Trending December 2023 # How To Generate Quality Faqs & Faqpage Schemas Automatically With Python # Suggested January 2024 # Top 15 Popular

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During my SEJ eSummit presentation, I introduced the idea of generating quality content at scale by answering questions automatically.

I introduced a process where we researched questions using popular tools.

But, what if we don’t even need to research the questions?

In this column, we are taking that process a significant step further.

We are going to learn how to generate high-quality question/answer pairs (and their corresponding schema) automatically.

Here is the technical plan:

We will fetch content from an example URL.

We will feed that content into a T5-based question/answer generator.

We will generate a FAQPage schema object with the questions and answers.

We will validate the generated schema and produce a preview to confirm it works as expected.

We will go over the concepts that make this possible.

Generating FAQs from Existing Text

Let’s start with an example.

Make a copy of this Colab notebook I created to show this technique.

Feel free to change the URL in the form. For illustration purposes, I’m going to focus on this recent article about Google Ads hiding keyword data.

The second input in the form is a CSS selector we are using to aggregate text paragraphs. You might need to change it depending on the page you use to test.

Here is what the rich results preview looks for that example page:

Completely automated.

How cool is that, right?

Now, let’s step through the Python code to understand how the magic is happening.

Fetching Article Content

We can use the reliable Requests-HTML library to pull content from any page, even if the content is rendered using JavaScript.

We just need a URL and a CSS selector to extract only the content that we need.

We need to install the library with:

!pip install requests-html

Now, we can proceed to use it as follows.

from requests_html import HTMLSession session = HTMLSession() with session.get(url) as r: paragraph = r.html.find(selector, first=False) text = " ".join([ chúng tôi for p in paragraph])

I made some minor changes from what I’ve used in the past.

I request a list of DOM nodes when I specify first=False, and then I join the list by combining each paragraph by spaces.

I’m using a simple selector, p, which will return all paragraphs with text.

This works well for Search Engine Journal, but you might need to use a different selector and text extraction strategy for other sites.

After I print the extracted text, I get what I expected.

The text is clean of HTML tags and scripts.

Now, let’s get to the most exciting part.

We are going to build a deep learning model that can take this text and turn it into FAQs! 🤓

Google T5 for Question & Answer Generation

I introduced Google’s T5 (Text-to-Text Transfer Transformer) in my article about quality title and meta description generation.

T5 is a natural language processing model that can perform any type of task, as long as it takes input as text and output as text, provided you have the right dataset.

I also covered it during my SEJ eSummit talk, when I mentioned that the designers of the algorithm actually lost on a trivia contest!

Now, we are going to leverage the amazing work of researcher Suraj Patil.

He put together a high-quality GitHub repository with T5 fine-tuned for question generation using the SQuAD dataset.

The repo includes instructions on how to train the model, but as he already did that, we will leverage his pre-trained models.

This will saves us significant time and expense.

Let’s review the code and steps to set up the FAQ generation model.

First, we need to download the T5 weights.

!python -m nltk.downloader punkt

This will cause the python library nltk to download some files.

[nltk_data] Downloading package punkt to /root/nltk_data... [nltk_data] Unzipping tokenizers/

Clone the repository.

%cd question_generation

At the time of writing this, I faced a bug and in the notebook I applied a temporary patch. See if the issue has been closed and you can skip it.

Next, let’s install the transformers library.

!pip install transformers

We are going to import a module that mimics the transformers pipelines to keep things super simple.

from pipelines import pipeline

Now we get to the exciting part that takes just two lines of code!

nlp = pipeline("multitask-qa-qg") faqs = nlp(text)

Here are the generated questions and answers for the article we scraped.

Look at the incredible quality of the questions and the answers.

They are diverse and comprehensive.

And we didn’t even have to read the article to do this!

I was able to do this quickly by leveraging open source code that is freely available.

As impressive as these models are, I strongly recommend that you review and edit the content generated for quality and accuracy.

You might need to remove question/answer pairs or make corrections to keep them factual.

Let’s generate a FAQPage JSON-LD schema using these generated questions.

Generating FAQPage Schema

In order to generate the JSON-LD schema easily, we are going to borrow an idea from one of my early articles.

We used a Jinja2 template to generate XML sitemaps and we can use the same trick to generate JSON-LD and HTML.

We first need to install jinja2.

