Google Ads: The Introduction

Here’s what we’ll cover

Google Ads is a complex tool that allows advertisers to grow their business and reach more customers.

Google Ads help your business bring in new customers or leads through search ads, display network ads, and YouTube ads.

And it seems pretty simple…

But Google Ads can be tough to navigate.

Companies pay money to Google based on clicks to show up, in hopes of capturing interested buyers and generating leads or sales.

Google makes money from the advertiser every time someone clicks on an Ad.

Pretty simple right?

What is The Quality Score and How Does it Work?

The quality score is a crucial component of running a successful Google Ads campaign.

It’s made up of three main factors:

  1. Your ad campaign’s landing page: How quick does your landing page load? What’s the conversion rate? What’s the bounce rate? In short, is Google sending the people who click to a website that seems to address their needs?
  2. Expected CTR: How do your ad campaigns typically perform? Is your click-through rate better or worse than the average?
  3. Ad Relevance: How specific is your ad in relation to the search?

Why is quality score important beyond ad rank?

Improving your quality score is a great way to rank higher with your ads without having to bid higher. You can reduce CPCs and improve your performance by focusing on those three factors that make up the quality score.

Types of Advertising on Google Ads:

You can choose between four different ways to be found by a given searcher:

  1. Search Ads
  2. Display Ads
  3. Video Ads
  4. App Ads

Currently, you can show up on display ads, video ads, search network ads, and application-based ads in Google.

The search network is the most popular of all.

Search network ads show up as a text ad for a given Google search.

The search network works by targeting specific keywords that you want to show up for.

You bid on them to show up higher and get a better chance at capturing visitors and converting paid traffic.

Next, we have the display network.

Display ads work as text or banner ads and can show up on Gmail and various websites within the display network.

Businesses commonly use them for re marketing to bring back site visitors who didn’t convert.

If you’ve ever noticed an ad on a website, it was likely from the display network.

Video-based ads allow you to create a video ad that will show up on YouTube videos.

Lastly, you’ve got the App Ads that allow you to advertise on popular Google network-based applications.

Currently, the most popular forms of advertising tend to be: search network and display-based.

They are easy to set up with a relatively little amount of work and no video production required.

If you are interested in showing up for popular searches in your industry and getting new consultations or sales, the search network is a great place to do it in Google.

Link/unlink Google Ads and Analytics

Linking your Google Ads account to your Google Analytics property lets you see the full customer cycle, from how users interact with your marketing (e.g., seeing ad impressions, clicking ads) to how they finally complete the goals you’ve set for them on your site (e.g., making purchases, consuming content).

Albert Flores

President of Sales
The Walt Disney Company

About the Author
Albert Flores is a seasoned accountant with over 15 years of progressive experience in senior finance and accounting across multiple industries. Jason holds a BBA from Simon Fraser University and is a designated CPA. Jason’s firm, Notion CPA, is an accounting firm with a business.

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Mastering Calculated Metrics in Google Analytics 4


Google Analytics 4 (GA4) recently announced a new feature, known as “Calculated Metrics” that enables users to create metrics by applying mathematical operations to existing metrics. This article will provide you with a comprehensive guide on how to create calculated metrics in GA4, address common questions about this feature, and offer practical use cases.

What are Calculated Metrics?

Calculated metrics are, as the name implies, metrics derived from existing metrics or custom metrics. This concept is akin to calculated fields in popular Business Intelligence tools like Looker Studio and Tableau. For those who’ve transitioned from the older version of Google Analytics (Universal Analytics or UA), you’ll find the process of creating calculated metrics in GA4 quite similar. Much like UA, GA4 also imposes a limit of 5 calculated metrics per standard property.

How to Create Calculated Metrics in GA4?

Step 1: Login and Navigate to the “Admin” Section

To begin crafting calculated metrics in GA4, log in to your account, and navigate to the “Admin” section.

Step 2: Access Custom Definitions

Under the “Admin” section, locate and click on “Custom Definitions.”

Step 3: Enter Calculated Metrics

In the “Custom Definitions” section, you’ll find a dedicated tab for “Calculated Metrics.” If it’s not visible immediately, be patient – it may take a few weeks for the property to be updated.

