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Measuring diversity in advertising: A challenge of scale

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Measuring diversity in advertising: A challenge of scale

“The vast majority of people, when they look at ad content, say they don’t see themselves in it, they don’t see themselves represented. We spend all this time and money telling stories and yet most of the people who look at them don’t see themselves in it. Not only is this clearly inefficient, it’s also quite alienating.” Comments from Anastasia Leng, founder and CEO of CreativeX.

CreativeX’s software provides AI-powered analysis of visual content — images and video — at scale, enabling brands to make data-driven creative decisions, supporting quality, brand consistency and compliance. Recently, it has turned its attention to driving insights into representation in partnership with Geena Davis Institute for Gender in Media. The Institute’s mission is to create gender balance, foster inclusion and reduce negative stereotyping in family entertainment and media. Creative X isn’t just looking at gender, however. “We’re looking at gender, skin tone and age range,” said Leng.

The state of representation

Using its proprietary Representation technology, CreativeX last week released results from an analysis of some 3,500 ads containing images or video (from 2021 and U.S. only). Among the findings:

  • Although 55% of ads featured women, men were 1.5X more likely to be shown in professional environments;
  • Individuals with light to medium skin tones featured twice as often in professional environments; and
  • Individuals in the over-60 age range featured hardly at all (around 1% of the ads) despite their formidable disposable income.

Madeline Di Nonno, CEO of the Geena Davis Institute explained: “What we have found is that, since 2016, there has been a very serious intent by some of the leading global brands – like P&G, Google, Facebook, Mars – to invest in people, to invest in resources and invest in process in order to come up with ways for them to not only absorb the information, but for them to figure out what’s going to work so that we can have improvements.”

Leng confirmed this from her experience with her own clients. “The intentions are there. The other big trigger is that consumers are pushing them in the right direction. Consumers care more than ever.”

So what’s the problem?

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The problem is scale

One of the major obstacles to meaningful change is the scale and complexity of ad creative when it comes to global brands like the ones Di Nonno referenced — brands with significant influence on the culture. “It’s been very challenging, especially when you’re dealing with global organizations and you have different regions, different entities around the world. These infrastructures are so big. How many brands, how many ads per brand per year? You’re talking about thousands and thousands, and the pace is so fast.”

That’s where CreativeX comes in, with its use of AI to automate the analysis of vast quantities of creative. “We connect all the different places where they’re running advertising to our system. This then allows us, via APIs, to pull in all of their content. What we then do is look through all their images and videos and basically append a bunch of metadata that helps us determine everything from who is in the creative – what kinds of people – all the way down to what settings they’re in, percentage of men versus women, under 21, over 60, and differences around stories you are telling about men versus women.”

It’s easy for brands to find excuses, Leng said. “You do a bit of research on a small sample size and people say, ‘But you didn’t look at all my content.’ In this case, we’ve taken those excuses off the table. We can look at all your content.” For some large brands CreativeX has so far looked at U.S. content only, Leng said.

She added: “We had an instance of one brand which said ‘We are the most diverse financial brand on the high street.’ We looked at all of their content – tens of thousands of pieces of content – and there was not a single person of color in a single one of their ads.” An extreme example, she admits. “The reason I talk about it is not because they were lying to us, not because they didn’t care, but when you have a large brand with thousands of marketers distributed across multiple locations, and a bunch of agencies, unless you have the systemic infrastructure to track this – everyone thought they were being diverse but no-one was actually doing it.”

Read next: When it comes to women, marketing is behind the times

The goal is systemic change

CreativeX and the Geena Davis Institute happen to share some clients, but the way the partnership is intended to work is that Geena Davis can introduce major global media and entertainment brands to the potential of CreativeX’s technology, while CreativeX can point its clients toward the Institute for advice and counseling on their representation policies.

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Ultimately, the goal is to drive systemic change, said Di Nonno. “We’ve been doing what we define as narrative culture-change work since 2004, we’ve had the privilege of being able to expand across many global verticals. We started doing global work in advertising in 2015, a result of me having the privilege of being the second jury president for the Glass Lions.” (The Glass Lion “recognises work that implicitly or explicitly addresses issues of gender inequality or prejudice, through the conscious representation of gender in advertising.”)

“However, as a research institute that is data-driven,” she continued, “when you think about the massiveness and volume of global advertising, to really come up with a turnkey, systemic auditing solution, there’s no way we could ever scale to be able to embrace that. So, with Creative X having created their representation project and having the opportunity to join forces, it really allows us to continue to do what we’re doing but also to have that scalability.”


About The Author

The holiday season is upon us
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.”

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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.”

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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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