!pip install jinja2

This is the jinja2 template that we will use to do the generation.

{ “@type”: “FAQPage”, “mainEntity”: [ {% for faq in faqs %} { “@type”: “Question”, “acceptedAnswer”: { “@type”: “Answer”, } }{{ “,” if not chúng tôi }} {% endfor %} ] }

I want to highlight a couple of tricks I needed to use to make it work.

The first challenge with our questions is that they include quotes (“), for example:

Who announced that search queries without a "significant" amount of data will no longer show in query reports?

This is a problem because the quote is a separator in JSON.

Instead of quoting the values manually, I used a jinja2 filter, tojson to do the quoting for me and also escape any quotes.

It converts the example above to:

"Who announced that search queries without a "significant" amount of data will no longer show in query reports?"

The other challenge was that adding the comma after each question/answer pair works well for all but the last one, where we are left with a dangling comma.

I found another StackOverflow thread with an elegant solution for this.

{{ "," if not chúng tôi }}

It only adds the comma if it is not the last loop iteration.

Once you have the template and the list of unique FAQs, the rest is straightforward.

from jinja2 import Template template=Template(faqpage_template) faqpage_output=template.render(faqs=new_faqs)

That is all we need to generate our JSON-LD output.

You can find it here.

Finally, we can copy and paste it into the Rich Results Test tool, validate that it works and preview how it would look in the SERPs.


Deploying the Changes to Cloudflare with RankSense

Finally, if your site uses the Cloudflare CDN, you could use the RankSense app content rules to add the FAQs to the site without involving developers. (Disclosure: I am the CEO and founder of RankSense.)

Before we can add FAQPage schema to the pages, we need to create the corresponding FAQs to avoid any penalties.

According to Google’s general structured data guidelines:

“Don’t mark up content that is not visible to readers of the page. For example, if the JSON-LD markup describes a performer, the HTML body should describe that same performer.”

We can simply adapt our jinja2 template so it outputs HTML.

{% for faq in faqs %} {% endfor %}

Here is what the HTML output looks like.

Now, that we can generate FAQs and FAQPage schemas for any URLs, we can simply populate a Google Sheet with the changes.

My team shared a tutorial with code you can use to automatically populate sheets here.

Your homework is to adapt it to populate the FAQs HTML and JSON-LD we generated.

In order to update the pages, we need to provide Cloudflare-supported CSS selectors to specify where in the DOM we want to make the insertions.

We can insert the JSON-LD in the HTML head, and the FAQ content at the bottom of the Search Engine Journal article.

As you are making HTML changes and can potentially break the page content, it would be a good idea to preview the changes using the RankSense Chrome extension.

In case you are wondering, RankSense makes these changes directly in the HTML without using client-side JavaScript.

They happen in the Cloudflare CDN and are visible to both users and search engines.

Now, let’s go over the concepts that make this work so well.

How Does This T5-Based Model Generate These Quality FAQs?

The researcher is using an answer-aware, neural question generation approach.

This approach generally requires three models:

One to extract potential answers from the text.

Another to generate questions given answers and the text.

Finally, a model to take the questions and the context and produce the answers.

Here’s a useful explanation from Suraj Patil.

One simple approach to extract answers from text is to use Name Entity Recognition (NER) or facts from a custom knowledge graph like we built in my last column.

A question generation model is basically a question-answering model but with the input and target reversed.

A question-answering model takes questions+context and outputs answers, while a question-generation model takes answers+context and outputs questions.

Both types of models can be trained using the same dataset – in our case, the readily available SQuAD dataset.

Make sure to read my post for the Bing Webmaster Tools Blog.

I provided a fairly in-depth explanation of how transformer-based, question-answering models work.

It includes simple analogies and some minimal Python code.

One of the core strengths of the T5 model is that it can perform multiple tasks, as long as they take text and output text.

This enabled the researcher to train just one model to perform all tasks instead of three models.

Resources & Community Projects

The Python SEO community keeps growing with more rising stars appearing every month. 🐍🔥

Here are some exciting projects and new faces I learned about on Twitter.