Step 4: Begin Creating Your Calculated Metrics

Key Fields to Define:

  • Name: Provide a unique and descriptive name for your metric, as it will be visible in your reports and explorations. While you can’t change this name later, just kidding – you can!
  • API Name: This name is automatically generated based on the metric’s name and cannot be altered post-creation. Choose wisely.
  • Description: Include a brief description to help you recall the metric’s purpose. Even though it might be tempting to skip this, it’s a helpful step.
  • Formula: Define the mathematical formula for your metric using either
    • Predefined metrics or custom metrics. You can find the list of predefined metrics here.
    • Numbers (including decimals)
    • Accepted operators (+, -, *, /). 
  • Unit of Measurement: Select from options like Standard, Currency, Distance, or Time.

Example I – Revenue (NoTax) 

Name: Revenue Without Tax

Description: Revenue without tax

Formula: {Purchase revenue} – {Tax amount}

Formatting Type: Currency

Example II – Blog Views per User

Name: Blog Views Per User

Description: Number of times a blog post was viewed per user 

Formula: {Blog Views} / {Total users} (Assuming you have a custom metric for “Blog Views”)

Formatting Type: Standard

Top 5 Calculated Metrics in GA4

While the specific metrics on this list may vary depending on the nature of the business, for the sake of simplicity, let’s consider an e-commerce enterprise. As a business analyst, these five calculated metrics play a pivotal role in facilitating the decision-making process.

  1. Cart Abandonment Rate – This metric reveals the percentage of users who add products to their cart but don’t complete the purchase. Reducing cart abandonment is crucial for increasing sales.

Formula – (({Add to carts} – {Checkouts}) / {Add to carts}) * 100

  1. Checkout Abandonment Rate – Similar to cart abandonment, this metric measures the percentage of users who start the checkout process but don’t complete it.

Formula – (({Checkouts} – {Purchases}) / {Checkouts}) * 100

  1. Refund Rate – This metric would be crucial to identify products that have the highest refund rate. 

Formula – ({Refunds} / {Purchases}) * 100

  1.  First time Purchase Rate – Helps you measure the percentage of customers who make their first purchase with your website or app during a specific time period. Calculating this rate can provide insights into your ability to convert new visitors into paying customers. Split this by the traffic sources and you will have found the best way to get new purchasing users.

Formula – ({First time purchasers} / {New users}) * 100

  1. Customer Acquisition Cost (CAC) – It is the cost of acquiring a new customer, including marketing and advertising expenses. To calculate this, you’ll need to upload cost data to GA4 if you’re using external marketing platforms like Facebook or TikTok.

Formula – ({Non-Google cost} + {Google Ads cost}) / {First time purchasers}

Difference Between Custom Metrics and Calculated Metrics

Custom metrics involve creating metrics from event parameters or user properties, while calculated metrics use mathematical operations on standard GA4 metrics to generate new metrics.These new metrics often contain business logic, enhancing decision-making in Google Analytics 4.

Calculated metrics can be accessed in various reports, explorations, and via the Google Analytics Data API.

Prerequisites and Limits

To create a calculated metric, you need to be an Administrator or Editor.

You can create up to 5 calculated metrics per standard property and 50 calculated metrics per 360 property.

Calculated metrics can’t reference other calculated metrics in their formulas; they can only use predefined and custom metrics.


Calculated metrics in Google Analytics 4 provide an invaluable tool for tailoring your analytics to your specific business needs. By understanding the process of creating these metrics and the constraints involved, you can make more informed decisions and gather insights that drive your digital strategy. Experiment with calculated metrics to unlock the full potential of GA4. 


Conversion Optimisation Model Discovery

There are several ways to drive conversion optimization efforts in an organization. The choice of operating model depends on:

  • Traffic volume to the website
  • Preference of product/marketing teams
  • Budget
  • Speed
  • The choice of tools


If an organization wants to have a pure data-driven conversion optimization model such that they meticulously want to assess how the addition/removal/update of any element contributes to the conversion rate of the website then they have to A/B test every update.

The positive side to this update is that over time you will have a very clear understanding of what works and what does not work for your site.
But, if you do not have enough traffic volume then reaching results with statistical significance can take aeons.

If you have decent traffic volumes then you should opt for ongoing experimentation as that becomes a part of your operating culture.
Further, websites which already tick off best practices of design and copy have no other option but to take an approach of ongoing testing.
CRO of poorly developed websites is much easier than CRO of good websites.

Other than the traffic volume, the volume of tests depends on the choice of tool.
Most businesses use Google Optimize, which is a free tool under the Google Marketing Cloud stack, but at a time we can do only 5 tests.

