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MARKETING

The Rise in Retail Media Networks

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A shopping cart holding the Amazon logo to represent the rise in retail media network advertising.

As LL Cool J might say, “Don’t call it a comeback. It’s been here for years.”

Paid advertising is alive and growing faster in different forms than any other marketing method.

Magna, a media research firm, and GroupM, a media agency, wrapped the year with their ad industry predictions – expect big growth for digital advertising in 2024, especially with the pending US presidential political season.

But the bigger, more unexpected news comes from the rise in retail media networks – a relative newcomer in the industry.

Watch CMI’s chief strategy advisor Robert Rose explain how these trends could affect marketers or keep reading for his thoughts:

GroupM expects digital advertising revenue in 2023 to conclude with a 5.8% or $889 billion increase – excluding political advertising. Magna believes ad revenue will tick up 5.5% this year and jump 7.2% in 2024. GroupM and Zenith say 2024 will see a more modest 4.8% growth.

Robert says that the feeling of an ad slump and other predictions of advertising’s demise in the modern economy don’t seem to be coming to pass, as paid advertising not only survived 2023 but will thrive in 2024.

What’s a retail media network?

On to the bigger news – the rise of retail media networks. Retail media networks, the smallest segment in these agencies’ and research firms’ evaluation, will be one of the fastest-growing and truly important digital advertising formats in 2024.

GroupM suggests the $119 billion expected to be spent in the networks this year and should grow by a whopping 8.3% in the coming year.  Magna estimates $124 billion in ad revenue from retail media networks this year.

“Think about this for a moment. Retail media is now almost a quarter of the total spent on search advertising outside of China,” Robert points out.

You’re not alone if you aren’t familiar with retail media networks. A familiar vernacular in the B2C world, especially the consumer-packaged goods industry, retail media networks are an advertising segment you should now pay attention to.

Retail media networks are advertising platforms within the retailer’s network. It’s search advertising on retailers’ online stores. So, for example, if you spend money to advertise against product keywords on Amazon, Walmart, or Instacart, you use a retail media network.

But these ad-buying networks also exist on other digital media properties, from mini-sites to videos to content marketing hubs. They also exist on location through interactive kiosks and in-store screens. New formats are rising every day.

Retail media networks make sense. Retailers take advantage of their knowledge of customers, where and why they shop, and present offers and content relevant to their interests. The retailer uses their content as a media company would, knowing their customers trust them to provide valuable information.

Think about these 2 things in 2024

That brings Robert to two things he wants you to consider for 2024 and beyond. The first is a question: Why should you consider retail media networks for your products or services?   

Advertising works because it connects to the idea of a brand. Retail media networks work deep into the buyer’s journey. They use the consumer’s presence in a store (online or brick-and-mortar) to cross-sell merchandise or become the chosen provider.

For example, Robert might advertise his Content Marketing Strategy book on Amazon’s retail network because he knows his customers seek business books. When they search for “content marketing,” his book would appear first.

However, retail media networks also work well because they create a brand halo effect. Robert might buy an ad for his book in The New York Times and The Wall Street Journal because he knows their readers view those media outlets as reputable sources of information. He gains some trust by connecting his book to their media properties.

Smart marketing teams will recognize the power of the halo effect and create brand-level experiences on retail media networks. They will do so not because they seek an immediate customer but because they can connect their brand content experience to a trusted media network like Amazon, Nordstrom, eBay, etc.

The second thing Robert wants you to think about relates to the B2B opportunity. More retail media network opportunities for B2B brands are coming.

You can already buy into content syndication networks such as Netline, Business2Community, and others. But given the astronomical growth, for example, of Amazon’s B2B marketplace ($35 billion in 2023), Robert expects a similar trend of retail media networks to emerge on these types of platforms.   

“If I were Adobe, Microsoft, Salesforce, HubSpot, or any brand with big content platforms, I’d look to monetize them by selling paid sponsorship of content (as advertising or sponsored content) on them,” Robert says.

As you think about creative ways to use your paid advertising spend, consider the retail media networks in 2024.

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Cover image by Joseph Kalinowski/Content Marketing Institute

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MARKETING

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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