5 Real World GA4 Use Cases for Ecommerce Merchants

Catchy title right? In case you just want to skip to the tips and tricks without the important context here’s a set of anchors

First a bit of background. Since Google announced the Universal Analytics sunset I’ve been kept very busy helping mid to large businesses get up and running with GA4. I’ve completed more than 50 implementations in fact, and the majority of these (close to 80%) have been for ecommerce merchants across most major platforms.

As part of my Google Analytics implementation service it became apparent early on that most marketers didn’t just need a well set up Analytics property, but they really needed training and support to make the most of the new toolkit and measurement philosophy introduced with GA4.

This meant that my service very quickly evolved, with the majority of clients taking a package of implementation and training. Now, my training process isn’t just me standing in a (virtual) classroom and broadcasting at a dozen people frantically taking notes, oh no. I mean there is a bit of that, it’s essential, but I limit that to what I refer to as the “Fundamentals” session, a streamlined “What I wish all my clients knew about GA before I started working with them” session.

Regardless of when or how we got into GA it’s safe to say we are pretty much all self taught in this industry so this GA4 Fundamentals session makes sure that any knowledge gaps are filled. I used to do the same with Universal Analytics clients too and even people who had been using GA for years told me they discovered things they never knew.

So the Fundamentals bit is useful, but I’m a big advocate of learning by doing so after this session people feel confident enough to get to the essential data they need and they are fully informed as to the art of the possible (custom measurement plans and all that good stuff). I give them a couple of weeks and then we do some 1:1 or small group workshops to build on what they’ve learned and really apply it to the context of their roles.

Why am I telling you all this? It’s not to sell my GA4 services (well it is a little bit, contact me to discuss) but it’s important context for the article. You see with 40+ ecommerce clients going through this process in the last 2 years you can imagine all the different perspectives and use cases these follow-up workshops have covered. These workshops have informed the tips and tricks I’ll be covering in the remainder of this article. These aren’t purely theoretical things, they’ve all stood up and walked. So let’s get into it.

Understand Upper Funnel Touchpoints

OK, starting with an obvious one, and if you’ve read anything about GA4 or listened to any podcasts on the subject this is one of the big advantages. But how do we really do it? And what do we do with that info anyway?

So, let’s talk a bit of retail theory for a second. You have an ecommerce business. You are selling stuff. Now, what happens when a customer makes a purchase from you?

I bet hardly anyone reading this went with “the customer churns out” as their first answer, but that is exactly what happens. There will be a period of time after the sale when it is almost impossible to get that customer to purchase again. That period of time will vary from merchant to merchant depending on how repeatable your proposition is and how good your CX is etc. (and if there is enough interest in this post I might write one on how GA4 can help you establish that period for your store) but it could range from days to years.

The other thing we know to be true is that it’s incredibly rare that a customer makes a purchase the first time they visit your store. It might take 3, 4, 5, 20 visits before they reach for their credit card (another future article topic).

So what does this mean for stores wanting to grow?

That’s right, you need to be attracting new customers who haven’t previously shopped with you at faster rate than you are churning out purchasers.

The problem with Universal Analytics was it didn’t really give us good enough data on what was happening prior to the session in which the conversion happened. Oh it tried bless it if you knew how to find the multi-channel funnels report but fundamentally the tech wasn’t up to the job so you ended up obsessing over the channels driving the converting sessions because you simply didn’t know which ones were driving valuable customers.

No wonder so many online retailers have seen such flat growth, they haven’t been working hard enough at acquiring new customers.

GA4, while not perfect, does a far better job here. So real world use case number 1 is spend as much time looking at your user acquisition reports as you do your traffic acquisition.

But how?

I decided to ease you in gently with a tip that is right there in the standard out of the box reports. If you have a standard unmodified library of reports it should be right there under Acquisition > User Acquisition and it will look something like this:

Compare this to the Traffic Acquisition report for the same period, this shows the channels that generated the converting session (basically what Universal Analytics used to show us)

So what does it tell us? Well for this retailer in particular we can see that on a last click basis we find some channels are better at converting bottom of the funnel traffic (i.e. those customers who are ready to purchase) whereas others are better at acquiring new users who went on to convert.