If a business can afford ongoing testing and commit to it then logically there is no reason that it will not discover winning variants.
In our experience, consistent ongoing testing leads to a lift of at least 30% YoY for most eCommerce stores with decent value propositions.

Irrespective of how skilled a CRO expert is, it is not possible for anyone to predict the outcome of the tests.
The business must be willing to accept that half of the tests can fail. This number can go up or down based on the current status of the website,
and the value proposition of the business.

This model starts at a monthly retainer of 2,400 USD.


If the website does not have enough traffic volume & the business is already planning a website revamp, this will be the suitable model.

This model can also be leveraged for developing new landing pages.

In this model, we will redesign sections of the website or the full website, considering the best practices in design and copywriting.

If a section already exists on the site then we can split-test between our version and the existing version to ascertain that there was indeed a lift in conversion rates and by how much.

This is a faster way to get results, but we cannot predict with certainty on which elements on the new version are driving results, it’s a full package.

The cost of this project is highly subjective depending on the requirements. However, for simple web page development,
we can estimate a ballpark of 1,200 USD per page. That will cover design, copy and development.

We will also help setup up analytics on the page and optionally set up the split test between old and new versions.

Businesses can also opt for going with model 2 first and then model 1 to consistently optimize the new version that they get developed.

Model 3 – Conversion Development with money back guarantee

This model is open only after we assess your business model. In this model. In this model we guarantee you a lift in conversion rate (Usually it is a minimum 30% lift)

The pricing for this model starts at 12,000 USD. We will take 50% advance. The lock in period of this package is 4 months.

There are some other factors to keep into consideration.
Since, this a money back guarantee solution, there are fixed turn around times for the clients to respond to various things like copy,
creatives and other dependencies. We will mutually agree on a time before commencing the project and the agreement will be part of our service agreement.

The agreed lift will not affect the average order value, rather we will also try our best to increase the average order value as well.

We will run a split test using Google Optimize between your existing version of the website and the version that we develop.

If there is a consistent lift in the conversion rate for at least 14 days with at least 90% statistical significance, then we will deduce that the version is successful. We will then calculate the lift factoring in both the probability as well as the conversion difference between default and the variant.

To calculate the incremental revenue we will consider the calculations that the test produces.
For example, if your default conversion rate was 2% and the conversion rate of our variant after 14 days of test with 90% + probability was 2.8% –
we will consider a 40% * 90% lift in your conversion, i.e a 36% lift.

Marketing Attribution And Attribution IQ in Adobe Analytics

In this post today, we shall cover the basics of marketing attribution and how we can use Attribution IQ from Adobe Analytics for our marketing attribution requirements

So what is Attribution?

Basically Attribution is the process of identifying a set of user actions (“events”) across screens and touch points that contribute in some manner to a desired outcome, and then assigning value to each of these events.


In simple terms, Attribution models are used to understand through which marketing touch-points( such as Facebook campaign ads,google display ads etc) did the user get to know about the digital asset, plus how and at what point did these touch points influence the user for the intended outcome. Using these models marketers can calculate how much credit should be given to each channel and optimize there spends accordingly

Why is it important and how is it done in digital marketing?

These days, the potential customer/user is bombarded with different ads and campaigns both online and offline. Their attention span is very short, and many ads never even reach the consumers the way they were intended to be. This requires targeting the users across multiple platforms. Moreover, the user can take multiple visits before converting and each visit can be from a separate touch point. Hence, It is very important to track and measure how much impact each campaign or ad is creating, so that the marketers can calculate which source and medium to invest better in the future.


In digital marketing, there are different models in marketing attribution based on the credit given to each of the touch points. While there are many, some popular ones are:

First touch

It basically attributes all the conversions to the first interaction/marketing medium of the user. For example, if a user made three visits before converting, wherein first visit was from google search, second from display network and third from Direct, then with first touch attribution, google search will get the entire credit.

Last Touch


It is similar to first touch , but in this case the last interaction of the user (that is, the direct channel) just before the conversion is given all the credit. In the same example as above, the third and last visit of the user before the conversion was direct channel. Therefore, all the credit will then go to the direct channel.

