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How To Use AI-Generated Content the Right Way (and Avoid the Downsides)

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We’ve been watching AI take human jobs for a while now — industrial, manufacturing, and even financial industries have been massively disrupted by the ability of machines to think like humans. But what about creatives? Are they at risk too? Could AI-generated content replace human writers any time soon?

Some marketers and innovators say yes. New technology tools powered by OpenAI’s GPT technology started the swell of AI-generated content on search engine results pages and elsewhere on the internet. Chatter about massive cost savings propelled by AI-written articles has put content creators on watch for signs their jobs may be at risk.

But is the hype real? Should we all be worried that content marketing as we know it is being taken over by machines?

If you ask me (and the powers-that-be at Google), the answer is no — at least not any time soon. I’ll explain why and share an experiment of my own.

#AI-generated #content has improved, but it won’t replace human content creators any time soon, says @BrennerMichael via @CMIContent. Click To Tweet

State of AI-generated content in 2022

Computers have used natural-language generation (NLG) to create text for decades. It’s only in recent years, however, that the technology’s become sophisticated enough for marketers to talk about its potential for content creation.

When OpenAI released the GPT tool (short for Generative Pre-trained Transformer) in 2020, it seemed like the potential may be realized. Since then, two more updates (GPT-2 and GPT-3) have been released, and GPT-4 is expected to be released in the coming months.

Each version has gotten progressively better at producing text that reads as if a real human wrote it. The release of GPT-3 resulted in an exponential jump in skill and accuracy as compared to GPT-2.

The upgrade from GPT-2 to GPT-3 resulted in an exponential increase in skill and accuracy capabilities.

Image source

GPT-produced content is presumably floating around the internet without being recognized as AI-generated (at least not by human readers). Its prevalence will only continue to grow in the coming years.

But will GPT-produced content replace human writers anytime soon? Unlikely.

AI-generated content still lacks the necessary nuance to meet high-quality content marketing standards. Not to mention that Google still says it’s spam.

#AI-generated #content lacks the necessary nuance for high-quality content, and @Google still says it’s spam, says @BrennerMichael via @CMIContent. Click To Tweet

For content marketers living by the laws of Google (AKA every content marketer), that’s a deal-breaker. If Google doesn’t rank AI-generated content, AI content creators simply can’t replace human content creators.

Google’s take on AI-generated content

During a recent Google SEO Hangout, senior webmaster trends analyst John Mueller left no room for wondering. In short: AI-generated content breaks from the webmaster guidelines Google’s had “since almost the beginning.”

The question was posed by Reddit moderator Rohan Chaubey. He referenced a recent thread on which John had commented that creators should not be using AI-generated content but failed to elaborate. When asked for clarification, John said:

My suspicion is that maybe the quality of content is a little bit better than the really old-school tools, but for us it’s still automatically-generated content, and that means for us, it’s still against the Webmaster guide. So we would consider that to be spam.

So there you have it. I’ve seen some interesting commentary about this exchange that suggests Google is hypocritical about AI-generated content. After all, they’re one of the most AI-powered companies in the world. Why wouldn’t they support a tool that allows marketers to use the same technology?

It’s easy to assume Google’s worried their tools wouldn’t distinguish AI-generated content from its human-created counterpart — and maybe it’s true to some extent. But I don’t think that’s the motivation for their position on this subject.

Google has long been a proponent of human-centered content. We’ve all heard it — don’t write for the algorithms; write for your actual audience. Google has developed algorithms specifically to recognize real human value and even given creators guidelines (like EAT and YMYL) to understand how they’re evaluating said value.

AI-generated content (at least in its current form) compromises the central tenant that Google (and all of us as content marketers) have long operated under: Content is more valuable than traditional advertising because it’s human-centered. It helps people — whether they’re about to make a purchase or not.

If Google approved and ranked AI-generated content, it would lose the quality standard that’s given them a more than 90% share of the search engine market.

My AI content experiment

I was pretty confident in my take on this after years of following the progress of GPT and other tools. Still, I thought it best to test GPT and see what it came up with.