Look at the Organic Video channel for example on Row 5, on a last click basis it led to 257 conversions and £87k revenue. Not bad, but when we look at how effective it was at getting new customers to the site who ultimately converted through another channel we can see that it has £113k from 328 transactions associated with it. Potentially this could be enough to tip the balance towards investing more in this channel. Similarly Paid Search, often considered the most bottom of the bottom funnel channels in this case we can see that it is playing a part earlier on in the user journey too.

What is also really apparent is that the £40k acquired from the email channel doesn’t exist in a vacuum. By definition it isn’t a new user acquisition channel. We instinctively know this, now we have the data to back it up.

By not investing all your time and effort in those channels that are generating the last click conversions and thinking about how you need to acquire more customers in order to grow you may well find yourself thinking more about “marketing” and less about “performance marketing” (another future blog topic) and that is a good thing in this jaded old marketer’s eyes.

tl;dr: Spend as much time in the User Acquisition report as you do in the Traffic Acquisition report.

Conversion Optimisation Using Path Exploration and Funnels

2 of my favourite exploration reports in GA4 are the Path Exploration and Funnel Exploration. The true power in them is when they are used together as part of the same process.

I’m going to assume familiarity with both of these report formats and just talk about the process as it’s quite a difficult thing to screenshot without identifying the client but hopefully this will still inspire you.

So what is the process? In a nutshell my approach to conversion optimisation is

  1. Establish a hypothesis
  2. Baseline your metrics
  3. Make your site modifications
  4. Monitor your metrics
  5. Make your adjustments
  6. Repeat

How do we do this with Explorations?

Establish the Hypothesis

The Path Exploration report can be used to find popular routes through your site, unpopular ones, and those journeys where people are getting snarled up and stuck in a loop. The great thing about GA4 path explorations is you can work backwards from a conversion event such as a purchase and establish the “happy path” or path of least resistance. Customers following this path should be more likely to convert, whereas those who deviate from it for whatever reason less likely.

You can even mix and match page views and events in your happy path.

Browsing this report should inspire the journeys through the site that you wish to either encourage or discourage, and hopefully give you an idea on what changes you need to make, but before you do that you need to…

Baseline Your Metrics

So you’ve found a sequence of pages and events that you want to influence either for better or worse with your site changes. Cool. But before you stick a load of Jira tickets in front of your devs you’d better have a way to measure whether the changes have delivered the goods.

Although the Path Exploration is great at uncovering user journeys it’s actually pretty terrible at reporting on changes to these journeys over time. So what do? Good question. What do is Funnel.

You see you can build a funnel exploration that exactly matches the journey you uncovered in the path exploration report. This can be used as the basis for your reporting going forward.

Make Your Site Modifications

Raise those Jira tickets yo.

Monitor Your Metrics

Once the changes have been deployed you can use your shiny new funnel report to see whether your customers are successfully completing your prescribed journey in greater or fewer numbers. Maybe you were optimising the checkout, maybe you were trying to get more wishlist engagement, whatever it was you can see whether it worked or not.

Make Your Adjustments

Have you actually made a difference? Do you need to modify your response?


Keep going until you’ve rinsed all the value out of that hypothesis and move on to something else. Protip: Never assume that because something didn’t work first time that it will never work.

Find Out Whether A New Web Release Has Impacted You

I was performing a GA4 audit for a very well known British brand recently and buried among the usual issues there were some really nicely thought out elements to the implementation. One of them was so cool that I interrupted my flow and went straight to LinkedIn

Just in case that screenshot needs more explanation here’s what they were doing

  • Every time their developers did a new release of the website (in this instance it was an SFCC site) they numbered the version of the codebase they were releasing
  • They wrote that version number to the dataLayer
  • A dataLayer variable was created in Google Tag Manager to read that dataLayer entry
  • The GA4 Config Tag Google Tag had a configuration parameter set to include this variable as the “website_release”
  • An event scoped custom dimension was created in GA4 for the “website_release”

I really like this, you can imagine how valuable it could become when looking back, for example

“Our conversion rate increased significantly in October last year, can we expect the same again…ah it coincided with release 3.4 of the website, we’ve not go anything as significant on the roadmap, best not forecast that for this year”


“Why are we seeing fewer add_to_cart events, ah looks like release 4.31 is the culprit, let’s investigate what was in that one”

Potentially powerful stuff and a massive timesaver when looking into the root cause of changes to onsite performance.