— Dan Leibson (@DanLeibson) September 9, 2023

— JC Chouinard (@ChouinardJC) September 9, 2023

I build a python script that merges all of the screaming frog reports and then highlights all the errors in the top line tab

— M.Marrero (@steaprok) September 9, 2023

I’m working on a @GoogleTrends Visualiser via PyTrends + (you guessed it!) @streamlit! 😉

— Charly Wargnier (@DataChaz) September 9, 2023

More Resources:

Image Credits

All screenshots taken by author, September 2023

You're reading How To Generate Quality Faqs & Faqpage Schemas Automatically With Python

How To Become A Youtube Partner And Generate Revenue From Your Videos

What Is A YouTube Partner?

How Do I Become A Partner?

YouTube users who are curious about taking the step towards monetization can check their eligibility by heading over to YouTube’s Account Monetization page. There is an “Enable My Account” button that will, assuming you’re accepted, enable you to monetize your account. Eligibility depends largely upon which country you live in and whether you own the worldwide rights to the videos that you upload, including all of the sound effects and music that may be contained inside them. If you did not create all of the content yourself, you must be able to show written permission to distribute it. You must also abide by both YouTube’s Terms of Service and Community Guidelines. The latter bars you from uploading content that is sexually explicit, depicting real-world abuse, or is otherwise harmful. If any of your videos break these guidelines, they will be taken down. YouTube’s partnership criteria are available for anyone to see.

What Then?


YouTube partners were originally surrounded by mystery, as Google only invited select producers to be participants. The process has since opened up, and anyone with a following and complete rights to their content can become one. If you have a camera, the dedication needed to produce a show, and the perseverance necessary to gain a following, you can become a YouTube partner. If you already produce YouTube videos, do you think of it as a hobby or a job?

Bertel King, Jr.

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How To Create Quality Mockups With Canva

If you’re looking for a quick, easy, and simple way to create good-looking mockups you’ll be excited to hear that you can create them with Canva.

We’ll cover three different ways to create mockups with Canva in this article, all of which are incredibly easy to do and also free!

There’s also no experience needed with complicated design software like Photoshop, as everything in Canva is beginner-friendly.

Let’s get to it.

Jump to a specific section:

Creating mockups with Canva: Method 1

The simplest way to create mockups with Canva is to utilize the ‘frame’ elements in Canva’s library.

Canva also has a good-sized library of existing templates that contain frame elements already. Most of these contain device frames such as smartphones, MacBooks, or laptops.

Here are some examples:

Mockup templates on Canva

From here you can replace the existing image in the frame(s) with a new one, or you can even add a video file to show within the frame:

Mockup template

Customized mockup template

It’s worth noting that you can only add images or video files to the frame elements on Canva.

So, you can create designs on Canva and save them as templates but you will only be able to add them to a frame if you download them and reupload them to your Canva account.

Also, the frame elements on Canva are fairly limited at the moment, so if you’re wanting to create mockups for products such as mugs or apparel you’ll need to use another tool or a different method on Canva (we’ll cover that later on).

Related articles

Creating mockups with Canva: Method 2

You can create mockup scenes from a blank canvas in Canva fairly easily.

The way we like to do is to start with a blank canvas in our chosen dimensions, and then we add a background color, pattern, or image e.g.:

Creating a mockup from a blank canvas

You can then add various elements to make up your image. You can make it as complex or as simple as required but using the frame elements will make it a lot easier:

Adding frame elements

Then all you need to do is add your designs or images to the frame elements plus you can add text or other elements where required:

Completing the mockup image

You don’t necessarily have to use the frame elements from Canva. They have other elements such as iPhone and MacBook cutout photos that you can layer over an image or element in your design.

It’s not as simple or as clean utilizing the frame elements though:

iPhone cutouts

MacBook cutouts

If you want to create flat lay mockups such as the one in the image below, you can do this with Canva too:

Flat lay mockup

There isn’t a huge amount of elements in Canva’s library that are suitable for flat lay designs though. However, there are some if you do a bit of searching in the Photo tab.

For example, we created a quick mockup scene with a pencil, pen, coffee mug, iPad, iPhone, eraser, paperclips, and notepad – all from Canva’s elements library:

Flat lay mockup created in Canva

If you want plenty of elements to use for flat lay mockup designs you should check out Creative Market or Envato Elements:

Scene Creator packs on Creative Market

Scene creator packs on Envato Elements

You can download flat lay collections from these sites and then upload them to Canva to use in your designs.

If you search for ‘scene creator’ on these sites and you’ll have thousands of options for elements to use for your flat lay mockups.

