Tag Archive for: google analytics

More Data, Better Results: How We’re Leveraging Google Analytics 4 To Drive Growth

Working in Google Analytics 4

Whether it’s optimizing paid spend, identifying the right channel mix, updating the user flow to improve CVR, pulling a creative analysis, implementing technical SEO fixes, or more, as a growth marketing agency, our entire team is in Google Analytics on a daily basis. So as we prepare to say goodbye to our longstanding and trusted analytics sidekick, Universal Analytics, the team at Tuff has been busy testing the new Google Analytics 4 platform with a variety of our partners. 

In this blog, I’ll take a look at a few ways we’ve started to leverage Google Analytics 4’s new or updated features to gain analytics insights to drive better learnings and key results for our partners. 

Customize Your Reports To Find The Data That Matters Most

We’re not going to lie, Google Analytics 4 seems very cumbersome at first. For example, there are ‘bread and butter’ reports, like the ‘source / medium’ report from Universal Analytics that are no longer configured for you from the start. Little things like this make the transition to Google Analytics 4 a bit more painful than it probably needed to be. 

But, one area where Google Analytics 4 really shines is in the ability to customize reports and visualize data in a much easier and simplified way when compared to Universal Analytics. Head to any of the initial reports created on the left hand menu, such as the Acquisition Overview report, and you’ll see a few pre-populated graphs, charts, and cards. 

While the initial layouts of these reports are great to start, Google Analytics 4 allows you to go a bit further by customizing each report to your liking by adding any number of dozens of pre-made cards to make the report more efficient and useful for your specific needs. 

As an example below, I’ve added two new cards, ‘Conversions by Source/Medium’ and ‘Conversions by Device Category’ to my Acquisition Overview report. As a PPC Strategist driving paid traffic to a website, these are two metrics that I care about greatly, so adding these as cards to my Acquisition Overview report will allow me to find these metrics as quickly as possible without needing to go further into other reports. 

custom google analytics 4 report

Customizing reports with additional cards is simple, but so is creating entirely new reports to find the information most important for you. 

In the example report below, we’ve created a report detailing repeat purchasers by the campaign that brought them to the site and the length of time it took to make a repeat purchase. This information is particularly useful for understanding which campaigns are creating the most ‘sticky’ customers – those who are willing to come back to our partner’s site and purchase more than once. 

It took only a few minutes to create this report, and by adding it to our reports section via the Library feature, we’re able to quickly find and interpret this information within Google Analytics 4, thus enabling us to act and allocate our ad spend accordingly to the campaigns that are driving the most repeat purchasers:

google analytics 4 campaign report

Act Efficiently With The New & Improved Insights Tab

ga4 insights tab

One of my personal favorite features of Google Analytics 4 is the new and improved Insights tab. First released in Universal Analytics, the Insights tab has gotten a new facelift in Google Analytics 4 and is offering a simpler, more efficient way to find important insights to help drive marketing decisions.

By simply opening the Insights tab, we’re presented with a few pre-configured sections to help spark our data investigation and find key information driven by Google’s AI, including: 

  • Basic Performance
  • Demographics
  • User Acquisition
  • Traffic Analysis
  • Technology
  • eCommerce

But, it doesn’t stop there. To find the exact measurements we’re looking for, we can type in a quick sentence to find the relevant data quickly. 

The key benefit of these insights is efficiency – while you may be thinking of data that you want to find, many times you may be unsure of the best way to configure your reports to find this data, especially when using a new platform like Google Analytics 4 that requires a learning curve. Instead of setting up a new report or editing an existing report, hop up to the search bar and type in exactly what you are looking for and you may just be surprised at how efficient and easy it is to use this enhanced Insights feature.

By typing into the search bar exactly what I wanted to see – in this case, ‘revenue and transactions and users by channel last month’ – I’m presented with a clear table that I can view in platform, share a link to, or download in PDF, PNG, or CSV format to share elsewhere. 

This insights tab has proven particularly useful early on when learning Google Analytics 4 as I’ve been able to find the most relevant information quickly, without needing to edit or configure existing or new reports. 

Conversion Paths to Determine Where Your Budget Should Be Allocated

Conversion paths and model attribution is one feature of Universal Analytics that all growth marketers love to use when looking to find a clearer picture of how their various marketing efforts are interacting with each other and contributing to traffic and revenue, especially as channel diversification becomes more and more important. Luckily, Google Analytics 4 has kept this feature and, in my opinion, improved it further by making it easier to use and more insightful, allowing growth marketers to more efficiently find the insights to their user paths that are most valuable in order to take this information and act on it.

Now living in the new ‘Advertising’ section and under the ‘Attribution’ dropdown, the Conversion paths section provides us with a clear look at which channels, sources, mediums, or campaigns are being credited at various touchpoints along the user journey. Take the following example below which shows the various touchpoints of the user journey for purchases for an eCommerce partner:

ga4 purchase flow

How can we use this information to drive better marketing decisions? 

First, what we can notice right away is that the Paid Social channel gets the most conversion credit out of any channel in the early touchpoints. However, as we get later into the user journey, Paid Search becomes the channel that is gaining the most conversion credit.

