Google Analytics is a POWERHOUSE for figuring out what's working and what's not in your marketing. When configured properly, you'll gain insights similar to Wicked Reports or Hyros! In this guide, I show how with both Universal Analytics and Google Analytics 4.

What is Attribution Modelling?

An attribution model is a set of rules for defining how much conversion credit is assigned for each touchpoint prospects encounter on their journey to becoming customers.

Some campaigns are better at acquiring leads (Top of Funnel). Whereas other channels are better at converting those leads into paying customers (Bottom of Funnel). Attribution modeling allows advertisers to view their conversion data through different lenses and gain key insights into how and where their efforts are having an impact.

Why do you need attribution models?

Let's say traffic is coming to your site via a Facebook ad, a display Google ad, paid search, Google Shopping, content strategy, Twitter, and Reddit.

Which channels are responsible for making revenue and which ones are losing money?

Typically, a number of channels play a role in converting prospects into customers and each should get a fraction of the credit.

But each ad network attempts to claim as much credit as possible for conversions they see even when they contribute very little to the end result. The reason they do this is they're in competition for your advertising dollars.

The more conversions, the healthier your ROAS, and the more likely you'll continue spending money.

It's why, when you add up the conversions from ad reports, the sum is significantly higher than the actual sales.

This is important when deciding whether to kill vs scale digital marketing campaigns. Without having an accurate picture of how each marketing effort performs, you risk making costly errors by over-investing in unprofitable channels and underinvesting in profitable ones. Either way, you leave money on the table.

How do you track marketing attribution?

To attribute conversions properly, you need to collect detailed data about traffic sources, transactions, and customer details. Then you need to blend data sets together so you can run sophisticated attribution queries.

It's nearly impossible to do this with a spreadsheet application.

Luckily, Google Analytics makes this relatively easy. All you have to do is feed it the right data format.

Multi-touch Attribution Models in Google Analytics

To unlock the power of multi-touch attribution, you must either have conversion goals or eCommerce enabled in Universal Analytics. Here's an example of a dataLayer payload for Universal Analytics.

Google Analytics 4 has purchase conversions and Ecommerce enabled by default. So, as long as you're collecting revenue data, you'll already have access to the Model Comparison Tool. Here's an example of a dataLayer payload for GA4.

What attribution models are available in google analytics?

Google Analytics has a number of excellent attribution models, depending on the version (e.g. UA vs GA4).

Last Touch Attribution Model

This model answers the question, "What's causing prospects to finally buy?"

This is your Bottom-of-funnel (BOF) traffic. Prospects already know about you and are probably ready to buy. This is the type of traffic that comes from email or retargeting campaigns.

ALL credit is given to the last interaction, while other channels that assisted in the conversion are ignored.

Last Non-Direct Attribution Model

A big portion of sales will come from direct visitors. But logic tells you these people didn't suddenly come to your site without some kind of prior awareness.

Weeks or months ago, prospects could've clicked a Facebook Ad, or found you in a Google search.

Perhaps, at the time, they weren't in a position to say "yes" to your offer. So, they bookmarked your site and came back later.

This behavior is typical. So, you gain more insight by looking for the interaction just before a direct visit that leads to a sale.

Similar to Last Touch, ALL credit is given to the last non-direct interaction.

Last Google Ads Click Attribution Model

This model is similar to Last Non-Direct Touch, except it gives all possible credit to Google Ads clicks. It's useful for specifically understanding how Google Ads (Google Adwords) are peforming.

Time Decay Attribution Model

In psychology, there's a phenomenon known as Recency Bias, where recent events influence you more than ones that happened a while ago.

Time Decay models this by assigning more of the credit to recent events.

First Touch Attribution Model

This model answers the question, "Where do customers intially coming from?"

This type of traffic is cold and is known as Top-of-funnel (TOF). Prospects are unaware of your business. Campaigns are geared towards building awareness.

ALL credit is given to the very first interaction, while other channels are ignored.

Linear Attribution Model

This model gives a broad view across all of your funnel touchpoints.

