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Is OpenAI’s Custom GPT the Next Frontier?

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Is OpenAI’s Custom GPT the Next Frontier?

Are you confused about what you should do with generative AI?

Well, the news from the first developer conference organized by OpenAI, the maker of ChatGPT, could take that to the next level.

The biggest announcement? You can make your own GPT. That’s right. Want your own personal version of ChatGPT to do something for you? Well, the tech now exists.

Watch CMI’s chief strategy advisor Robert Rose explain, or keep reading his thoughts:

Almost one year ago, OpenAI publicly launched ChatGPT. You could ask questions, ask a follow-up to the answer provided, and ask it to create content by simply chatting with the interface.

Those early days brought the first use cases (subscription required) and warnings it would replace customer service chatbots and Google’s search engine. Two weeks after the announcement, people discovered ChatGPT could write original content. Being only human, of course, people immediately went to the ridiculous. They asked it to explain how to make a peanut butter sandwich in the style of the King James version of the Bible or to explain how AI works as an annoying guy who can’t stop going on tangents.

A few weeks later, people got a bit more serious (subscription required) and realized ChatGPT could write code, essays, or news stories even though the learning model’s knowledge only went through 2021. Toward the year’s end, people worried students would use it to chat, reporters would become obsolete, and coding would be a dying skill.

But by early 2023 – months after the initial announcement – marketing teams played with it. At companies of all sizes, they let out a huge, collective sigh. ChatGPT could produce decent short-form content. You saw all kinds of startups emerge that would magically write your ad copy and blog posts, adjust your SEO, create your persona research, write your landing pages, develop your FAQs, and more. ChatGPT could do everything. Content people worried their jobs would disappear. Everybody wondered what would happen next.

Almost a year later, OpenAI says ChatGPT has 100 million weekly users, and what’s next has arrived: You can build your own GPT to fit how you want to use it.

In a custom GPT demo, OpenAI used customized learning material to provide a “creative writing coach.” In another example, it used a limited set of focused learning materials to help attendees navigate the developer conference.

As a side note, Robert has developed a GPT called Content Marketing Companion. It uses his four books and classes on content marketing for the learning model. He can ask it basic questions on content marketing strategy. The results?  Pretty good but not mind-blowing (yet).

“The real value seems to lie in giving your custom GPT the ability to act, such as browsing a website, sending an email, or letting it access other tools such as Zapier or Canva to perform these tasks,” Robert says.

Embrace the long, innovative vision of GPT

Consumers are still reeling with the practical use cases for ChatGPT, and so you want to go to things you recognize. As you realize, most of the use cases are simplistic, like Robert’s Content Marketing Companion.

Think about your content library – blog content, customer help documentation, social media posts, etc. By giving a custom GPT access to that knowledge, you could create your brand’s own content marketing companion.

In the long run, you can’t differentiate yourself with such simple uses because anyone can make them. Someone else could easily re-create Content Marketing Companion with their own or even Robert’s content. Use cases and instructions already exist on how to build custom GPTs for SEO and marketing.

“Truly innovative outcomes of a custom GPT don’t yet exist because people haven’t even begun to envision them,” Robert says. OpenAI believes this, too, as it also announced plans for what is ostensibly an app store for custom GPTs.

What should you and your marketing team do?

Well, the thousands of original research projects into how marketing teams use generative AI make one thing clear: Marketers are still just playing around with it at the end of 2023.

“As my own research notes, though, it’s people, not teams, doing the experimenting,” Robert says. “With few exceptions, marketers are not looking at how to integrate AI into your team’s workflow and systems yet. You’re trying to figure out how it works for individuals to make their jobs easier.”

But that’s what people have always done with every new technology, including the original ChatGPT almost one year ago.

See GPT as a content strategy challenge

For businesses, generative AI – custom or off the shelf – is a content strategy challenge, not a technology challenge. Play. Experiment. Try out custom GPTs. But do it in the context of how it makes your business better, and let that imagination be free.

Leave the development of the custom GPT that will speak Klingon language to the nerds who nerd out on such things. (By the way, I developed that custom GPT, calling it Klingon Language Tutor.)

In any event, challenge your marketing team to stop interpreting these new developments from the lens of how the advancements can make their jobs better or easier. “You need to put on the lens of what opportunities you have to make your business better,” Robert says. “To do that, you must have standards, guidelines, and the people processes and technologies that already make your business good.

“If you can’t answer what makes your content and marketing strategy good, no customized GPT is going to make it any better.”

That’s it. Until then, Robert says, “Qapla’ batlh je.” (For the non-Klingon readers, that translates to “success and honor to you.”)

Please note: All tools mentioned in this article were suggested by the article’s author. If you’d like to suggest a tool, share the article on social media with a comment.

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Cover image by Joseph Kalinowski/Content Marketing Institute

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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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