GA+

No matter how many guidelines and heuristics you follow, you’ll surely still hit walls and discover inefficiencies in the system. But hopefully, if you study some hard-fought Google Analytics tips to start, you’ll avoid the most damaging mistakes.

That’s where this article comes into play. I’ve organized 25 Google Analytics tips and guidelines learned from many, many hours of time spent in the tool and discussing GA as well as data in general with analysts (a fun cocktail party conversation, I must say).

So, let’s get you up to speed with the insight gathered in those conversations…

 Google Analytics Tips for Beginners

Google Analytics Tip #1: Start with Business Questions.

Digital analytics is a means of extracting insights that will translate into business value. To put that more simply, your analysis work should be driven by important business questions.

This will come as a slight contrast to the title of the article, but one of the top Google Analytics tips is that you shouldn’t just rely on guidelines or checklists; rather, it’s all about asking smart, important questions.

The mark of a good analyst isn’t an advanced knowledge of a tool’s interface, either — though it does help to know where you’re clicking and what the screens mean.

It’s being able to step back and think critically, ask important business questions, and answer them with a high enough degree of precision in order to take action.

The challenges each business faces are quite unique. If you try to follow a formula or a best practice, you begin to have the illusion that you’re gathering insights–but you’re just taking the easy way out.

I think Avinash Kaushik put it best:

“You need business questions, because:

1. Rather than being told what metrics or dimensions to deliver you want business context: What’s driving the request for that data? What is the answer the requestor looking for? Then, you apply smarts, because you have context.

2. Best practices are highly overrated. If this is your first day on the job, sure, go ahead and puke out what “industry experts” recommend. But know that it won’t impress anyone, because you don’t actually know what the business is doing / cares about / is prioritizing.”

What are the right questions? It’s highly contingent on your business and your situation. I could rattle off important business questions that I’d like to know endlessly:

• Do customers that purchase X tend to purchase more products down the line? In any common order?
• What are the common on-site behaviors of our most valuable customers?
• What’s the marginal value of our display campaigns? How can we improve their impact?
• What’s the behavioral difference in cohort A and cohort B, since we made X changes to the product?

…and on and on. But lead with the question, don’t just “data puke” a report.

Google Analytics Tip #2: Set Up Goals.

We can’t cover Google Analytics tips without covering Goals. You can’t use Google Analytics if you don’t use goals.

You may think you’re using Google Analytics — you look at your traffic numbers, you see your bounce rate and how it changes over time, you can discover what devices your audience is using. But if you haven’t set up goals, you’re missing out on 95%+ of the value that Google Analytics offers.

Setting up goals in Google Analytics forces you to take a strategic approach to your measurement. It makes you ask, “what’s the purpose of this site?”

Luckily, it’s incredibly easy to set up goals in Google Analytics. Just log into your Google Analytics account and then:

• Click Admin, and navigate to the desired view.
• In the View column, click Goals.
• Click +NEW GOAL or Import from Gallery to create a new goal.

Google Analytics is useless without Goals.

• You have 3 basic options for creating goals:Using a goal template
• Creating custom goals
• Creating Smart Goals

I won’t go too far into the different types of goals (here’s a great guide for that if you want to read more), but for basic and prototypical business cases, the goal templates tend to suffice. You can easily set up a Goal for a thank you page, for instance. Let’s pretend we’re doing that for an ecommerce site here:

Setting up our first goal in Google Analytics

You’ll notice in the image above that I also added steps under “Funnel.” This is important, and can add a ton of value when you start thinking about conversion optimization. More on that later, though.

Think about what matters to your business and set up goals in Google Analytics to track for it.Google Analytics Tip #3: Map Out Your Event Tracking Strategically.

Events in Google Analytics help you fill the gaps between traffic analysis and goal analysis. Essentially, they let you see nuance – what are users actually doing on the site?

Here’s the Google Analytics’ official definition:

“Events are user interactions with content that can be tracked independently from a web page or a screen load. Downloads, mobile ad clicks, gadgets, Flash elements, AJAX embedded elements, and video plays are all examples of actions you might want to track as Events.”

