MARKETING
Can AI-Generated Content Hurt Your Search Ranking? [Insights from HubSpot, Litmus, Casted & 500+ Marketers]
![Can AI-Generated Content Hurt Your Search Ranking? [Insights from HubSpot, Litmus, Casted & 500+ Marketers] Can AI-Generated Content Hurt Your Search Ranking? [Insights from HubSpot, Litmus, Casted & 500+ Marketers]](https://articles.entireweb.com/wp-content/uploads/2023/08/Can-AI-Generated-Content-Hurt-Your-Search-Ranking-Insights-from-HubSpot.pngkeepProtocol.png)
AI is giving marketers a newer, faster, and easier way to create content. But will the convenience of AI come at the expense of your search ranking?
After all, if everyone can use AI to create high-quality content at lightning-fast speeds, what will be the great differentiator in search results?
Here, we’ll explore everything you need to know about AI-generated content — including how it performs in search results, its limitations, and tips for leveraging it.
Table of Contents
Can AI-generated content impact my search ranking?
The Limitations of AI-Generated Content
Tips for Using AI-Generated Content
Can AI-generated content hurt my search ranking?
It’s a question on every marketer’s mind: should I use AI to create content?
At this time, Google has made it clear that AI-generated content has no impact on search ranking. As long as your content is helpful, original, and relevant, you have the green light.
In other words, Google is less concerned about how content is produced and more concerned with the quality of the content itself.
Using the E-E-A-T framework, Google will continue to reward content that is:
- Helpful
- Demonstrates expertise
- Published on an authoritative site
- Trustworthy
But here’s the rub: AI-generated content may not check all those boxes.
“Now, more than ever, the value of content hinges on the authenticity of its creator and the underlying value, meaning, story, and perspective of the content they’re creating,” Josh Blyskal, Associate Marketing Technical Manager at HubSpot, told me.
As the internet becomes flooded with AI-written content, the real hurdle is standing out from the masses.
The Limitations of AI-Generated Content
While AI can speed up the content creation process — which certainly has SEO benefits — it’s not a self-driving technology.
For instance, many AI models cannot discern whether the information it collects from the web is correct or not. It may pull information that is inaccurate, unreliable, or even biased. In a similar vein, AI models rely on limited data.
ChatGPT, for example, is limited to data before 2021. If you ask it to write an article about, say, “The Top TikTok Stats of 2023,” it will generate an article with outdated data. Google will then prioritize articles with more current information, potentially impacting your search ranking.
Then there’s the issue of duplicate content. Imagine thousands of marketers asking ChatGPT the same question and pasting its response into their content. When Google crawls these pages, it may determine that the content is unoriginal or lacking expertise.
This isn’t to suggest AI-generated content is inherently bad. In Blyskal’s own words: “If I read an article, liked it, derived value from it, and then discovered that the article was written by AI, would I care? I’m not sure I would.”
However, AI-written content alone may not be enough to compete in search engines. To stand out, you must combine AI with human expertise and perspective.
Tips for Using AI-Generated Content
1. Be transparent.
“While it may seem counterintuitive, being transparent with your audience about how you use AI is paramount for building trust, especially as we move into an AI-integrated world,” Kimberly Huang, Content Marketing Manager at Litmus, told me.
For example, in Litmus’ recent newsletter, the team used generative AI to help create content ideas. They ended the email by sharing, “This email was written using Jasper.”
Even adding a simple line of copy that lets your audience know you leverage AI can go a long way in fostering trust.
2. Lean into thought leadership.
It’s no secret that marketers operate in high-pressure, deadline-driven environments. While it’s tempting to rely on AI for a quick content fix, this approach does little to foster genuine connections around your brand.
This is why Lindsay Tjepkema, CEO and co-founder of Casted, leans into thought leadership.
She told me, “Generative AI may offer a short-term solution, but it’s not a sustainable means to champion your brand’s story or build genuine customer relationships.”
“Instead, marketers should build their strategies around human-centric, authentic thought leadership content like podcasts, webinars, and video content. Then, amplify that content across all other marketing channels to extend their reach,” she continued.
3. Keep an eye on newly automated processes.
“Tools like ChatGPT are creating opportunities for automating and scaling processes that used to take a lot of time to complete,” Rory Hope, Head of Content SEO at HubSpot, told me.
“These automations can analyze content and improve insights, or produce content with the intention of ranking highly on a search engine,” he added.
For instance, marketers can leverage tools like HubSpot’s Campaign Assistant to generate outlines and first drafts, enabling them to streamline parts of the writing process.
Another area is keyword research. Consider this: Meta doubled its monthly search traffic by using an AI-powered SEO tool to target non-branded keywords. It also conducted website audits — powered by AI — to remedy broken links, missing metadata, and slow page speeds.
That said, Hope cautions that AI still requires human oversight. He told me, “We should be experimenting with generative AI, but human SEOs should stay in the loop to review the quality of newly automated processes.”
Putting It All Together
To excel in this evolving SEO landscape, marketers should combine the power of AI with human oversight, expertise, and perspective. By putting high-quality content at the forefront, marketers can stay competitive in the next era of search.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
![YouTube Ad Specs, Sizes, and Examples [2024 Update] YouTube Ad Specs, Sizes, and Examples](https://articles.entireweb.com/wp-content/uploads/2024/06/YouTube-Ad-Specs-Sizes-and-Examples.jpg)
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
![Why We Are Always 'Clicking to Buy', According to Psychologists Why We Are Always 'Clicking to Buy', According to Psychologists](https://articles.entireweb.com/wp-content/uploads/2024/05/Why-We-Are-Always-Clicking-to-Buy-According-to-Psychologists.pngkeepProtocol.png)
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
![A deeper dive into data, personalization and Copilots A deeper dive into data, personalization and Copilots](https://articles.entireweb.com/wp-content/uploads/2024/05/A-deeper-dive-into-data-personalization-and-Copilots.jpg)
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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