MARKETING
SEO Recap: ChatGPT – Moz
The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.
We’re back with another SEO recap with Tom Capper! As you’ve probably noticed, ChatGPT has taken the search world by storm. But does GPT-3 mean the end of SEO as we know it, or are there ways to incorporate the AI model into our daily work?
Tom tries to tackle this question by demonstrating how he plans to use ChatGPT, along with other natural language processing systems, in his own work.
Be sure to check out the commentary on ChatGPT from our other Moz subject matter experts, Dr. Pete Meyers and Miriam Ellis:
Video Transcription
Hello, I’m Tom Capper from Moz, and today I want to talk about how I’m going to use ChatGPT and NLP, natural language processing apps in general in my day-to-day SEO tasks. This has been a big topic recently. I’ve seen a lot of people tweeting about this. Some people saying SEO is dead. This is the beginning of the end. As always, I think that’s maybe a bit too dramatic, but there are some big ways that this can be useful and that this will affect SEOs in their industry I think.
The first question I want to ask is, “Can we use this instead of Google? Are people going to start using NLP-powered assistants instead of search engines in a big way?”
So just being meta here, I asked ChatGPT to write a song about Google’s search results being ruined by an influx of AI content. This is obviously something that Google themselves is really concerned about, right? They talked about it with the helpful content update. Now I think the fact that we can be concerned about AI content ruining search results suggests there might be some problem with an AI-powered search engine, right?
No, AI powered is maybe the wrong term because, obviously, Google themselves are at some degree AI powered, but I mean pure, AI-written results. So for example, I stole this from a tweet and I’ve credited the account below, but if you ask it, “What is the fastest marine mammal,” the fastest marine mammal is the peregrine falcon. That is not a mammal.
Then it mentions the sailfish, which is not a mammal, and marlin, which is not a mammal. This is a particularly bad result. Whereas if I google this, great, that is an example of a fast mammal. We’re at least on the right track. Similarly, if I’m looking for a specific article on a specific web page, I’ve searched Atlantic article about the declining quality of search results, and even though clearly, if you look at the other information that it surfaces, clearly this has consumed some kind of selection of web pages, it’s refusing to acknowledge that here.
Whereas obviously, if I google that, very easy. I can find what I’m looking for straightaway. So yeah, maybe I’m not going to just replace Google with ChatGPT just yet. What about writing copy though? What about I’m fed up of having to manually write blog posts about content that I want to rank for or that I think my audience want to hear about?
So I’m just going to outsource it to a robot. Well, here’s an example. “Write a blog post about the future of NLP in SEO.” Now, at first glance, this looks okay. But actually, when you look a little bit closer, it’s a bluff. It’s vapid. It doesn’t really use any concrete examples.
It doesn’t really read the room. It doesn’t talk about sort of how our industry might be affected more broadly. It just uses some quick tactical examples. It’s not the worst article you could find. I’m sure if you pulled a teenager off the street who knew nothing about this and asked them to write about it, they would probably produce something worse than this.
But on the other hand, if you saw an article on the Moz blog or on another industry credible source, you’d expect something better than this. So yeah, I don’t think that we’re going to be using ChatGPT as our copywriter right away, but there may be some nuance, which I’ll get to in just a bit. What about writing descriptions though?
I thought this was pretty good. “Write a meta description for my Moz blog post about SEO predictions in 2023.” Now I could do a lot better with the query here. I could tell it what my post is going to be about for starters so that it could write a more specific description. But this is already quite good. It’s the right length for a meta description. It covers the bases.
It’s inviting people to click. It makes it sound exciting. This is pretty good. Now you’d obviously want a human to review these for the factual issues we talked about before. But I think a human plus the AI is going to be more effective here than just the human or at least more time efficient. So that’s a potential use case.
What about ideating copy? So I said that the pure ChatGPT written blog post wasn’t great. But one thing I could do is get it to give me a list of subtopics or subheadings that I might want to include in my own post. So here, although it is not the best blog post in the world, it has covered some topics that I might not have thought about.
So I might want to include those in my own post. So instead of asking it “write a blog post about the future of NLP in SEO,” I could say, “Write a bullet point list of ways NLP might affect SEO.” Then I could steal some of those, if I hadn’t thought of them myself, as potential topics that my own ideation had missed. Similarly you could use that as a copywriter’s brief or something like that, again in addition to human participation.
My favorite use case so far though is coding. So personally, I’m not a developer by trade, but often, like many SEOs, I have to interact with SQL, with JavaScript, with Excel, and these kinds of things. That often results in a lot of googling from first principles for someone less experienced in those areas.
Even experienced coders often find themselves falling back to Stack Overflow and this kind of thing. So here’s an example. “Write an SQL query that extracts all the rows from table2 where column A also exists as a row in table1.” So that’s quite complex. I’ve not really made an effort to make that query very easy to understand, but the result is actually pretty good.
It’s a working piece of SQL with an explanation below. This is much quicker than me figuring this out from first principles, and I can take that myself and work it into something good. So again, this is AI plus human rather than just AI or just human being the most effective. I could get a lot of value out of this, and I definitely will. I think in the future, rather than starting by going to Stack Overflow or googling something where I hope to see a Stack Overflow result, I think I would start just by asking here and then work from there.
That’s all. So that’s how I think I’m going to be using ChatGPT in my day-to-day SEO tasks. I’d love to hear what you’ve got planned. Let me know. Thanks.
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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