It's useful to gaining a general understanding for where revenue is coming from. Start with Linear Attribution for initial ROI investigations before drilling deeper with the other models.

Credit is evenly distributed among channels.

Position-Based Attribution Model

This model gives the 40% of the credit to the first and 40% to the last interactions, with the remaining 20% distributed among the other channels.

Data-Driven Attribution Model (GA4 only)

Of all the models, the Data Driven Attribution Model is the most sophisticated. (Only available in GA4.)

GA4 analyses your e-commerce data using machine learning to create a probabilistic model. Each marketing channel is then given a fraction of the available credit.

Data-Driven gives the most accurate gauge of how various channels and campaigns are performing.

What attribution model does Google Analytics use by default?

Universal Analytics uses Last Non-Direct Touch Attribution... even though its own documentation says it uses Last Click.

On the other hand, Google Analytics 4 uses Data-Drive Attribution out of the box, which is the most accurate of all attribution models.

What is the default attribution window for Google Analytics?

The default window for Universal Analytics attribution models is 30-days. However, you can create custom models with longer attribution windows.

GA4 applies a 90-day look-back attribution window by default.

How do you set up multi-touch attribution in Google Analytics?

GA4 and UA use mult-touch attribution models by default. There is nothing to set up per se.

However, you'll want to use the Model Comparison tool for marketing analysis. (See below).

Can you change the attribution model in Google Analytics?

You can't change the default account attribution model in Universal Analytics. You can however apply a  different attribution model to a specific view.

With GA4, however, you can change the model for the entire account. Here's how you do it:

1. Go to Admin > Property > Attribution Settings
2. Select an attribution model (Data-driven is recommended)

3. Choose the 'look back' windows for acquisition and conversion events:

Google Analytics Model Comparison Tool

Both Universal Analytics and Google Analytics 4 have a Model Comparison Tool.

What is a comparison view in google analytics?

The Model Comparison Tool allows you to compare two different or three attribution models.

Here's how to analyze marketing data in Universal Analytics.

First, filter by source / medium:

In the image above, CPA for google / cpc is $33.44 (Last Non-Direct Click) or $23.12 (First Interaction).

This result makes sense... some prospects visit your site without taking action until subsequent visits. That's why Last Click numbers are lower than the First Interaction ones.

You can also change the view to show either CPA, value (revenue), or ROAS.

To understand the ROAS of your advertising campaigns, select the Conversion Value & ROAS option from the dropdown.

In the example above, the ROAS for Last Non-Direct Interaction is 409%. But when comparing it with First Interaction, we get a figure of 594%.

By the way, if you want to see ROAS data for Facebook, Titkok, Pinterest, and other ad networks in this report, here's how you can import your ad-cost data.

NOTE - At this point in time, GA4's model comparison only allows you to view conversions & revenue. So, until GA4 updates this tool, stick to Universal Analytics for analyzing CPA and ROAS.

Why use the google attribution model comparison tool?

No single model is 'correct'. Think of the different attribution models as lenses you can use to answer different questions.

Here are some examples of questions you might ask:

• Which marketing channels are best at acquiring new prospects (Top of Funnel)?
• Which marketing campaigns are the most efficient at converting leads into customers (Bottom of Funnel)?
• Which lead sources deliver the greatest customer lifetime value?
• How does the content strategy perform in Middle of Funnel interactions (Middle of Funnel)?
• What's the contribution of each marketing effort, overall?

Using one lens is not enough to answer these questions. You normally start with Linear Attribution and then drill deeper with the other models.

The Model Comparison Tool speeds up this process.

Where is the Model Comparison Tool in Google Analytics?

In Universal Analytics, go to Conversions > Attribution > Model Comparison Tool.

In GA4, go to Advertising > Attribution > Model Comparison.

What is MCF channel grouping in Google Analytics?

This is the set of groupings GA gives you by default. For example -  Direct, Organic, Social, Search, Email, etc.

To edit the existing Channel Group or to add your own definition, go to:
Admin > View > Channel Settings > Channel Grouping.

What is the default attribution window for Google Analytics?

