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The deprecation of Google Analytics (as we’ve known it)

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The deprecation of Google Analytics (as we've known it)

It’s time to be excited about the great migration.

The biggest shake-up in the marketing analytics world is that Google Analytics as we know it is going to be sunset and will eventually stop collecting data in July 2023 (October 2023 for GA360 customers). There were mixed responses, to say the least – conflicting tweets, memes and disappointed forum posts were generally the first reactions to the news, and it proves that this drastic move needed to happen at some point. 

As more practitioners and marketers adopt the new Google Analytics 4 (GA4), the benefits are starting to flip the mood from nervousness to excitement.

The version of GA that’s been around for over a decade, Universal Analytics, is hard to leave behind since it’s such an embedded part of web measurement. GA4 was announced in October 2020 but wasn’t met with widespread eagerness that would be expected for a new, robust product. To be fair, there were quite a few other things going on in the world at that time, but in any case, marketers weren’t rushing to make the switch, and the industry seems to be going through the stages of grief for the familiar product:

  • Denial – “I don’t need to change platforms, so I will ignore GA4 for now.”
  • Anger – “How could Google get rid of Universal Analytics?”
  • Bargaining – “What if the deadline is extended? Can we ask for more time?”
  • Sadness – “It will take so much effort to migrate and learn a new tool.”
  • Acceptance – “This is more advanced and helps with the cookieless future that I keep getting asked about. I’m in.”

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It may be a challenging road ahead to migrate, but the move to Google Analytics 4 shouldn’t be considered bad news. It comes with new features, tracking methodology, a lower price point for 360 and has perks for users on the free version. The most important aspect is that it fits more appropriately into the current app landscape and is built to face compliance and privacy changes for what’s in effect and what’s coming.

Ultimately, GA4 is the solution to the internet now, not the internet from a decade ago.

Understanding the past, understanding the future

The news is disorienting, so the timeline should be put in perspective.

Google acquired a product in 2005 called Urchin. Before GA, web analytics was based on server log files, and it was not as intuitive or marketer-friendly. There are still relics of that era with things like UTM parameters (Urchin Tracking Modules) and the property IDs themselves. The “UA” in an ID like UA-12345-1 doesn’t represent Universal Analytics. It stands for Urchin Analytics. Since then, there have been new iterations of GA for the web. Here’s a list of where we’ve been:

There’s one thing that these tracking methods from 2005 to 2022 have in common – all of them still process data and show up in reports, no matter which tracking library you’re using. So, Google is still processing data from the time when the internet looked like this

It’s been 10 years since the release of Universal Analytics. In 2012, Google Tag Manager had yet to be released, and mobile-first web design was a new concept. App tracking was still in beta, and it would be six years before DoubleClick products would evolve to become part of the new Google Marketing Platform. We’ve come a long way, so tracking had to be completely rebuilt, and the Google Analytics from 2005 to 2020 will be taken away and put on a shelf next to Google+ and Google Local.

The UA version of Google Analytics was designed to embrace multi-device behavior, collect more user data, and allow offline and cross-channel measurement. However, culture is no longer multi-screen – it’s multi-multi-multi-screen. The average number of connected devices per person in North America alone will reach 13 in 2023. Universal Analytics cannot easily track different platforms together, and it was not meant to do so. Now that we’re in a more app-centric phase of connectivity, GA4 is a better solution since it was built for that type of analysis. Instead of gathering more data, the goal is to use data that is modeled and as anonymous as possible.

Universal Analytics will be disappearing coincidentally around the same time as the death of the cookie. The hyperconnected landscape called for a necessary pivot for users to have more control over their data, more privacy considerations and more transparent analytics practice. Google Analytics 4 has answered that call with a variety of customizations and settings to establish trust with your visitors while continuing to activate on rich data. User tracking will now be supplemented with machine-learning data baked right into Google Analytics 4. Users’ current trends in behavior will be automatically analyzed to predict future behavior and provide modeled conversions. The privacy-centric features are a core component, but there are other reasons to embrace the change.

What to get excited about

In addition to being the first Google Analytics product to have the built-in capability to collect data from multiple sources, it is a better evolution for enterprise-level while also offering more to small- and mid-size businesses.

The free version of GA has turned into a freemium product. Standard non-paid users now have access (although limited) to tools like BigQuery, GMP integrations, more unsampled data, and access to advanced visualizations through Exploration Reports (formerly called Advanced Analysis).

For Google Analytics 360 customers, those features are much less limited, and some of the additional perks are:

  • Enterprise-level data and user governance through roll-up and sub-properties.
  • More control over data retention.
  • Streaming and nearly unlimited BigQuery exports.
  • Quicker processing, even for large data sets in the billions.
  • The ability to use up to 400 advanced audiences to pass to marketing platforms.
  • Unsampled custom reports, explorations, and the ability to use longer date ranges in advanced reports.
  • Higher level of custom data collection for events, conversions, custom dimensions, and user properties.

Migrations were strongly suggested throughout these iterations but never forced (except for the Google Analytics app tracking SDK). However, older versions of tracking will not be as useful in 2023. It’s symbolic that even the echo of Urchin Analytics in those “UA-12345-1” properties is gone for good and replaced with Measurement IDs and data streams.

Deadlines and timelines

As a reminder, Universal Analytics will officially sunset in July 2023 for those on the free version and October 2023 for GA360 users. This means that properties will be read-only, and data sent to Google will not be processed. There won’t be exceptions, so migrating will be the top priority for everyone. Even if you’re not currently using the platform but have used it in the past, it’s still a time for action. We’re not just moving on. We’re also moving out – historical data will eventually be erased, so data must be saved and exported. The deletion won’t happen until at least six months after the sunset date, but it’s a crucial step in the migration process.

All web and app data should be 100% in Google Analytics 4 by the shutoff date, but ideally sooner. Parallel tracking should be in place and refined now so that data can be available on both platforms. The GA4 numbers won’t match 1:1 to Universal Analytics. Having year-over-year reports comparing UA to GA4 may be misleading, and reports will not be able to use the same data source. With GA4 tracking in parallel, next year’s reports will be comparing apples to apples. Depending on your organization, seasonality can guide how quickly to ramp up and set priorities for the most critical metrics and events. Whether it’s higher education enrollment, holiday e-commerce, or tax season, yearly activity is a consideration for building as much parity as possible between UA and GA4.

Next steps

The first step is to get GA4 on your websites and apps. It’s not too late to get started on a new strategy to fit the new tracking method and create your Google Analytics 4 properties, but delaying parallel tracking may cause reporting, remarketing, and compliance difficulties. After that, learning about how you can take advantage of the durable Google Analytics 4 should spark ideas and conversations beyond migration.


Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

The deprecation of Google Analytics as weve known it

Samantha has been working with web analytics and implementation for over 10 years. She is a data advocate and consultant for companies ranging from small businesses to Fortune 100 corporations. As a trainer, she has led courses for over 1000 attendees over the past 6 years across the United States. Whether it’s tag management, analytics strategy, data visualization, or coding, she loves the excitement of developing bespoke solutions across a vast variety of verticals.

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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