This means, for this example, that the Paid Social channel is highly important for our top-of-funnel marketing efforts. Users who later make a purchase are coming to the site first from Paid Social more than any other channel. So, it’s critical that we continue our top-of-funnel marketing efforts on this channel, and if possible, look to expand here to introduce the brand to more people via Paid Social since we know that these users are likely to convert.

On the other side, Paid Search is receiving the most conversion credit for late touchpoints. For this example, this means that when users are ready to make a purchase, they are turning to Paid Search to find the site and make the purchase. Many times, we see the final touchpoint be from a Branded Paid Search campaign, as users are searching for the brand name on Google to be taken to the site and make their purchase. 

Additionally, Email is playing a crucial role across all steps of the user journey. We can see Email as a top three channel across the early touchpoints, mid touchpoints, and in particular at the late touchpoints. Email is proving to be incredibly important in nurturing the user along the journey from initial interaction to final purchase.  

Universal Analytics 3 Says Goodbye in Less Than a Year

Although you may be inclined to push your Google Analytics 4 transition to as close to the cutoff date as possible, we recommend familiarizing yourself with the new analytics platform as soon as possible to begin enjoying some of the new features and benefits. Play around with the new layout, test and configure new reports, and you’ll soon find ways to enhance your marketing efforts in conjunction with Google Analytics 4 like some of the ways detailed above.


Ecommerce Store

How to Use Google Analytics to Discover Lookalike Audiences

Ecommerce Store

Do you remember where were you on the day that the digital advertising world stood still? Do you remember what you were doing when iOS14.5 loaded onto scores of devices overnight before their owners even reached onto the nightstand to grab them and open up their social media app of choice? Our growth marketing agency jumped into the brand new world of paid social advertising and have taken away a few key learnings for the post iOS14.5 world. 

The Fallout of iOS14.5 

On April 26, 2021, Apple followed through on its promise to prioritize privacy with App Tracking Transparency. This update prompted users to either “allow” or “don’t allow” apps on their iOS devices to track their activities. 

Paid social advertisers in particular felt the blow like they were brave enough to step into the ring with Mike Tyson in his prime. Much of the targeting that advertisers had become so reliant on was either gone or no longer effective. Despite Apple and Facebook (now Meta) warning for months: so many marketers were drastically affected. 

Sure with the change, you could still target Android users, and Facebook did roll out new aggregated events to limit the fallout Some damage control measures went into place, but it goes without question that Apple had delivered a near-fatal blow. So, scrappy Facebook advertisers have had to look for new ways to find their target audience online. 

From that scrappiness, new audience targeting strategies emerged that could help social ads strategists and media buyers cut through the crowd and reach their audience. At our growth agency, we’ve identified two lookalike audiences that have proven to be super valuable. Even better, they’re accessible to anyone with Google Analytics installed on their website.

The First Lookalike: Pageviews 

The first audience we’ll discuss today is a lookalike audience based on pageviews. 

Imagine you’re looking at your analytics and you see that you have 347 transactions this week for a product. For a lot of advertisers, they either stop there or ask the question, “How can I get this number higher?”  before they close the tab and move on. Scrappier advertisers take it a step further.

Looking at Google Analytics’ acquisition report we can see the traffic generated from various channels and campaigns.

If you add in a custom segment of Purchasers, you can look at the metrics based on people that have made a purchase on the website. From there we want to know the pages per session.

Pageviews for Google Analytics Purchasers

In the case of this e-commerce company, the Purchasers had a drastically different behavior than the larger pool of all users. And this is where the beauty is.

From the data, we can tell that Purchasers visited 11.47 pages per session. They were more engaged. They clicked around, they viewed a few pages, and they inevitably visited the cart followed by the checkout page. 

We can take an educated guess that a cohort of users with that same behavior pattern would be likely to purchase as well. And that’s what we’re betting on for this first lookalike.

So back in Facebook (still not used to Meta), we’ll make our way to the audiences module within the business manager. Here we’ll be creating a custom audience.

Overview of Meta's custom audience website tool

To do this follow the following steps:

  •  Choose Website as your custom audience source. 
  • Choose the pixel you want this based off of
  • Instead of selecting all website visitors, select PageView from the events dropdown. 
  • Click the Refine by dropdown below, and select frequency. We’ll leave the middle drop-down as “is greater than”….
  • Change the number to the number of pages per session the Purchasers normally have minus 10-20% (so, if it’s 10 pages, then make it 8). We’re subtracting 2 or 3 because it gives Facebook a buffer so that they can have a large enough audience to build the audience from. 
  • Click Create audience, and then create a lookalike from that audience.

The Second Lookalike: Time on Site

The second lookalike audience uses analytics as well. Remember that the Purchasers segment spent around 11 minutes on the site. So again, we can safely assume that Purchasers spend more time on the site.

So the second audience we want to create is a lookalike based on time on site. To do this, create a new custom audience. This time instead of choosing PageView from the dropdown of events, we’ll select Visitors by time spent. 

 For the percentile, start by leaving it at 25%. If the data suggests otherwise in the future, then you could always drop it to 10% or 5%.