User Scatterplots To Inform Merchandising

If you’ve not looked into Scatterplots and you are an ecommerce merchant you are really missing a trick. You’ve probably seen the scatterplot card in the Monetization > Ecommerce Purchases report but did you know you can create your own version in the Explore Reports? Start with a Free Form report and select the Scatterplot option and then add your item scoped dimensions and metrics.

A picture paints a thousand words so here’s one I’ve created from the Google Merchandise Store sandbox account, you can replicate this config in your own store.

So what is it telling us?

Quite simply it is showing the revenue generated by each product relative to the number of views that product had. Why is this really useful? We in the report above we have item views on the X (horizontal) axis and item revenue on the Y (vertical) axis. We can then mentally split the resulting scatterplot into 4 quadrants like so:

  • Top Left: Low Views/High Revenue Contributors – consider making these more visible to your customers or promoting them in your email newsletters to increase the views and hopefully generate even more revenue from this selection
  • Top Right: High Views/High Revenue – these are well optimised and contributing well relative to their visibility but they will need monitoring for signs of customer fatigue
  • Bottom Right: High Views/Low Revenue – products that are very visible but aren’t contributing much revenue. This could be part of an intentional marketing strategy (we’ve all seen those brands who offer a clickbaity product like a tank for $1m with no intention of ever selling one), or it could be a low cost-high margin product. If there isn’t a specific business context behind it then maybe you’ve bored your audience with a previously popular product (audience fatigue) or are simply showing them something that isn’t landing with them. Might as well deprioritise that item and look at other ways to sell it through
  • Bottom Left: Low Views/Low Revenue – products that aren’t being seen enough to contribute significant amounts of revenue, experiment with making these more visible through on-site merchandising, email newsletters, socials, promo banners etc. and see which quadrant they move to

Understanding Upper Funnel Part 2: LTV Boogaloo

Buried in the Explorations reports you’ve got a nice little feature that can really open some eyes. It’s called the User Lifetime report and what it does is shows you the average revenue per user and average number of transactions for users acquired by different channels.

Here’s an example showing the comparison between customers first acquired from Bing vs customers acquired from Google. This particular client isn’t doing anything on Bing at all but as you can see they probably should.

There was one retailer in particular who I showed this report to and it changed things up quite significantly for them. You see they had a PPC agency working for them and what they were doing was putting all their effort into Google Ads and just sticking 10% of the budget onto Bing with minimal management. I guarantee this is sounding very familiar to many of you reading this.

The bottom of the funnel numbers were less sexy on Microsoft Ads and the volume lower, conventional wisdom from last click data in UA was that you’ll get more revenue from Google so let Bing take care of itself. That was until the merchant in question looked at the LTV report and found that on average over 12 months a new customer acquired from Microsoft Ads generate FIVE times more revenue than a customer first acquired from Google.

So what was happening? It’s pretty clear that in this instance Microsoft was actually more effective as a discovery channel, with subsequent visits happening though navigational channels (including Google) so Bing simply wasn’t showing up as being especially valuable in the reports they had used up until that point.

What did they do? They asked their PPC agency to put more focus on Microsoft Ads than they were previously, this didn’t mean shift all the budget to that platform of course, the volume is still on Google, but over time they were able to find a level of spend and effort that maintained decent lifetime value at higher volume. Literally a hidden growth hack.

Are You Inspired?

GA4 has a load of extra bells and whistles, and if you are an ecommerce merchant looking to grow you need to look at the new features that help you achieve this. Hopefully this article has given you some inspiration, if it has I’d love to hear from you.

If you’d like to chat more about your own GA4 use please do contact me either here or on LinkedIn.

Coleman Marketing Services

Digital Marketing Consultancy

Ecommerce Consultancy

  • Project Specification, Discovery & Requirements
  • Technical Architecture
  • Martech Integrations
  • Conversion Optimisation
  • Platform and Supplier Selection
  • Agency Relationship Management

Ecommerce Platform Expertise

I've worked with most major platforms including