For Universal Analytics, the default attribution window is 30-days, but can be extended to 90-days by creating a Custom Attribution models.

Google Analytics 4 comes standard with 90-day attribution windows.

What is a custom attribution model in Google Analytics?

Universal Analytics gives you the option of creating a Custom Attribution Model. (GA4 doesn't have this feature yet.)

How to create a custom attribution model in Google Analytics?

In the Model Comparison Tool, click one of the model-dropdowns, then select the "Create new custom model" option from the menu.

1. Give your model a name
2. Choose a baseline model to start with
3. Adjust the Lookback Window (I suggest 90-days)
4. You can also adjust the amount of credit based on source, campaign, time on page, etc.

Choosing the Right Attribution Model

How do you choose the right attribution model?

Before killing campaigns, ask yourself, "Is this campaign delivering value for Top of Funnel, Middle of Funnel, or Bottom of Funnel campaigns?"

Top of Funnel (TOF) is the hardest to get right, likely less profitable than BOF, but has the most potential for scaling. Use First Touch Attribution to see which channels and campaigns are profitable.

You might have a campaign that's working well in Middle of Funnel (MOF) or Bottom of Funnel (BOF) but not Top of Funnel. Check Linear Attribution before killing low-performing campaigns.

Finally, you may find a campaign becomes sufficiently profitable when you suppress cold traffic (TOF).

Which attribution model is the easiest to use?

Last Touch Attribution is the easiest model to use. That's also why it's the most commonly used.

What is the best attribution model for lead generation?

Last Non-Direct Touch is the best model to use for lead generation analysis. The reason is, most prospects won't naturally gravitate back to a lead gen landing page unless directed back by an ad or other marketing campaign. In other words, they don't normally bookmark the page and return in 2 to 3 months. So Last Non-Direct Touch is fairly close to reality.

However, these leads can easily take 3-months to 2-years to convert. In this scenario, Data-Driven Attribution is ideal.

Keep in mind, with Google Analytics you're limited to a 90-day 'look back' window. Visits that happen outside the 90-day cannot be attributed.

For greater flexibility with conversion windows, you'll have to stream your GA4 data into BigQuery and create attribution dashboards in DataStudio. Go to Admin > Property > BigQuery links.

When you connect GA4 to BigQuery, if you haven't already got a Google Cloud Platform account, you'll be asked to create one and then add your billing details.

Wrapping Up

GA powers web analytics for MILLIONS of businesses around the World. But most are not enjoying it's full benefits.

To access Google Analytics powerful attribution models, you need Ecommerce configured with data being fed in the correct format. Alternatively, you can setup conversion goals... just make sure you're sending revenue data with the events.

Google Analytics 4 offers the more accurate Data-Drive Attribution Model. But GA4 doesn't allow you to create custom attribution models.

Universal Analytics has a more powerful Model Comparison Tool. It's especially useful for analyzing CPA and ROAS. This gives you Wicked Reports or Hyro-like reporting powers! The only drawback is the lookback window is limited to just 90-days.

For the time being, I find myself using both tools.

For sales cycles that take significantly longer than 90-days, you can enable the native integration between GA4 and BigQuery, then analyze your data with python and Data Studio.

Need Help?

Feel free to Schedule a call to discuss your needs with any of the following:

• Setting Google Tag Manager browser & server
• Getting clean data out Shopify, Squarespace, Wordpress, Woocommerce, Typeform, Thrivecart, Samcart, etc
• Configuring Google Analytics e-commerce, goals, custom reports
• BiqQuery & DataStudio data analysis

Related

Add Google Tag Manager to Shopify

Google Ads Enhanced Conversions (or GEC) is Google's version of Facebook CAPI. It uses hashed user data to capture conversions that would otherwise be lost due to ad tracking-blocker technology.

In this article, you'll learn the 4-steps to implement Google Enhanced Conversions with Google Tag Manager Server-side. I recommend avoiding the  Global Site Tag method because it's a browser-based solution (read more about this below).

