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Why AI-generated content still needs the human touch

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Why AI-generated content still needs the human touch


AI has already made an impact in the marketing content industry, with AI-generated content filling in the gaps for all kinds of assets, from simple product descriptions to more complex news articles to books and even films—just ask the Writers Guild of America.  

No surprise then that so many digital marketers and content creators are on red alert and asking questions about the future. Will it take over my job? Will it take over the world? The short answer is no—and we’re going to explain why. 

As far as the tech itself is concerned, it’s still early days. A robot won’t be sitting at your desk tomorrow morning, sipping fresh oil from your personal coffee cup. And if you wake up in a cold sweat after being chased by a terminator carrying a keyboard rather than a phased plasma rifle… well, we think we can help you sleep a little easier.  

What is AI-generated content? 

First off, let’s make sure we’re all on the same page. Artificial intelligence refers to algorithms that generate content without human assistance. AI content generators use a variety of methods such as machine learning, natural language processing, and neural networks. 

BUT. 

AI-generated content is not a like-for-like replacement for human content marketers. Rather than a threat, we see it as a tool that makes life easier and boosts productivity. 

The rise of AI in content creation for marketers 

AI-generated content has become increasingly popular, especially in digital marketing where it’s already proving to be a real game-changer. Advancements in natural language processing are making AI-generated content more human-like. Improvements in machine learning algorithms are making it more reliable. At first glance, it’s easy to see why the market for AI-generated content is expected to keep growing fast.  

It’s efficient 

AI content generators help content teams to generate content much more quickly. By streamlining the content creation process, they allow marketers to focus on the more value-adding, enjoyable, and creative aspects of their roles.  

It’s insightful 

AI content software is capable of analyzing user interactions, identifying the topics of most interest to your audiences, and even tweaking content strategies. Basically, will generate targeted marketing content while you’re out grabbing some lunch.  

Multilingual 

For brands that do business overseas, AI content systems can also deliver content in multiple languages, saving the time and expense of hiring translation or transcreation agencies. 

Accurate 

AI don’t do the grammatical errers or spilling typos what compromise the quality and credibility of your content. Algorithms can also improve the engagement levels of your texts. It can even suggest improvements such as simpler words and shorter sentences.  

All in all, if AI had feelings, then no doubt it’d be feeling very smug about itself. But it shouldn’t get too carried away…not just yet. 

The challenges and limitations of AI-generated content  

So yes, AI has the potential to revolutionize the way we produce and consume content. Yet just like any technology, there are limitations to what AI-generated content can achieve. You see, it also raises very real issues such as contextual errors, ethical concerns, and ownership of content, in addition to a distinct lack of originality, creativity, and accountability. Not to mention the lack of that human touch that technology simply can’t replicate.  

Lack of creativity 

Artificial Intelligence can quickly produce large quantities of content, but this is based on pre-loaded data and often relies on pre-existing templates or algorithms. AI tools basically create a mash up of information snaffled from different websites and sources. And it’s exactly because they use existing data that these tools are unable to think up fresh ideas or original content. They’re unable to think, full stop.  

The results can sound pretty formulaic as well as blandly repetitive. For example, an AI program can generate a thousand product descriptions without a second thought. But if you’re hoping for a sassy, attention-grabbing tagline, you’ll be left disappointed. 

Lack of originality 

Free-thinking humans (some, at least) are equipped with the imagination to come up with new and original ideas. Put simply, AI isn’t. We can inject personality and voice into our writing. AI can’t. However smart and advanced they may be, algorithms can only do what they’ve been programmed to do. To any developers out there, please don’t enter ‘take over the world’ even as a joke.  

Sure, researchers are working on AI that can generate more creative content, but we’re still miles and years away from anything that can truly compete with the creativity of the human mind.  

Lack of context  

AI also has a bit of a reputation for struggling with context, especially when it comes to cultural nuances and social references. As humans, we can usually discern that certain wordplays, memes, or slang that hit the mark in one country or community may not go down so well in another. A limited scope for understanding cultural references also means, of course, a limited scope for including them—yet they are exactly the kind of thing that make communications relevant, engaging, and effective. 

Ethics and bias concerns 

AI is also surrounded by significant concerns around ethics and bias. Needless to say, algorithms don’t set out to produce biased, hurtful, harmful, prejudiced, or discriminatory content. They have no mind, no opinions, no preferences for one over the other. Yet AI-generated content is only as transparent and unbiased as the data on which the system is trained. And because they mindlessly collate and repeat existing information that itself may not have been checked (or has even been deliberately created to perpetuate bias), AI content generators are vulnerable to spreading and amplifying biases and discrimination.  

Let’s take one simple example. An AI program may be designed to prioritize metrics such as engagement or click-through rates, yet this could well come at the expense of accuracy or relevance.  