I used one of the most foundational topics relevant to my business: why is content marketing important? I asked GPT to write a blog intro about it. Here’s what I got:

Content marketing is a strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience — and, ultimately, to drive profitable customer action.

When executed correctly, content marketing can be an extremely effective way for companies to connect with their target audiences, build trust and credibility, and boost sales. Additionally, content marketing can help companies to position themselves as thought leaders in their industries, which can further help to attract new customers and drive business growth.

After reading it, my previous confidence in my take on AI-generated content dipped. Of course, my prompt was simple, but this text is totally acceptable for a blog intro. No grammatical errors. Reads to me as if a human wrote it.

Then I did two things.

First, I had a friend enter the same inquiry on a separate GPT account. Their intro was extremely similar to mine. Not a total red flag — both were accurate, and as we know blog articles often include similar content about common topics — but not ideal. I’d rather not publish blog posts that start almost exactly the same as others ranking for the same keywords.

#AI created an adequate blog intro, but it was too similar to another AI-created intro on the topic to stand out in search, says @BrennerMichael via @CMIContent. Click To Tweet

Then, I asked GPT to write me a long-form article on the topic. This time, I got an OK article, but it was generic. Points were repeated, nuance (like personal commentary or real-world examples) was absent, and no new perspectives were included.

My takeaway: AI-generated content tools like GPT can be useful now. But they’re definitely not ready to replace humans.

Gain benefits using GPT and similar AI tools

Right now, AI-generated content fits better into the role of a tool writers can use and not the role of the writer.

#AI-generated #content fits better into the role of a tool writers can use and not the role of the writer, says @BrennerMichael via @CMIContent. #ContentCreation Click To Tweet

As I wrote this article and tried out GPT, a couple of beneficial use cases came to mind:

Fight writers’ block

The blank page can be daunting for even the most seasoned content writers. Asking a tool like GPT for some blog intro ideas could be just the thing to get ideas flowing for a new piece.

Create an (extremely rough) first draft

I thought the full article I requested from GPT provided some bare bones for an article. I could see it as a sort of outline generator for companies to share with writers who could add their spin to it.

Conduct AI-powered research

I did a final experiment with GPT for an article “with statistics” about digital marketing. The result was a draft with tons of stats — complete with sources — on the subject. The screenshot shows five statistics, which is only about a quarter of the ones included. It includes:

  • $146.6 billion is spent on digital marketing in the United States, according to eMarketer.
  • 80% of people would rather buy a product or service online than in-store, as reported by HubSpot.
  • 57% of consumers won’t recommend a business with a poorly designed site from Adobe.
  • 75% of people judge a company’s credibility based on its website sign, according to HubSpot.
  • Search is the No. 1 driver to content sites, beating social media by more than 300%, as reported by Compete.

AI-generated content from GPT-3 shows a list of statistics about digital marketing.

Image source

This AI-generated content could drive a powerful initial direction for an article that’s quicker to identify than sifting through full-length articles found in Google search rankings.

#AI-generated #content can be helpful to fight writer’s block, create a broad outline, or find statistics on your topic, says @BrennerMichael via @CMIContent. Click To Tweet

Why we still need humans to write

My final thoughts led me back to a core tenet of value-driven marketing: empathy. No one knows my audience better than me, and no one knows yours better than you. Especially not a machine — no matter how smart said machine might be.

AI-generated content as a replacement for human writers is still a long way off (if not totally impossible) because it lacks the ability to channel empathy into the content. What you get from GPT (and the like) is an accurate but generic summary of what’s already been said on a topic rather than a piece written specifically to provide new perspectives to your audience.

Your customers are way too savvy for that kind of content, and frankly, it doesn’t meet the standards set by brands to provide value. For the foreseeable future, human-centered and human-created content still wins.

HANDPICKED RELATED CONTENT:

All tools mentioned in the article are identified by the author. If you have a tool to suggest, please feel free to add it in the comments.

 Register to attend Content Marketing World in Cleveland, Ohio. Use the code BLOG100 to save $100. 

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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