Creating mockups with Canva: Method 3

The last method, and probably the one that gets the best results, is to use the Smart Mockups integration on Canva.

To start the process, create a new design on a blank canvas. You’ll need to make sure the dimensions of the canvas work with the Smart Mockups template that you plan to use.

In our case, we’ll be using a landscape template, and the dimensions 1600x1069px work well for that.

You’ll need to upload a design to your canvas that you want to see on a mockup template. Having a design with a transparent background is probably the best idea:

Design for our mockup

Navigating to the Smartmockups integration

There are mockup templates for smartphones, computers, cards, frames & posters, books, clothing, and mugs (nowhere near as many templates that are available on Smartmockups though):

Smartmockups templates on Canva

Choose a template you like, and it will load onto your canvas with your design or image added to it. You can then resize the mockup to fit your canvas.

Mockup template controls

The only customizations you can do at the moment are to resize and reposition your design on the mockup and change the main item color in the mockup template (e.g. the t-shirt or mug color):

Mockup customization options

And that’s you created a mockup via the Smartmockups integration. You can now download your mockup image.

We created this mockup in a matter of seconds:

Our final mockup

Related articles

Alternative options

Placeit mockup generator

Whilst Canva is an incredible tool for creating all kinds of designs, we feel there are some better options when it comes to creating mockups online.

Placeit is probably the best mockup generator but there are some other high-quality tools that allow you to create incredible-looking mockups with ease. Here are the top alternative tools for creating mockups:


Here are the answers to some of the most common questions around creating mockups with Canva:

Do Canva do mockups?

As you can see in this article, Canva do offer users the ability to create mockups. Using the frame elements that Canva offers or the Smartmockups integration, users can quickly and easily create mockups without needing any design software experience.

Plus you can create so much at no cost at all. That being said, we would recommend using Placeit if you’re looking for the best mockup tool online.

How to make mockups in Canva?

Again, you can see how to create mockups in Canva earlier in the article. It depends what you’re wanting to create.

The premade templates with the frame elements are great to use if you’re promoting a digital asset and can utilize the smartphone, tablet, or computer frames.

If you want to use a different kind of product such as t-shirt, mug, or book the best way to create those types of mockups is by using the Smartmockups integration in Canva.

What is the Canva and Smart Mockups integration?

Smartmockups is a fantastic mockup tool, they are one of the top t-shirt mockup generators and book mockup generators.

A small selection of their mockup template library is available to use within Canva. So users can take one of their images or designs and put them on the Smartmockups templates that are available on Canva.

If you have a Smartmockups account you can also connect your Canva account and this will allow you to transfer designs from Canva to your Smartmockups account to use with the full Smartmockups template library.

You can read more about it here.

Can you use Canva mockups for commercial use?

There are some restrictions that you can read about in this article.

How to use Canva mockups?

You can utilize the mockups you create in Canva in several ways.

If you’re looking promote print on demand designs, you can utilize the mockups via the Smartockups integration to create promotional material for your print on demand products.

Again, this could be images for your product listings, social media content, and your website.

Wrapping things up

So, there you have it, that’s how you can quickly, easily, and affordably create high-quality mockups in Canva.

Canva is such an awesome online design tool that literally anyone can use, beginner or expert! So, if you’re looking to create some great-looking mockups and promotional material you should give it a go.

How To Compress Your Images Without Affecting The Quality

If you have ever waited on a site with large image files to load, you have seen why image compression is necessary. To compress images means you take away or regroup parts of an image so that it takes up less space.

There are two basic algorithms used for compression – lossy and lossless. Lossy compression makes changes that create lower quality images. The smaller you make the file, the more noticeable your differences between the original and the compressed file become.

Lossless compression algorithms don’t discard any information, so they result in larger files than lossy compression generates. Lossless compression finds better ways to store the information, and the picture does not lose any quality.

Types of images

First, let’s take a look at four of the most popular image types.

JPEG images (Joint Photographic Experts Group) are “lossy” images. They use a scale of compression that decreases the image’s file size substantially. It eliminates as much information as possible from the file by deleting data your eyes won’t notice. However, if you make the image too small, the result will have more obvious pixelation. The images also have more artifacts, which are features on a compressed image not in the original. JPEGs have 24-bit color with up to 16 million colors available.

