.I.A. Framework

This is the work of Chris Mercer, check out his webpage here: https://measurementmarketing.io/. Or do a Google Analytics course for Beginners. Chris is an amazing educator and I can only recommend this course: https://cxl.com/institute/online-course/google-analytics-beginners/

To continue: When people come to you and demand answers, the first thing you must do is turn that question around: “What action do you want to take?”

A lot of times people think they are following the straightest path, but they actually aren’t. Usually non-analysts have a foggier picture about the data. It is the analyst’s responsibility to make the picture clear. To translate a desire for some action into a question, which can be answered with analytics. And to make sure that the invested energy creates actionable results. 

Knowing the ACTION makes the ANALYSIS easier.

Because an analyst knows why we are asking the question in the first place. Knowing the desired action leaves you more open and creative when searching for ways to help that story.

Enter the Q.I.A. Framework.

  1. Q: Start with a question.
    Translate the question into a formulation which makes it answerable with the means of Google Analytics.
  2. I: Information. What piece of information is needed to answer the question?
    This should be determined before you start fiddling around your Google Analytics dashboard. Otherwise you are susceptible to a bunch of biases that will not help at all.
  3. A: Action. What action will you take based on answers you get?
    Don’t just give out analytics reports with no actionable tasks. Analysts final products should be suggestions for actions, not snapshots of dashboards.

Let’s check the theory on some examples.

Examples of good questions

Should we redesign the site to be mobile-friendlier?

Lets translate this into two answerable questions:


  • What devices are used to access the site?
  • How much traffic (engagement, revenue, leads … ) does each device category generate?


Thinking behind it: If there is a sizable amount of users using mobile, we’ll look into a redesign. But what is sizable? To put it in the form of an action-initiating statement:

“If at least 20% of users are using mobile devices to access the site, we’ll look into a redesign.”

The solution would be quite straightforward:

  1. In Google Analytics find the Landing Page report,
  2. Add secondary dimension: Device Category,
  3. Is traffic significant enough compared to your metric?

Should we focus on building more affiliate partners?

Translated into an action-initiating statement:

“If our partner’s traffic is selling products then we’ll invest resources in getting more partners.”

We would need: Traffic source results, Ecommerce/Stages (maybe we are not selling products), Partner IDs.

How would we go about this:

  1. Check traffic sources. 
  2. Check conversion rates and various stage goals (maybe affiliates don’t create much sales, but are good at generating engaged users).

Should we allocate a larger portion of our budget in Google Ads to close more sales?

This would translate into: “Which channel has a better ROI?”

The analysis would demand complete traceability of source/medium to the final sales or deals. This could be quite tricky in products with long sales funnels. A well thought-through CRM system would store the needed data here and analysis would have to connect multiple data sources.


When getting answers from Google Analytics you should always evaluate the usefulness of your final product – the answer. What will we, as a company, have from doing the analysis? What is the measurable effect this task can have? 

An analyst’s efficiency is not measured by the esthetics of color-coded dashboards. Its value is in the change of the metric you are researching about. 

Stay focused & happy analysing!