Prerequisites

Before attempting this method, make sure you have the following already set up:

Google Tag Manager web container with tags:

1. Google Analytics 4 configuration
2. Google conversion linker

Google Tag Manager server-side container with tags

1. Google Ads conversion tracking tag
2. Google conversion linker

A little background around why solutions like Enhanced Conversions are growing increasingly important...

The iOS14 Debacle

Recently, we saw the advent of iOS14 and its new privacy protocols, blocking all 3rd-party cookies and shortening the expiration time of 1st-party cookies to just one week.

Apple further stipulated that Facebook had to limit the extent of events and data extracted from users on iOS and Safari devices.

Apple had two main reasons for its actions:

1. Facebook makes a LOT of money from Apple's users' data (and wasn't giving Apple a kickback). Similarly, Google misses out on massive potential revenues from data Facebook gets from Android devices.
2. People were gradually waking up to the fact that their personal info, browsing habits, location, and purchasing behavior were being passed onto goodness-knows-who to do goodness-knows-what.

Look to the "Cambridge Analytica" saga to get a sense of just how user data can be misused.

This set in motion a series of dominoes that are seeing the likes of Android and browsers like Brave, Opera, Firefox, and even Chrome starting to take similar stances on privacy by tightening up the data available to third-party trackers.

What this means for digital advertisers is it's getting harder to know what's really going on with marketing campaigns.

Insights from Facebook CAPI

The way Facebook handled iOS14 is interesting because it was the first to come up with a comprehensive response to iOS14... probably because they were the most affected and stood to lose the most.

Conversion API had been available for 2-years prior, but most advertisers stuck with the Facebook Pixel because it was MUCH easier to set up.

CAPI, a server-to-server tracking solution involves passing hashed personal info such as name, email, mobile, zip, city, state, country, and user-agent, to create a redundant conversion data stream in case of having conversion tracking blocked in users' browsers.

Based on results we've seen from clients, we estimate around 17% or more of data is blocked in browsers and devices, and FB CAPI does a solid job of filling in a good chunk of these missing pieces.

Server-Based vs Browser-Based Tracking

iOS, Android, browsers, and ad blockers will continue to tighten up privacy by blocking connections between users' devices and external 3rd-party trackers... likely due to increasing pressure from pro-privacy groups who eagerly litigate the tech giants when they overstep the mark.

Apple, Android, Brave, Opera, and other platforms harness the power of Machine Learning to detect tracker footprints and then start blocking them. So over time, we can expect browser-based tracking solutions to become less and less reliable.

However, these same technologies will not block the devices from sending data back to the same root web domain that a user is visiting. The reason for this is devices and servers need to be in regular communication for modern websites to function correctly. If this behavior stopped, then probably 80% of all websites would stop working!

Herein lies the power of server-based tracking... by sending customer data and transaction data to a server on your root domain (e.g. sgtm.yourdomain.com), you can forward that same data to any endpoint of your choosing, including Google Ads, Facebook Ads, Tiktok Ads, Pinterest Ads, Universal Analytics, Google Analytics 4, and more.

For the time being, this type of tracking technology can't be blocked.

Why GEC with Global Site Tag is Kind of Pointless

You could theoretically implement Enhanced Conversions using GTM browser OR Global Site Tag if you really wanted to. Both are possible. But, for the aforementioned reasons, doing so is kind of pointless.

Remember, any tracking technology that runs in the browser can easily be blocked, which means you'll lose ever more conversion measurement data.

In short, Enhanced Conversions MUST run from the server in order to do it's job properly (and Google Tag Manager Server-side is an excellent way to do this).

Reliable Data Matters

There are two COMPELLING reasons for going to lengths to collect complete, reliable data.

The first is reporting. When blending data from ads, CRM, shopping carts, and other sources, the more accurate your reports and dashboards will be. In short, you'll be clearer about what's happening in your growth engine.

Second, the Ad Platforms' A.I. that auto-automizes your ads all require CLEAN data.

Junk in ==> junk out.

The better quality signals you give the robots to work with, the better results they will deliver in the form of quality traffic.