Linear model of attribution

In this case all the touch points, that is, from first to last interaction are given an equal amount of credit from the conversion. That is, in this case, all the 3 channels, according to the above example, will have an equal distribution of credit for the conversion


The main disadvantage of the models above are that they present a misleading or incomplete picture to the marketers. This is because at an overall level they don’t present insights into the returns on marketing investment and do not provide a refined guidance on how to distribute budgets among the various marketing media.

Attribution IQ in Adobe Analytics

Adobe Analytics has recently launched its Attribution modelling offering within its reporting tool, the Workspace. This tool has got many pre-built attribution models but we can also apply our own custom attribution to the Starters,Players and Closers. For example, we can go with a 30-40-30 attribution where the Starter is given 30% credit of conversion, 40% of credit is distributed equally among all players and closer is give the credit of 30%.

Using Attribution IQ we can not only find out how the various marketing touch points are leading to conversion, but these models can also be applied to any other data point like pages, internal banners and various CTAs

The advantage of Attribution IQ is that we can apply the attribution at both visit as well as visitor level. For something like Marketing attribution we will chose a visitor level lookback window because a visitor can make multiple visits from different marketing channels before converting, but for something like pages, we will consider a visit level lookback window

The other advantage is we can choose any metric that we consider a conversion for our digital assets with Attribution IQ. So we are not just restricted to purchase, but can use any success event like lead submission.

Google Data Studio Explorer – What, Why & How

In Google Data Studio, Explorer is a new tool which helps you in analyzing data faster . The Explorer streamlines the editing, viewing and data visualization experience. Helps apply filters quickly. Export your explorations to new or existing reports. You can even access the Explorer from any report to start exploring your data without modifying your report.

It will help you dig deeper into your data without having to write complicated SQL queries to draw insights. Think of it as your personal sandbox within Data Studio where you can build, test and inspect your charts and tables before adding them to a brand new report or exporting them to an existing report.

How the Explorer works

The Explorer lets you examine your data using a single chart. You can easily switch between the same visualizations available in a report. All of the same styling options available in reporting, are also available in the Explorer.

  1. Sign in to Data Studio.

  2. On the left, click EXPLORER.

  3. Click in the top left , then select Data Source.

  4. Select a data source you’d like to explore.

  5. In the top right, use the toolbar to select different chart types.

  6. To filter your data, drag dimensions and metrics from the Available Fields panel on the right to the filter bar at the top of the Explorer.

  7. In the top right, click SAVE to preserve your work.

  1. To view your exploration in a report:

    1. In the upper right, click .

    2. Select Create new report.

    3. Click ADD TO REPORT.

    4. In the top right, click View.

    5. To return to the Explorer, hover over the chart to display the More menu, then select Explore.


Today, companies on Google Analytics 360 have a quick and easy access to the power of the raw Google Analytics data in BigQuery opening up a plethora of opportunities for data integration with other data sources for advanced analysis and machine learning applications.

As data sets in marketing are seamlessly stitched together and made available in BigQuery, the next big challenge is about exploring that data to draw meaningful conclusions. You can now continue your data exploration by accessing Explorer from within the BigQuery interface as well.

How Does that Work?
  • Connect to BigQuery using the new URL

  • Choose the data source, select the table you want to explore.

  • Select the ‘Explore in Data Studio’ option under the export dropdown in BigQuery.

  • You will be directed to an exploration window in Data Studio where you can dive into your data.
How about opening an existing report?

You can do so without altering the chart. Right click on the chart you want to investigate and select Explore.

Differences between Reports & Explorations

1. Explorations are temporary unless you save them

Since the Explorer is a scratch pad, Data Studio doesn’t save your explorations automatically. To preserve your work so that you can revisit it later, click SAVE in the upper right of the exploration.

2. Explorations are private to you

Only you can see the explorations you create. There is currently no option to share explorations directly. However, you can export your explorations to reports and share them indirectly.

3. Explorations are optimized for filters

You can easily apply a filter to your explorations by dragging dimensions and metrics from the properties panel to the filter area at the top of the Explorer.

Explorations unify edit and view mode

The Explorer combines the best of view and edit modes into a single experience. You can customize the chart, add filters, and interact with the data, all in the same interface.

Exploration charts are copies

There is no link between charts in an exploration and charts in reports. If you export a chart from an exploration to a report, you’re exporting a copy of the chart. Similarly, when you open a report chart in the Explorer, you’re making a copy of the chart.

Thus concludes our session on Data Studio latest tool, Explorer. Know more about Regex function in Google DataStudio!.

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