The Graphics Interchange Format (GIF) compresses images in two ways. One, it reduces the number of colors. GIF images have an 8-bit palette and only 256 colors. It also replaces large patterns with smaller ones. So, if there are five kinds of blue, GIF will represent them as one. It is both lossy and lossless depending upon the picture you are compressing. A picture with fewer than 256 colors, will not lose any quality. However, if you have a full-color photo, it can lose up to 99.998% of the color.

TIFF is a very flexible format that can be lossy or lossless. Most TIFF files are not compressed, and their high quality makes them perfect for storing graphics and printing. These image files contain all the details of the storage algorithm and all the colors, so they are very large. Their large size requires a long transfer time, slows loading time, and uses a significant amount of disk space.

Portable network graphics or PNG files is a lossless compression, so it does not cause loss of quality and detail. The compression is completely reversible, meaning the image will be recovered precisely as it was sent. PNG finds patterns in the image to use to compress the size. This file type uses only 256 colors but saves the information about those colors quite efficiently. It also supports 8-bit transparency.

Reasons to compress your images

The most common reason for compression of images is to optimize your website. Sites with uncompressed images can take a much longer time to load. Long load times will cause more of your customers to abandon your page in search of another one.

If you have pictures you want to send over email, you need to be aware of what the file size limit is for attachments with your service. If your file is too large, you won’t be able to send it, and even if you can send it, it may transfer too slowly.

Compressing your images will reduce the amount of space you need to store them. If you compress your images, they won’t take up as much space, preventing the need to purchase more storage.

Two free, useful online tools

There are many different tools available to compress images. Two of them are Optimizilla and CompressNow. With both these services you can upload multiple images at one time and preview the result of the compression before you download it.

Both tools use a simple drag-and-drop method for uploading your image files and the ability to upload using the file manager. You can download all the files you compressed together, or you can do it one at a time. When you download your images, they will keep their original names but will have a tag added to the end of the file such as “-min” or “-compressed.”

When you use CompressNow, you choose the compression level before you compress the file. You can upload up to ten JPEG, GIF, or PNG files at a time up to 9 Mb.

Optimizilla gives you the option to upload up to twenty JPEG or PNG images at a time and displays the photos before and after optimizing, and you can change the optimization level for each photo.

For WordPress users

If you are using WordPress for your online sites, we have previously covered some of the best image optimization plugins you should use for your site. This site uses chúng tôi image optimizer, though many would recommend WP Smush, which comes with more features.

Whether you need to speed up your website or send pictures over email to Grandma, you should consider using one of these tools to compress the images. What other tools have you used?

Tracey Rosenberger

Tracey Rosenberger spent 26 years teaching elementary students, using technology to enhance learning. Now she’s excited to share helpful technology with teachers and everyone else who sees tech as intimidating.

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How To Take Better Quality Screenshots On Windows 10. (Higher

Learn how to improve the quality of screenshots captured on Windows 10 PCs. An easy to enable and use option that will improve the quality of screenshots captured on your Windows 10 computer.

How to Fix Netflix Not Responding or Won’t Load Content. (Chrome, Firefox, Edge, and Other Browsers)

Taking screenshots on Windows 10 is one of the operating systems most simple tasks and is as easy as pressing the Print Screen (Prt Scr) key on your keyboard and pasting the contents to your desired location. It’s almost easier than on mobile devices. The only problem with screenshots on Windows 10 is that they are often of low quality and don’t allow you to zoom in with as much detail as screenshots taken on other devices.

Although this has been the case for quite a long time, there is also a solution that has been around for just as long, hiding in almost plain sight. With a simple flip of a toggle, you can improve the quality of screenshots captured on Windows 10 quite drastically.

Related: How to Fix the Clock on Windows 10 Not Automatically Adjusting For Time Changes. (Daylight Savings)

How Do You Make Windows 10 Take Higher Resolution Screenshots?

Finally, flip the toggle next to Let Windows try to fix apps so they are not blurry to On. Alternatively, you can try to manually choose a setting that works well for you by using the Custom Scaling option. Just keep in mind that using the manual option may take a fair amount of trial and error to get ideal settings.  

After making this change, you should see a decent improvement in the quality of your screenshots, however, they will still reflect the overall screen resolution of your PC. If your screen resolution is set to 1920×1080 don’t expect a screenshot to come out at 3840 x 2160…

How Do you Improve Windows 10 Screenshots Using Software?