Be warned though. As you move to higher percentiles the audience will shrink even more. Once you’ve created this custom audience, go ahead and create a lookalike based on this one too.

Overview of Meta's Site Visitors Percentile Tool

The Real Trick to Using Analytics to Build Facebook Audiences

And just like that, you have two audiences based on data that you could target using your first-party data. And this is just the beginning. There is a myriad of different audiences you could build from the data. 

You could narrow the custom audience further by switching the parameters from Include people who meet “Any Of” the following criteria to Include people who meet “All Of” the following criteria.

You could create custom events and conversions in your pixel based on events that are important to you and build an audience based on their behavior.

You could select specific web pages to narrow your time-on-site audience. 

The possibilities are vast. The important thing is learning how to use analytics to understand your audience beyond the bottom line numbers. From there, you can segment your audience in the way that makes the most sense for your business goals.

Looking to leverage your existing data for your ecommerce site? Our growth agency is happy to dive in and work with you on a growth strategy. Let’s chat

pulling a report from google analytics

How to Drive Better Strategic Decisions Using Google Analytics’ Attribution Models

pulling a report from google analytics

The way the majority of marketers use Google Analytics’ attribution models to make strategic decisions is broken. I’m talking, “holy cow, this has major implications on our bottom line”, broken.

If you’re making major decisions regarding the allocation of ad spend, or trying to measure the success of a campaign and are using Google Analytics’ default reporting, you’re going to want to read this. 

What is Google Analytics’ Default Attribution Model? 

Google Analytics defaults to a Last Click attribution model for most of its reports (and the key word here is most). Last click attribution gives 100% of the credit to the last source, or campaign a user came from prior to converting. 

Some reports, such as Google Ads breakdown under the “Acquisition” report defaults to a Last Non-Direct Click attribution model. What this means is that Google Analytics will ignore all direct traffic and give 100% of the credit to the last channel a customer clicked through prior to returning via direct traffic. Last Non-Direct click is a preferred attribution model over Last Click for many marketers because most direct traffic has had some sort of interaction with your brand prior to coming to your site. 

What are the problems with using Last Click attribution modeling? Don’t I want to know what made my target audience convert?

It’s true, Last Click, or Last Non-Direct Click can be insightful for measuring the final tactic that caused a user to convert. However, this is an incomplete measurement of the full aspect of the marketing funnel. Chances are, your target audience didn’t convert out of the blue. 

Here are some of the limitations of using Last Click or Last-Non Direct Click attribution modelling: 

  • Can give a distorted view of what is actually driving your target audience to your brand in the first place
  • Doesn’t account for multiple touchpoints in an advertising funnel
  • Silos data and gives 100% of credit to one channel or tactic
  • Doesn’t show how multiple channels and tactics interact with one another
  • Shows an incomplete customer journey

Take this real-client example: if you see that email marketing is accounting for 30% of your e-commerce purchases, and paid social is driving very few last-click purchases (but a lot of email signups!), you wouldn’t want to stop running the social ads that are leading to email signups. In doing so, you’d be shutting down two acquisition channels at once. 

The last-click attribution model is flawed, and doesn’t take into consideration that a customer today has to nurtured to make a conversion. 

Alternatives to Last Click Attribution Modelling

Luckily, Google Analytics offers several attribution models that marketers can use to get a more complete picture of their conversion efforts. There are several ways to do this. 

Want to watch, instead of read? Check out this video! 👇

First, under the Multi-Channel Funnels report, you can select “Assisted Conversions” report and view how many of your conversions were multi-touch. Selecting the “Source / Medium” breakdown allows you to view how different channels function: either as more of an assisted tactic, or a direct tactic. 

assisted conversions in google analytics

A value closer to 0 in the final column means it’s a primarily final conversion tactic. If the value is close to 1, the channel operated equally as a direct and assist tactic.  If the value is over 1, it means the platform assisted in more of an “assist” role. 

Another favorite tool to compare Google Analytics’ Attribution Models is the Model Comparison Tool. At Tuff, we often use the “Last Interaction” vs, “First Interaction” report to identify demand generating campaigns and platforms. 

Google Analytics’ Attribution Models

In this report, we see that Facebook and Google ads have a 20% and 47% increase in attributed conversions when using the first-click attribution model. Google Analytics’ first interaction attribution model gives 100% of the credit to the first source a user interacts with before converting. 

A savvy marketer can also apply a secondary dimension to view campaign filters applied to dig into which ad campaigns on specific platforms are driving the majority of the initial interest in their brand and product. 

Using Google Analytics’ Attribution Models to Make Strategic Decisions

As you’ve probably guessed by now: the answer for how to use Google Analytics’ many different Attribution Models (we haven’t even touched on Time Decay, Linear, or Position Based models, or how to measure via Zero Party data) is not a “one size fits all” solution. 

Instead, we recommend comparing the different models, identifying what campaigns and channels are generating demand and interest for your products, and leveraging different optimization tactics to drive revenue for your brand. It takes more time to compare models, but the savings can be immense. Ready to see how Google Analytics’ first-click attribution modeling can unlock major demand generation wins at the top of funnel for your brand? Drop us a note.