Why Google Released Enhanced Conversions

I watched bemused as Facebook tore it up in the process of rolling out Facebook CAPI. At the time, it was obvious that they panicking.

iOS14 basically turned the ad world upside down!

I had in-depth discussions with several high-rolling digital media buyers, trying to get a bead on what iOS14 and CAPI would eventually mean for advertising.

Meanwhile, we didn't hear a peep from Google. It's almost as if they didn't care.

Somehow, we thought, Google must have had this sewn up with their World-class predictive modeling... and from what we saw, Youtube Ads appeared to be far less affected than Facebook, even with advertisers running CAPI.

But as the months passed by, we saw CAPI improve. Probably because it took a while for their Machine Learning to adapt to the new data sets.

In the past, if you wanted to send server-side conversion data you'd have to go through the hassle of applying for Google Ads API access.

So, it was a nice surprise to finally see Google Ads roll out their own GTM version of CAPI with Enhanced Conversions.

How Google Enhanced Conversions Work

Similar to Facebook CAPI, Enhanced Conversions hashes customer data- email address, phone, first name, surname, street, city, region, country, postcode and combines it with IP address, user-agent, and other data from Google Analytics 4.

It shouldn't surprise us to hear that Google cross-references user account data across gmail, Youtube, Workspaces, and hundreds of other product accounts in order to attribute a conversion action back to a specific Google Ad.

As far as we know, because the data is hashed using the SHA256 algorithm, theoretically user personal info is protected under GDPR and CCPA laws.

Personally, I'm highly skeptical about their claims... My theory is that Facebook and Google have the smarts to know who a specific user is, regardless of whether personal data is encrypted or not.

4-Steps to Implement Enhanced Conversions using Google Tag Manager

As I mentioned at the beginning, this article is going to get REALLY long if I have to explain how to set up Google Tag Manager, GTM Server-side, and Google Analytics 4. I'll link to each topic as I create a separate in-depth article for each one.

Now that you understand why we're doing things the way we are, let's go through the actual steps. It's quite simple as long as you're already collecting data from your system and storing them as data layer variables in GTM.

Here's a basic overview of what we're doing:

Turn on enhanced conversions in Google Ads dashboard

Collect user provided data

Send data from GTM web container to GTM server container

STEP 1. Google Ads Conversion Settings

Login to your Google Ads account - https://ads.google.com/

In the top navigation, click "Tools & Settings".

Under the "Measurement" submenu, click "Conversions".

Then click the link for the conversion you want to enable for Enhanced Conversions.

Scroll to the bottom and toggle the option labeled "Turn on enhanced conversions" and choose Tag Type "Google Tag Manager".

The system will ask you to enter the URL where you have Google Tag Manager installed.

Input your URL and click the "Check URL" button. Then Google will go check and verify that you have a valid GTM setup.

If successful, you'll see the following message in a light blue box, indicating you have Google Tag Manager installed.

Once that's done, it's time to feed user provided data to Google Ads.

STEP 2. User-Provided Data Variable

Google stipulates this the minimum data required to make Google Enhanced Conversions work:

Phone number (must be provided in addition to one of the other two pieces of information above)

Email address (preferred)

Name and home address (street address, city, state/region and postcode)

The upshot is, you must have the phone number, plus the email address and / or the full home address (street, city, state, country, postcode).

If you're missing the minimum dataset, the conversion will fire as it does normally, but you won't get the additional benefit of GEC matching events based on personal customer data.

Login to Google Tag Manager account - https://tagmanager.google.com/

If you don't already have them in your GTM web setup, you'll need to create data layer variables for email, phone, first name, surname, street, city, region, country, and postal code. My customer data layer variables look like this:

Click the "Variables" link on the left side panel.

Under "User-Defined Variables" heading, click the "New" button.

Give the tag a meaningful name, indicating that it's to do with Google Ads and also "user-provided data". The naming convention for tags, triggers, and variables is surprisingly important. It keeps GTM containers organized and helps with future maintenance. So try to be consistent!

Click "Variable Configuration" to reveal a dropdown menu.

In the top-right search box, type "user-provided data". As you begin typing, you'll see the variable appear in the search results. Click to add this variable type to your GTM browser container.

