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Google delayed third-party cookie deprecation: Why and what’s next?

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Google delayed third-party cookie deprecation: Why and what's next?

Google blindsided the marketing and advertising industry last month by pushing back third-party cookie deprecation in Chrome until at least late 2025. The reason? Feedback on the Privacy Sandbox initiative suggested much more testing was needed.

There’s no simple answer to why Google did this — but there are a number of possibilities we explored with Andrew Frank, distinguished VP analyst at Gartner covering marketing and advertising.

What is Google’s motivation? ”I’m not sure how fruitful it is to try to analyze Google’s motivations; I’m not sure that the company really acts or speaks with a single voice on some of these topics,” said Frank. “It’s pretty clear that whatever they had hoped to accomplish with the Privacy Sandbox is proving more elusive than perhaps they thought it would.” This, he admitted, was “sort of a non-answer.”

Frank pointed to the absence of action by industry standards bodies and recalled a conversation from two years ago — around the time Google launched the Sandbox — with a representative of WC3. “I asked why we were letting Google make the rules for something as fundamental as how browsers work. Their answer was, ‘Google will get it done faster, because they don’t have all the complicated requirements of a standards body – public commentary, and so forth.’ Looking back, it’s kind of ironic, because if that really was the reason to let G run ahead with this, it couldn’t have been much slower than what we’re seeing now.”

Ironic too that the IAB, the U.S. advertising standards body, is calling out its own members, demanding “less incrementalism and more burning impatience.”

Read next: Google again delays third-party cookie deprecation

You put FLoC in, you take FLoC out… The other major and related u-turn Google accomplished earlier this year was the abandonment of the Sandbox’s most prominent solution, Federated Learning of Cohorts.

“It’s ironic that G puts out an acronym like FLoC,”‘ Frank agreed, “and the entire web community worldwide is rushing into the engine room to try to figure out how to make it work — and suddenly it’s, like, forget that idea.”

The shadow of ADPPA. We recently speculated that the possible bipartisan passage of the American Data Protection and Privacy Act might be serving to put a brake on Google’s progress, writing that: “The specter (benevolent or otherwise) of this legislation is hovering over the many attempts out there to develop alternatives to third-party cookies, including Google’s own Privacy Sandbox initiative. Will the identifiers already on offer, or in development, be in compliance with this legislation if it passes?”

“It’s a plausible theory,” Frank agreed, “and if that is the case we can expect more delays. It seems like that Act is now subject to a lot more controversy than maybe it was a few weeks ago when we had the Dobbs ruling that overturned Roe v Wade. Now it seems that a lot of the focus and anxiety is around protecting health data. Maybe I’ll be surprised and this will sail through the Senate; but I’ve never gone wrong betting against the efficacy of the U.S. legislative process.”

The state of alternative identifiers. Countless adtech vendors have made more or less progress developing alternative routes to addressability, usually consisting of first-party data supplemented by data from other sources, appended in a non-privacy-threatening way. In various flavors, these alternatives have been advanced by many vendors, perhaps most prominently The Trade Desk, LiveRamp and Lotame. Are they frustrated that the need for their solutions has become less pressing?

“I think it’s a two-sided coin,” said Frank. “On the one hand, it must be frustrating, because it’s giving people a reason to delay. On the other hand, it’s given them more time to refine and test their solutions and get them right and get buy in from people who may be reluctant — especially on the publisher side where I think a lot of these alternative solutions are foundering a bit.”

Why is there a lack of adoption among publishers? “Unified ID 2.0, The Trade Desk’s solution, has got extensive buy-in from the adtech community, even the SSP community; where it hasn’t seen much success has been with large publishers, companies that have their own idea about how to deal with privacy. Some publishers are concerned that it might somehow degrade the value of their first-party data; there’s a sort of competing idea about seller-defined audiences, and the publishers think that if they can own the targeting and measurement side they’ll have higher yields.”

Another setback was the IAB deciding to pass on being the “administrator or shepherd” of the standard. “That highlighted the difficulties in transitioning this, or any other standard an adtech company might come up with — to a neutral standard that is owned by the industry. That is what I think we ultimately will need.”


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A year from now. What if, a year from now, Google punts it back to 2026? “It’s the Chicken Little story,” said Frank. “You can’t continue to be surprised by the same thing over and over again. It does seem like there’s a limit to the number of times they can pull this off – but maybe there isn’t. Maybe they;re not feeling the pressure to do anything differently. It’s not clear what the cost to them of delaying this is.”

Why we care. We have some views on what’s happening here, but it’s obviously good to get input from an independent expert. The takeaways? We are free to guess at what Google is doing, but it’s a multi-headed monster. Google itself might not have one, single view on the need for the delay.

Whats more, it could absolutely happen again — and might keep happening until we know if the federal government is going to act on data privacy or dropm it and move on.


About The Author

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space.

He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020.

Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.

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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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