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FLoC is off the table as Google switches to targeting by Topics

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FLoC is off the table as Google switches to targeting by Topics

Google will replace Federated Learning of Cohorts (FLoC) with a new interest-based targeting proposal called Topics, the company announced Tuesday.

The Topics API will share a limited number of topics of interest, based on the user’s recent browsing browsing history, with participating sites without involving external servers. Users will be able to review topics assigned to their profile and remove them. There are no plans at this stage to allow them to add topics. Google says it will have a process in place to exclude potentially sensitive topics like race and sexual orientation. The final iteration of the user controls, as well as other technical aspects of how Topics works, will be determined based on the trial and feedback, Google said.

We reported in the Daily Brief last August that this change was being contemplated.

Google had no news to announce with respect to its Privacy Sandbox Timeline for deprecating third-party cookies, although it conceded it might change depending on trials and feedback.

How it will work. “With Topics, your browser determines a handful of topics, like ‘Fitness’ or ‘Travel,’ that represent your top interests for that week based on your browsing history,” Google said in the announcement.

Up to five topics are associated with the browser. Topics are stored for three weeks and the processing occurs on the device, without involving any external servers, including Google’s own servers.

Google is starting this initiative with about 300 topics “that represent an intersection of IAB’s Content Taxonomy V2 and also our own advertising taxonomy review,” said Ben Galbraith, Chrome product director, “This is a starting point; we could see this getting into the low thousands or staying in the hundreds [of topics].” 

When a user goes to a participating website, the Topics API selects three topics (one from each of the past three weeks) to share with that site and its advertising partners. If a site does not participate in the Topics API, “Then it doesn’t provide a topic nor does it receive a topic,” Galbraith said. The site itself or its advertising partners can opt in to the Topics API.

Google has also published a technical explainer containing more details about the Topics proposal.

The difference between FLoC and Topics. One of the main distinctions between Google’s previous targeting proposal, FLoC, and the Topics API is that Topics does not group users into cohorts. As the Electronic Frontier Foundation has pointed out, fingerprinting techniques could be used to distinguish a user’s browser from the thousands of other users within the same cohort to establish a unique identifier for that browser.

Additionally, under FLoC, the browser gathers data about a user’s browsing habits in order to assign that user to a cohort, with new cohorts assigned on a weekly basis, based on their previous week’s browsing data. The Topics API determines topics to associate with the user on a weekly basis according to their browsing history, but those topics are kept for three weeks. They are shared with participating sites and advertisers rather than a FLoC cohort ID.

The difference between contextual advertising and Topics. Galbraith confirmed that, unlike traditional contextual advertising, users can be targeted by topic even on sites that have nothing to do with the topic. In other words, someone that had showed interest in camping equipment in the previous three weeks might be targeted with ads for tents on a sports website.

“Time will tell” which browsers will adopt. Google is in the early phases of implementing the Topics API, so other browsers likely won’t have had a chance to evaluate it. But, Chrome was the only browser to adopt its predecessor (FLoC), so it’s unlikely that Firefox, Safari, Edge or other browsers will adopt Google’s proposal this time either.

“We’re sharing the explainer, which is the beginning of that process to discuss with other browsers their view on the Topics API, so time will tell,” Galbraith said.

Why we care. Google is currently set to deprecate third-party cookies in Chrome sometime next year and now we have a better idea of what audience targeting options will be available. Although FLoC is now officially off the table, the remarketing solution FLEDGE is still under consideration.

Prior to the Topics API, Google ran into a number of challenges with FLoC, including lack of adoption, industry pushback and regulatory issues. The company has likely addressed some of those challenges with this new proposal, but adoption among other browsers remains unlikely, which could impact how big of a user base advertisers are able to get in front of.

Galbraith declined to make explicit comment on the alternative identifiers being developed within the adtech industry, only saying that Google believes that the days of tracking consumers are over.

Additional reporting by Kim Davis.


About The Author

1640838256 758 Inclusive marketing resources to strengthen your brands messaging

George Nguyen is an editor at Third Door Media, primarily covering organic and paid search, podcasting and e-commerce. His background is in journalism and content marketing. Prior to entering the industry, he worked as a radio personality, writer, podcast host and public school teacher.


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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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