Next, click each box for email address, phone, first name, etc, and add the corresponding data layer variables from the dropbox list that appears. Note - the "Region" field expects state or province in capitalized 2-digit format (e.g. Arizona is represented by AZ).

If you're having trouble saving the changes, it's because the User-Provided Data variable expects a value for ALL of the variables. It won't save unless all of the fields contain a value.

At this point, you've created a special variable containing all of the parameters Google Enhanced Conversions needs in order to attribute events to the right source.

Now, we must somehow get this data to Google Tag Manager Server-side and the easiest way to accomplish this is with Google Analytics 4.

STEP 3. GA4 Tag Settings

Click "Tags" in the right sidebar. Then click and open the Google Analytics 4 configuration tag.

Toggle the checkbox labeled "Include user-provided data from your website".

From the dropbox menu select the variable User-Provided Data variable you just created in STEP 1 and click "Save".

So far we've completed all the necessary steps for GTM Browser-side. Finally, we have one last option to configure in Google Tag Manager Server-side.

STEP 4. GTM Server-Side Google Ads Tag Settings

Login to Google Tag Manager Server-side container, click the "Tags" link in the left side panel, and open the Google Analytics conversion Tag.

Toggle the checkbox labeled "Include user-provided data".

Conclusion

To summarize, Google Enhanced Conversions is relatively quick and simple to set up as long as you already have Google Tag Manager Browser and Server-side, along with GA4 and Google Ads tags properly configured.

If you've just started down this path of server-side tagging, congratulations!

You're well on your way to improving ad reporting and ad optimization. You've also taken some important steps along the way to building your own first-party customer data platform.

Next Steps

If you need help with any of these steps, drop us a line. We have a team of professionals whose sole job is to solve tricky conversion tracking issues for Google Ads, Facebook Ads, Shopify, Google Shopping, Wordpress, Woocommerce, Woofunnels, Squarespace, Clickfunnels, and more.

In this article, I share 5-steps to add Google Tag Manager to Shopify. This should take 5-minutes or less to complete.

(Note – In this article, I’m not covering how to set up marketing tags for Universal Analytics, Google Analytics 4, Google Ads, Facebook Ads, and other integrations. To keep this brief, I’ll cover these related topics in subsequent articles.)

Why Use GTM on Shopify?

If you want to run profitable digital marketing campaigns for your Shopify store, you must track every meaningful step of your checkout process.

This helps you understand the effectiveness of individual marketing campaigns… customer journey… customer lifetime value… ROI of your various products… content effectiveness… and so on. As a result, you can learn where exactly to focus your resources so you grow your business.

Basic Shopify comes with some analytic capabilities called Shopify Analytics. This displays purchases, revenue, refunds, and even UTM data for referral traffic from other sites.

Universal Analytics is also a native Shopify integration that includes Enhanced Ecommerce Tracking. But Shopify only sends this conversion data to Universal Analytics and not to Google Ads.

Also, if you’re running Facebook ads, there are currently major issues with Facebook Pixel and Facebook CAPI not de-duplicating properly (hundreds of store owners complaining about this on Reddit)… so you end up double-counting conversions. This in turn screws up the reporting and profitability of your ad campaigns.

Lastly, there’s still no support for Google Analytics 4 – Google’s much improved Google Analytics product.

This means if you’re relying on Basic Shopify default features, you’re likely missing out on many profit-improvement insights.

Google Tag Manager (or GTM for short) unlocks the full power of your conversion tracking & analytics power of your ecommerce data. The only downside is, you normally have to upgrade to Shopify Plus, starting at $2,000 per month. (Fine if you’re an enterprise customer but hard to justify for smaller stores.)

The good news is, our solution doesn’t require Shopify Plus. You don’t even need a Shopify app for this to work.

So now we’ve covered the ‘why’ let’s go over how to set it up:

5-Steps to Install Google Tag Manager on Shopify

Step 1- Create a Google Tag Manager Container

Head on over to https://tagmanager.google.com/ and click Create Account to create a new Google Tag Manager account (if you don’t have one already).