Once you’ve set up events, here’s what an example report may look like from within the GA interface (accessible via Behavior > Events > Overview):

Events overview in Google Analytics

• You’ll notice that there are three components that make up events in GA:Category
• Action
• Label (optional, but recommended)

For example, if you have a video on your homepage and want to track interactions with it, the following values could be in play:

• Category: “Videos”
• Action: “Play”
• Label: “Home Page”

Google Analytics events is a massive topic. It’s also a strategic one. It’s an area where, again, you need to step back and think about your business goals. What user interactions matter to your site? Perhaps, it could be that users:

• Download an ebook
• Play a video
• Interact with a slider
• Interact with a menu
• Play a song

The list is limitless. There are also platforms that are built around event-based tracking, like Amplitude and Heap, that you can check out (usually used by software products or mobile apps, but also by ecommerce, too).

Read up more on Google Analytics events here.

Google Analytics Tip #4: Question Your Definition of Bounce Rate.

People often take bounce rate at face value. They think that a high bounce rate is bad and a low bounce rate is good. It’s not that simple, though.

If that were the case, the easiest way to lower your bounce rate would actually have nothing to do with user behavior; the easiest way would be to set up an adjusted bounce rate.

The definition of a bounce rate is basically a single-interaction session on your site. Most people forget the “interaction” part, and they just assume that a bounce is when someone 1) lands on a page and then 2) leaves.

But look at the rest of the description from Google Analytics:

The definition of Bounce Rate.

“Without triggering any other requests to the Analytics server during that session,” is what matters. Because, depending on how you have events set up in Google Analytics, someone could actually come to only one page and exit, and not be counted as a bounce.

• It’s a somewhat complicated topic, but there are essentially two types of events you can set up:Interaction Events
• Non-Interaction Events

To explain these two, imagine a hypothetical website, let’s say an ecommerce site that sells doggo tshirts. A user lands on the homepage, watches a product video, and then leaves. Should that count as a bounce? Well, it depends how you set up that product video event.

Define what counts as a meaningful event on a page and consider it as an interaction.

If you set it up as a non-interaction event, it won’t affect your bounce rate. The session will be counted as a bounce. If it’s an interaction event, then by playing that video, the user sends an additional request to the Analytics server during the session, and it will not be counted as a bounced session.

You can see how this is a strategic decision. You can select what counts and what doesn’t as interactive events.

It’s a somewhat confusing topic, so I encourage you to read Yehoshua Coren’s guide on the subject here.

 Google Analytics Tip #5: Set Up Funnels.

Let’s bring the conversation back to goals for this another one of our Google Analytics tips. With any goal, we can set up a funnel in order to view the journey that our users, in aggregate, took to reach the goal. With that, we can view the steps at which people fall off, effectively finding the leaks in our conversion funnel.

A typical Google Analytics funnel visualization

• It’s easy enough to set up funnels, especially when you have a simple page-based goal. You just work backwards at steps the user would need to follow to get there. In the example above, the user goes from:Cart
• Billing and Shipping
• Payment
• Review
• Purchase Complete

Yours may differ, but it usually follows a similar format (at least with ecommerce). You set up funnels while you’re setting up your goal:

Set up funnels while you set up your goals.

If you want to read more on funnels, check out this section of a CXL article.

 Google Analytics Tip #6: Review Horizontal Funnels.

As part of Google Analytics’ Enhanced Ecommerce feature set, you’re able to squeeze a lot more value out of funnels.

This works mainly through the Shopping Behavior report (Ecommerce > Shopping Behavior). Of course, you have to enable Enhanced Ecommerce as well as configure how you want your funnel to display. But this is what it will look like:

“Horizontal” funnels in Enhanced Ecommerce

Essentially, you get to access a horizontal funnel, which is actually quite customizable in its use cases (check out this article for a tutorial on using it for content engagement analysis).

In addition, there are many problems and limitations with the classic Google Analytics funnel (including backfilling, and the lack of segmentation capabilities).

So, the above funnel shows you a lot. It shows you that out of 17,844 total sessions, 3,459 of them viewed products. From there, 1,680 sessions included an “add to cart” action, etc. We can mix it up further, still, and change our “Sessions” to “Abandonments,” giving you an inverse way of viewing the data:

You can flip to abandonments to see another angle of your data.

You can also view the funnel using different dimensions. For instance, to view behavior by Device Categories, just click on “User Type” and select “Device Categories” from the drop down menu:

Analyze ecommerce analytics funnels by a variety of dimensions, including device category.

In addition to the previous Google Analytics tips (a tip within a tip, if you will), you can add customer audiences, which is super powerful. Just click + Add Segment at the top of the screen as you usually would, and select whichever audience you’d like to analyze.