Or say an AI system has been trained on a dataset that lacks representation from a certain group or race. As a consequence, it isn’t farfetched to imagine the resulting content could show a bias towards that group or race. 

In fact, there is no shortage of challenges… 

AI generated content also faces myriad other challenges such as a lack of accuracy—facts and figures may be out of date, for example. We’ve touched on the unwitting spread of misinformation, fake news, and propaganda. Then there’s a complete inability to produce inspiring and evocative content that resonates with audiences on an emotional level. And a potentially damaging vulnerability to plagiarism, because again, AI draws on existing sources.  

And finally, there’s the fact that search engine crawlers don’t much like AI-generated content. For example, in February 2023 Google released an updated set of guidelines that place the emphasis on “helpful content written by people, for people.”  

When it comes to automatically generated content, our guidance has been consistent for years. Using automation—including AI—to generate content with the primary purpose of manipulating ranking in search results is a violation of our spam policies.” 

The essential role of the human touch 

AI-generated content can be a useful tool for producing reams of content at speed, yet that doesn’t come without its challenges. After all, we need to remember we’re talking about nothing more than a machine. And all too often, that’s just how the final content can sound.  

In contrast, high quality marketing copy is about the art of persuasion: pulling the right strings to bend the consumer to your will. Whether those strings come in the form of brand personality, humor, insight, excitement, emotion, or empathy, they all have one thing in common: the human touch.  

“Only humans possess the ability to relate to audiences on a personal level. Only humans can step into the audience’s shoes and understand their needs, desires, and pain points. Only humans can create compelling content that resonates, engages, and builds relationships.”  

By drawing on our own experiences, emotions, and perspectives, humans can inject creative techniques to capture and keep the reader’s attention. In contrast, AI simply cannot craft content that speaks directly to people, ignites emotions, and prompts audiences into taking action. To produce genuinely effective content, therefore, we need to combine the efficiency of AI with the creativity of the human mind.

How to keep your content human  

What you get from AI is never the end of the process, but only the beginning. The real work lies in going through that rump of content and tailoring it to your brand tone, your audiences, and your messaging. That’s why it’s so essential to have human oversight: only you can inject the personality that makes your content feel authentic.  

Remove the repetition 

In combining what amounts to a series of short sentences, AI content generators can often be guilty of repeating specific words or phrases. AI content generators can therefore sound slightly unnatural. AI content generators can even sound robotic. AI content generators therefore require aid in sounding more 

well… human. Right? 

Go with the flow 

Let’s face it, AI-generated content can sound staccato. It tends to overuse simple words such as ‘it’ and ‘the’. And as we’ve just noted, it can display a distinct preference for short sentences that don’t always flow. That’s why it’s worth going through to add natural transitions or using commas to extend the odd sentence. Anything that helps create a smoother, more readable, more human-sounding piece. 

Check the facts 

Since AI writing tools don’t actually understand the text they generate, they can hardly be expected to fact check them either. Plus, they are not always up to date with (***gratuitous cultural reference alert***) everything everywhere all at once. Get the right kind of fact wrong and you risk your credibility at the very least. 

Make it personal 

Anyone can enter a few prompts and create a blog, say, or a product description. It’s easy. It’s fast. But in general, it’s also likely to be generic, bland, and impersonal. AI content is based on algorithms and, as a consequence, it lacks personality. That’s where you come in. You could start by adding key ‘on brand’ words or phrases before weaving in industry jokes, quotes from colleagues or peers, or other relevant touches that make the content unique to you.  

Add a viewpoint 

AI can lay out the stats, the facts, and the figures (but given what we’ve covered above, don’t forget to check them). What it can’t do is use them to form an opinion. That’s why a truly rounded, natural-sounding text will reflect your unique point of view, based on your personal knowledge and experiences, 

Conclusion: AI needs you 

<Enter Prompt>  

Cut a long story short please 

AI is great for producing large amounts of copy quickly and efficiently. It’s great for overcoming writer’s block. And great for generating relevant ideas for new pieces of content. Yet it also faces a number of limitations and potential issues, as well as lacking that most human of qualities: creativity.  

These limitations emphasize the essential role of human oversight. By combining the strengths of AI and human expertise, we can produce quality marketing content that is accurate, engaging, and persuasive.  

Think of AI-generated content as a block of marble, which you then sculpt according to your plan or vision.

Quality content will always be in demand, as will the content writers and marketers able to produce it. And while AI-generated content may replace some jobs (or some aspects of some jobs), it also opens new opportunities for content curation and editing.  

It all means that AI is more of a tool than a threat. And to create the most effective content possible, the powers that be should look on AI as your assistant, not your replacement.


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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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