Give your account an appropriate name. for example, your business name.

Select your country.

Name your container. I normally use the domain name of the store as a name.

Then choose “Web” as the target platform.

Step 2 – Get Your GTM Code Snippets

Once you accept Google’s terms (if this is your first time using Google Tag Manager), you’ll see the dashboard for your new web container.

You will also see a popup containing two code snippets… one for the <head> and the other for the <body>.

Copy and paste these into a notepad. We’ll use them in just a moment.

Step 3 – Add GTM to Shopify Liquid Theme File

Now that you have your GTM head and body snippets, it’s time to add them to your Shopify store.

To do so, log in to your Shopify account.

In the left-hand sidebar, click the “Online Store” link.

Click the “Actions” button, and choose “Edit” to open the Shopify theme editor.

The theme.liquid file should open by default. If it doesn’t, click the link in the left side panel labeled “theme.liquid“.

There are two places to paste the code snippets you grabbed earlier. Note – you have a <head> snippet as well as a “body” snippet. Each one must be pasted in the right place.

Paste the code snippet just after the opening <head> tag. This code actually loads Google Tag Manager.

Now, scroll down until you see the opening <body> tag and paste the second code snippet (see the blue outline in the image below). This handles the scenario when a user’s browser doesn’t allow Javascript to run.

Click the green “Save” button to update your Shopify theme.

Step 4 – How to Add Google Tag Manager to Shopify Checkout?

Now the GTM container code will load on all of your main pages.

At this point your setup is capable of tracking e-commerce events such as Page View, View Item, Add To Cart and Checkout, but NOT Purchase completion… unless of course, you’ve upgraded to Shopify Plus.

As a result, we have one more step in order to enable event tracking for Purchase actions.

This requires adding the GTM code snippet to your “Checkout Settings”.

Let’s go there now.

First, click the “Settings” link at the bottom of the left-hand sidebar of your Shopify admin.

Click “Checkout”.

Then scroll down until you see the section labeled “Additional Scripts”.

Paste your <head> code snippet in the box provided.

Step 5 – Verify GTM Snippet is Installed Correctly

Finally, you need to check everything’s working correctly.

In your Google Tag Manager container dashboard, click the “Preview” button in the top right-hand corner of the page.

Enter the URL of your store in the Tag Assistant popup that appears.

Now, in the bottom right-hand corner of your screen, you should see an overlay box displaying the word “Connected”. This indicates the correct Google Tag Manager tag is found installed on your site.

Following these 5-steps, you should have your Google Tag Manager installation loading in your Shopify website in just a few minutes.

In future articles, I’ll go into detail on how to use Google Tag Assistant to debug your setup. This is essential for getting all of your conversion tracking, analytics, and other integrations working correctly.

Next Steps

Installing the GTM code on your Shopify pages is just the beginning. Next, you need to consider:

• The conversion events involved in your checkout process, including Page View, Add to Cart, Checkout, Purchase, Email Registration, etc.
• The transaction data you need to know about each action including product name, product id, number of items, category, price, tax, shipping, transaction id, etc.
• The platforms you need to send data to that may include any of the following… Universal Analytics, Google Analytics 4, Google Ads, Facebook Ads, Taboola, Outbrain, Pinterest Ads, Twitter Ads, Mixpanel, Hotjar, and custom HTML .
• The tracking codes and marketing tags, along with the triggers and variables that you need to set up.

If this all feels overwhelming, don’t worry! Rome wasn’t built in a day.

First, get the GTM code installed. Next, get Google Analytics and Enhanced Ecommerce working via Google Tag Manager, along with all of your events showing up properly in your Google Analytics Account.

Then you can add other tracking tools and integrations, as you need them.

Conclusion

We’ve covered a lot here. Hopefully, it all makes sense!

You might find the 5-steps covered here easier to follow if you check out the accompanying Youtube video.

If you have any questions please leave them in the comments below.

Also if you’ve found this content valuable, please like and share. It helps others who are struggling to improve their Shopify tracking too. Thanks!