Another cool feature of this Enhanced Ecommerce funnel feature is that, if you’re integrated with Google AdWords or Doubleclick, you can create an audience off of anyone who abandons at a particular step and use them in your AdWords campaigns. This enables more advanced targeting for your ad campaigns.

 Google Analytics Tip #7: Integrate Google Analytics w/ Your Testing Tool.

If you’re running A/B tests, make sure you’re bringing the data somewhere other than just your testing tool.

It’s not that your testing tool is untrustworthy; it’s just that you want a second look at your data. In addition, there’s a ton of limitation with how you do post-test analysis if you’re only using your testing tool to do it.

For instance, you won’t be able to dig into different segments and see how they were affected by the test. You also (usually) have to ask for the raw data when it comes to revenue analysis, which is a pain. By integrating with Google Analytics, you can get a second take on your data, and you can also view user behavior at a more granular level.

Always integrate test data with your analytics tool. – image source

 Google Analytics Tip #8: Utilize Custom Segmentation.

You’ve heard it before: averages lie. As Avinash Kaushik put it, “All data in aggregate is crap.”

If you’re only looking at Google Analytics reports from a high-level birds-eye view, you’re missing out on so much information. In addition, most important business questions rely on your ability to dig into advanced segments (not all, but most, I’ve found).

Google Analytics has a whole bunch of out-of-the-box segments. You can find them when you click on + Add Segment and then click “System.” They include segments like “Bounced Sessions,” “Direct Traffic,” and “Made a Purchase.”

Google Analytics “System” segments

These can be helpful, but shouldn’t limit the scope of your analysis. For further insights, you can use custom segments. To create one, you just click the red + New Segment button on the top left. Once there, you can pretty much set any parameters you may want to dive into.

Custom segments are limitless in their permutations.

Now, there are pretty much unlimited possibilities here, but I’ll give you a concrete example to really drive home the value of this Google Analytics tip. Let’s say we want to see the behavior of all those who landed on a given blog post and then made a purchase. To do that, we’ll go under “Advanced” and click “Sequences” (given that we care about the order of actions).

The actual set up will look like this:

Setting up an advanced segment in Google Analytics

For an in-depth explainer on creating custom segments and more Google Analytics tips, check out this guide from Google, or check out a guide Shanelle Mullin wrote on CXL’s blog.

 Google Analytics Tip #9: Learn RegEx.

RegEx (or a Regular Expression) is a sequence of symbols and characters expressing a string or pattern to be searched for within a longer piece of text. What’s the importance of RegEx to Google Analytics? There are a lot of use cases. You can…

• Create filters. Many filters require Regular Expressions.
• Create one goal that matches multiple goal pages.
• Suppose your Thank You page has many names, but they’re essentially the same goal. You can use Regular Expressions to “roll them up.”
• Fine-tune your funnel steps, so that you can get exactly what you need.

Learning RegEx will boost your skills as an analyst, without a doubt. There are many good resources to learn from, but this PDF from LunaMetrics is my favorite one. Here’s a good forum with many other resources.

My advice is to chip away at it piece by piece. Learn something and try it out in Google Analytics, then repeat until you’re an expert.

Google Analytics Tip #10: Explore Google Analytics Custom Reports.

Another way to get more out of Google Analytics is to use custom reports. I talked about this a bit in a previous KlientBoost article, but to reiterate, custom reports allow you to create reports and dashboards directly in their interface. You can find this feature in the Customization section under “Custom Reports.”

There are three types of reports:

• Explore
• Flat Table
• Map Overlay

Without diving too deeply into complexities or nuance, here’s an example of a very simple custom report that shows conversions by device type:

Conversions by Device Type Custom Report

Google Analytics custom reports are super easy to make, and when you figure out how to do it, you’ll be using them all the time. To learn more, read this post from the KlientBoost blog (specifically the custom reports section)–or if you’d just like some quick ideas for custom reports, this is a good CXL article on the topic.

 Google Analytics Tip #11: Automate Your Reporting.

While I think analytics should largely be a function of asking important business questions, there are certain reports that you’re constantly running. When a task is repetitive, it only makes sense to automate it.

With Google Analytics, you can do this a few ways, most of which include bringing your data to a third-party tool. We go over this extensively in a previous KlientBoost post on GA reporting, but two of my favorite ways to automate reports are:

• Google Analytics API and Google Sheets
• Data Studio

Realistically, the API/Sheets strategy may give you some number crunching capabilities that you can’t get in Data Studio, but Data Studio is so easy to use and share that I couldn’t recommend anything else if you’re looking for a simple an effective way to automate reporting.