On a side note, if you have recently updated to Windows 10 version 1809, there’s a good chance you are now seeing suggestions within the Settings App. If these bother you, check out the article below which will show you how to disable them. How to Remove/Hide Suggestions From Settings on Windows 10.

Google Analytics 4 Faqs: Stay Calm & Keep Tracking

On March 16th, 2023, Google Analytics shocked the marketing industry by announcing that Universal Analytics would stop processing hits in July 2023.

This didn’t go over so well.

Some marketers are unhappy with the user interface; others are frustrated that GA4 does not have key features.

Many are still in the denial phase – besides, isn’t it still in beta?

Let’s take a step back and answer the burning questions here:

Why is this happening?

What do these changes mean?

What do I need to do right now?

Why Universal Analytics Is Updating To Google Analytics 4

Many marketers have built business processes around Universal Analytics and want to know why this change is happening.

So, I asked former Googler Krista Seiden, who helped build GA4 and is also the founder of KS Digital, “Why is this GA4 update happening?”

Seiden explained that GA4 has actually been in development for many years.

Originally, it came out as a public beta called App+Web, and in October 2023, it dropped the beta label and was rebranded as GA4.

“GA4 isn’t so much an update, but an entirely new way of doing analytics – set up to scale for the future, work in a cookieless world, and be a lot more privacy-conscious,” Seiden explained.

Google’s announcement blog was entitled,“Prepare for the future with Google Analytics 4.”

… for the future.

We keep hearing this; what does “for the future” mean?

When I read Google documentation and chatted with analytics experts, I noticed three main themes or ways that GA4 prepares your business for the future:

updated data model,

works in a cookieless world,

and privacy implications.

Let’s unpack each of these.

Data Model

A data model tells Google Analytics what to do with the site visitor information it collects.

Universal Analytics is built on a session-based data model that is 15 years old.

This was before internet devices like smartphones were widely used.

UA measurement was built for independent sessions (group of user interactions within a given time frame) on a desktop device and user activities were tracked with cookies.

Fun fact, I learned from the head of innovation at Adswerve, Charles Farina, that you can actually still implement GA javascript code from 15 years ago.

Yes, I’m talking about the original tracking code (Urchin).

And it still works today.

In the past few years, this old measurement methodology has become obsolete.

As much as we love Google Analytics, there are many examples of how it just does not work with the way users interact with our websites today.

Farina shared an example with conversions.

In Universal Analytics, goals are session-based. You cannot measure goals by user.

If a user watches four videos in one session, it can only count as one conversion.

In GA4, conversions (or goals) are event-based.

Cookieless World

Google Analytics works by setting cookies on a user’s browser when visiting your website.

Cookies allow a website to “remember” information about a visitor.

That information can be as simple as “this user has visited before” or more detailed, like how a user interacted with the site previously.

Cookies are widely used on the web. And they can be helpful for things like remembering what items you put in a cart.

However, cookies also pose a privacy risk because they share data with third parties.

As the world becomes more aware of privacy issues, users increasingly want to opt out of sharing their data.

And because more people opt out of sharing their data, Google Analytics cannot report on all the people who visit a website.

There is a growing gap in the data collected.

Google Analytics had to adapt to remain useful to website owners.

And they did.

GA4 is designed to fill in the gaps using machine learning and other protocols to create reports.

This is called “blended data.”

In the blog post about this change, Google explains.

“Because the technology landscape continues to evolve, the new Analytics is designed to adapt to a future with or without cookies or identifiers.

It uses a flexible approach to measurement, and in the future, will include modeling to fill in the gaps where the data may be incomplete.

This means that you can rely on Google Analytics to help you measure your marketing results and meet customer needs now as you navigate the recovery and as you face uncertainty in the future.”

Data Privacy

Data privacy is a big topic that deserves its own article in length. To oversimplify it, people want more control over their data and its use.

Laws such as GDPR and the California Consumer Privacy Act are enforcing this desire.

Google Analytics says that GA4 is designed with privacy at its core – but what does that mean?

All UA privacy settings will carry over, and we are getting new features.

For example, Google Analytics 4 does not store IP addresses and GA4 relies on first-party cookies, which supposedly keep them compliant with privacy laws.

I encourage you to use this time to consider your data strategy and set the tone for your company’s data privacy policy, assess your digital footprint and consent management, and ensure compliance.

