MARKETING
Do Better AI Prompts Translate to Better Outcomes?
Around the turn of the 21st century, Web 1.0 came online. (Well, not the 1.0 part since no one knew it would have versions.)
Everything centered on the “net.” You logged onto the net using the Netscape browser. You made calls with your net phone. Microsoft created dot-net software. ZDNet and CNet published tech news. Digital music was called “net music.” Sandra Bullock starred in The Net, a movie about hackers.
The net was everywhere in the late 1990s and early 2000s.
In 2024, the net doesn’t get many mentions. Today’s conversations center on prompts — prompts for generative AI. “You need to get better at prompts.” “You need prompt engineering.” “You need to be the best prompter in all of prompting to get good at creating content.”
But you know what? Knowing how to create prompts for generative AI is akin to knowing how to create HTML codes in the early days of the net.
That’s what CMI’s chief strategy advisor Robert Rose says and what prompted him to give his take on the topic. Watch the video or read on for his thoughts:
Pondering prompt results
AI-generated content has gained traction in marketing content these days. I see more and more images and text clearly generated by AI. The images are easier to spot because the AI generators create a certain look and appear frequently on Facebook and LinkedIn.
Play “Where’s Waldo?” with them, and you can usually see relatively odd components. See what I mean in this image accompanying a blog post about teams working together to edit a document.
Seventeen people sit and stand in an office-looking room with big windows. Eight of them sit at a big table with two open laptops, marker-filled cups, a tablet, coffee mugs, books, and papers. Most of the others look at the table. Nearby sits a flip chart, a few more desks, and posters on the painted brick walls as sunlight streams through the windows.
I have so many questions:
- Why do all the men have beards?
- Is that a picture of a raccoon on the back wall?
- What does that poster next to the raccoon say?
- Is that a small person with no legs sitting on top of that table in the back?
- What is wrong with the woman in the front right?
- Does the woman on the left have a computer mouse surgically attached to her left forefinger?
But I digress.
What about AI-generated text? It’s replete with adjectives and flowery language. When I prompted ChatGPT to write about content marketing and open with a story to establish context, it created:
“Once upon a time, in the bustling boardrooms of a Fortune 1000 company, there was a vice president of marketing, Alex, who faced a daunting challenge. Sales were stagnating, and traditional marketing methods were losing their luster.”
Yes, ChatGPT does love alliteration. In this case, it used three in two sentences.
In any event, many would suggest the problem with AI images and text lies in the prompts provided to the generative AI tool.
What’s in a prompt?
A hot topic among marketers, prompts have become a commodity as thought leaders sell or give away their best classic Mad Libs fill-in-the-topic format. Some suggest you tell the generative AI who it should be, such as “Pretend you’re a librarian” or “Pretend you’re a world-class aviator coming out of the Top Gun school in San Diego.”
(I tried that last one and asked for the No. 1 piece of advice. That prompt earned this response: “Embrace the challenge.” Whew, that’s good stuff and no threat to Tom Cruise.)
At the Content Marketing Institute and The Content Advisory, we have dived deep into the world of generative AI, testing the tools and understanding the strengths and weaknesses of the learning models to generate content.
My conclusion: Creating the best value from generative AI has NO relevance to being an expert at prompting.
That’s not to say that learning to ask better questions — what you really want to know — won’t get better and more valuable answers. That’s possible whether you ask AI or humans.
But aside from asking for specific looks for images and providing words to help the AI understand jargon, prompting diminishes returns beyond the most basic levels. If generating good AI output is relegated to only those who can “code” good prompts, then it’s not the disruptive technology everybody believes it to be.
The Dunning-Kruger effect also emerges to create a problem with AI-generated content. People overestimate their ability or knowledge. But knowing how good you are at something requires the same skills as being good at it in the first place. With AI-generated content, people think it’s higher quality because they don’t understand what “good” looks like.
Make this your go-to prompt
I know a company that recently replaced its customer content-enablement team with generative AI and a freelancer. After they prompted the generative AI, it created scores of new customer-enablement content the freelancer published to the website. There was only one problem. The content gave wrong information about the products, technology, and how things worked. The freelancer couldn’t discern the content’s accuracy. While the content was impressively written, it was just plain wrong.
As you consider integrating generative AI into your content strategy, remember my prompt: Do not become prompting experts. Drive hard to become experts in your subject matter and create awesome human content with an AI assist originated from simple, easy-to-remember prompting.
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Cover image by Joseph Kalinowski/Content Marketing Institute
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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