Data Studio makes it easy to create, automate, and share reports.

Take a few hours and learn how the interface works. CXL Institute put out a class on Data Studio if you’d like to dive a bit deeper.

 Google Analytics Tip #12: Analyze Website Performance for Tech Fixes.

Though it’s not the sexiest part of conversion optimization, one of the most important factors in on-site website optimization is making sure that your site runs fast. Another factor is whether you have technical bugs on certain browsers and devices. If your site isn’t usable, persuasion triggers and pretty design don’t matter.

Luckily, there are a few simple reports you can run in Google Analytics to see if you’ve got a speed problem (everyone can improve). It can also show you if you have bugs on individual devices or should focus on any specific technical fixes.

We’ll start with a cross-browser and cross-device testing.

Open up your Google Analytics and go to Audience -> Technology -> Browser & OS report.

You will see conversion rate, bounce rate, etc., for each browser. Make sure you look at one device at a time, so you don’t get fooled by averages. Apply a device segments first: desktop only, tablet only and mobile only.

Cross-Device and Cross-Browser analysis

In this report, I organized mobile traffic by a comparison view. That way, we can see which ones are underperforming. In general, though, look for the anomalies.

As Peep Laja, founder of CXL, wrote in a blog post:

 “While it could be that people using older versions of IE are just some stupid old people not spending money, but it might also be that you’re the stupid one losing money due to some bugs or UX issues.”

Next, let’s look at speed. Go to Behavior → Site Speed → Page Timings. Turn on the ‘comparison’ to easily spot slower pages:

Page speed report in Google Analytics

Again, look for all entries that load with a sub-optimal speed. Then, you can use a tool like Google PageSpeed Insights to find issues to repair.

 Google Analytics Tip #13: Set Up Your Views Correctly.

While every organization operates differently, there are some Google Analytics tips and best practices when it comes to setting up your Google Analytics account. There will always be abberations with good reasons, but generally it’s advised to set up three views right away:

• A Master View
• A Raw Data View
• A Sandbox View

The Raw Data view, as suggested, should remain untouched. Once you add a filter to a view, it alters the data permanently, therefore you should have some insurance in the form of a Virgin view.

Next, the Sandbox view should be used to test new implementations, filters, etc. Anything that you eventually plan on implementing in your usable, workable, Master view, you should first try out in Sandbox view. This is sort of a “measure twice, cut once” method that prevents you from creating needless mistakes.

Google Analytics Tip #14: Implement Google Analytics Accurately.

This is easily the most important tip on the list, so I apologize for the buried lede. However, it’s also the most complicated to talk about. How you implement Google Analytics should be largely prototypical, but there will be slight nuanced based on your situation.

The important thing is that you and your organization can trust the data that you’re getting from the system. If you don’t trust the numbers, they’re absolutely worthless.

To fortify your trust in the system, I recommend doing a Google Analytics audit. Be critical. Ask important questions about your data, and try to maintain your data integrity and accuracy.

Google Analytics Tip #15: Review Cross-Domain & Sub-Domain Tracking.

One of the most common problems in a Google Analytics setup is the lack of correct cross-domain or subdomain tracking. This fundamentally fractures your view of the customer journey and how users interact with and convert on your site.

An example that I’ve written about before…

I could listen to Spotify’s web app all day every day. I could be a power user, a paying customer, and yet, I still may want to wander over to their developer’s site every once in awhile, because I want to discover the most depressing Radiohead song using their API and some wizard-like R skills.

Thing is, Spotify sends this data to another Google Analytics property. What this means for the analyst is that I become a new user according to the developer’s site. In this way, there’s no way they can track my complete customer journey and total behavior with Shopify’s web properties.

You can see this by using GA Debugger, a free Chrome extension that is useful for doing analytics audits without even opening up the GA interface.

Here’s the homepage (spotify.com)

And the developer’s page: (developer.spotify.com)

They’re different tracking IDs.

If you have subdomains or other domains, you should look into cross-domain and sub-domain tracking implementations. Especially if you’re running an ecommerce shop, some cart solutions fracture the journey, which makes analytics quite useless.

Here’s a good guide from Google Analytics on the topic, and here’s an even better one from Optimize Smart.