What Do These Changes Mean For My Business?

The second thing marketers want to know is, “How is GA4 different?”

Or really, “How will these changes affect my business?”

Don’t get too caught up in comparing Universal Analytics and GA4.

The numbers won’t match.

It’s a rabbit hole with no actionable or otherwise helpful outcome.

As Seiden pointed out, this is not just a platform upgrade.

It’s a completely new version of Google Analytics.

GA4 is a new data model and a new user interface.

Keep reading for a summary of key differences between UA and GA4 data and how they affect your business.

Changes in Data Modeling

The most important change is the way data is collected.

Universal Analytics uses a session-based data model (collection of user interactions within a given time frame) and collects data as various hit (user interaction) types within these sessions.

This is why watching four videos in one session only counts as one conversion in UA.

Google Analytics 4 is user-based and collects data in the form of events.

Each event has a unique name (event_name parameter) used to identify the event, with additional parameters to describe the event.

For more on the differences between the two data models, see UA versus GA4 data in the Google help docs.

Spam Detection

Have you ever seen a giant spike in traffic in Universal Analytics or a bunch of random traffic sources that you couldn’t explain?

Spammers could send fake data to people’s Google Analytics accounts by using the Measurement Protocol.

As you can imagine, this created a big problem with inaccurate data.

Google has fixed this problem by only allowing hits with a secret key to send data to a GA4 property. This key is visible in your GA4 data stream settings but is not available publicly.

Data Retention

Data retention refers to how long Google Analytics keeps disaggregated data. At the end of the retention period, the data is deleted automatically.

The default setting for data retention in Universal Analytics is 26 months. But you could choose a different time interval, from 14 months to “do not automatically expire.”

In GA4, you can choose to retain data for two months or 14 months.

At the end of the retention period, you keep the aggregated data in standard reports, but the disaggregated data used in Explore reports are no longer available.

What is aggregated versus unaggregated data?

Think of aggregated data as a summary used to look at website visitors as a whole.

And disaggregated data is dissected or broken down into smaller subsections, such as a specific audience or segment.

Shorter retention periods are not really a big deal.

You can still accomplish the same use cases while doing more to respect user data privacy.

You can still run (aggregated) standard reports to show how well you are doing compared to past performance.

And data from the most recent months is the most useful if you want to make predictions and take action.

User Interface: Reporting

GA4 reporting comes with a learning curve.

With Universal Analytics, there was an emphasis on pre-built reports. It was fairly easy and quick to navigate “done-for-you” reports.

Google Analytics 4 is oriented toward taking greater ownership of our data. With that comes the flexibility of custom reporting templates.

Because the data model has changed and the platform is more privacy-conscious, replicating some of the tasks you performed in Universal Analytics may not be possible.

As an agency or freelancer, you have an additional responsibility to communicate wins and opportunities to your accounts.

And they’re going to need time to learn GA4 or, more likely, rely on you to learn GA4.

To visualize the data in a more familiar way to your clients, I highly recommend Data Studio.

What Do I Need To Do Right Now?

There is no need to panic.

You have time to implement GA4 configuration, time to update business processes, and time to learn new reports.

With that said, GA4 needs to take priority on your roadmap.

Audit your existing analytics setup and create a GA4 configuration plan.

Setting up GA4 before July 2023 is mission-critical.

Start building historical data so that you can do a year-over-year analysis next year.

Once GA4 events are collected, get your team up to speed and update your processes.

A year from now, they will need to be comfortable using Google Analytics 4 to make marketing decisions.

Start planning team training sessions. SEJ rounded up the top educational guides and GA4 resources here.

Last but not least, make plans to extract historical data in Universal Analytics before July 2023.  BigQuery doesn’t cost anything aside from the low storage fees.

Final Thoughts

You’re not just getting an upgrade when you switch to Google Analytics 4. You’re getting an entirely new way of analytics.

This solution is necessary to respect user data privacy and get actionable insights in a cookie-less world.

At the heart of this change is a new data model that makes GA4 different from what we have used in the past decade.

Right now, it’s important to configure GA4 and conversion events for year-over-year data when UA is sunset in July 2023.

After embracing the change, you might enjoy the flexibility and user insights with GA4.

Happy tracking!

More resources:

Featured Image: Paulo Bobita/Search Engine Journal

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