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Why SEOs Need to Embrace AI

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Why SEOs Need to Embrace AI

The author’s views are entirely their own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

It’s no question that the AI conversation has dominated the SEO community during the last year. The implications of this new technology are both extremely exciting and a little scary at the same time. At Go Fish Digital, we’ve been following these trends closely and refining our processes around the possibilities that AI brings.

Within both the SEO and larger technology communities, there is a huge discrepancy of opinions.

Many are weary of the implications and skeptical on the long-term benefits for marketers.

Some believe that this is a passing trend similar to voice search.

Others believe that this is a revolutionary technology that will impact every aspect of search in the future.

Out of curiosity, I performed a poll on my Linkedin page. I asked if SEOs thought that ChatGPT was going to disrupt SEO:

Nearly two-thirds of respondents said that ChatGPT is going to change our industry. I tend to agree with them. As a community, we need to be getting prepared for the imminent changes that AI is going to bring.

SEOs need to embrace AI

I believe that as a community, we need to be paying attention to this generational technology. While the tools certainly have their shortcomings, the outputs they’re producing already are nothing short of impressive. These tools will allow us to become more educated, more efficient and more technical.

It’s important that we not only keep in mind where these technologies are today. We must understand and expect that these tools will get exponentially better over time. The performance of GPT-4 is already significantly improved from GPT-3.5

Thinking about a 5-year time horizon, these tools will advance far beyond what we’re seeing in today’s versions. This is why SEOs need to be adopting these technologies right now. The ones that do, will be well-positioned for the future of marketing.

Improving our SEO efficiencies

Back in March, I was curious as to how many SEOs were utilizing ChatGPT in their day-to-day workflows. Despite the fact that it was relatively new, I wondered how quick SEOs were to adopt using it:

A poll on LinkedIn on how marketers integrate ChatGPT in their day-to-day work

To my surprise, 52% of respondents already claimed to be using ChatGPT to help with regular SEO tasks. This poll was conducted just 3 months after it’s initial release.

This makes sense as there are a lot of really great use cases for SEO tasks that we do on a daily basis. By using AI technology like ChatGPT, you can significantly improve the efficiency at which you’re able to work on some of these tasks.

A simple example is keyword research. With ChatGPT, you can immediately create large seed lists of potential keywords that have semantic relationships to the core topics that your website is trying to compete for.

Tom Demers recently wrote a great guide on Search Engine Land where he walks through his process of using AI for keyword research. In the guide he shows multiple examples of how he was able to use different types of prompts to directly identify keywords or find sources to mine for query opportunities.

He even showcased how he was able to export data from third-party SEO tools and bring it into a table format within the ChatGPT interface:

ChatGPT integration with third-party SEO tools

Content ideation is another great example of a tactical task that ChatGPT can leverage. Here I prompted ChatGPT to give me 30 different topic ideas about “The Metaverse”. It delivered them in about 30 seconds:

Prompting ChatGPT to give topic ideas for content.

If I ran a technology blog, I could vet that against existing content on the site and find gaps where search opportunities might exist. Even if there was no direct SEO value, these topics still help position us as a topical authority in a particular content area.

You could even use ChatGPT to optimize your site’s content at scale. Tools such as GPT For Work allow you to connect to Google Sheets to the ChatGPT API. This allows you to feed in dynamic prompts and get the output back in Google Sheets.

As a result, you could create thousands of title tags and meta descriptions. You could give a site a baseline level of optimization with about 30 minutes of setup:

Using Google Sheets with the ChatGPT API to create title tags

From a tactical perspective, there are so many use cases for ChatGPT to help with SEO.

  1. Keyword research

  2. Content ideation

  3. Content evaluation

  4. Schema generation

  5. Featured snippet creation

  6. Title tags and meta descriptions

  7. Ideas for new content sections

  8. Readability improvements

While there are many resources available, Alyeda Solis wrote a fantastic guide on the different use cases for SEO.

If you’re performing SEO in any capacity, it’s very likely that you can find a use case where your day-to-day efficiencies can be improved by utilizing some of these processes. This will allow us to produce a more efficient output and spend time working on initiatives that are less prone to automation.

Enhancing our knowledge base

I believe that only looking at strictly tactical implementations would be using AI far within its limits. There are many other great applications for the SEO community beyond that.

One of the best use cases that we see many industries using ChatGPT for is to enhance their knowledge base. AI can be an excellent teacher when prompted correctly. It can summarize information exceptionally quickly and give it to us in an output that’s completely customized to our learning style.

For example, the late-great Bill Slawski used to analyze patents that Google filed for. These patents are more technical and Bill used a long-form writing style.

Bill Slawski's patents

We started testing running Bill’s patents through ChatGPT and prompted it to summarize core points. A successful prompt was “Summarize the whole article in 5 bullet points. Explain like I’m in high school”:

Asking ChatGPT to summarize an article

For my learning style, this allowed me to get enough detail to understand the patent and its implications without having the output oversimplify Bill’s ideas. If I was curious about any given idea, I could simply prompt ChatGPT to elaborate more and it would allow me to go deeper.

You could also get summaries from Google’s documentation. Here I fed it text from Google’s page on canonical tags and asked it to give me best practices.

Asking ChatGPT to summarize a page

How many of us struggle with technical SEO, web technology or understanding how search engines work?

With ChatGPT the work of great technical minds like Bill and Google’s documentation essentially becomes democratized. Now when you encounter an SEO topic that you don’t understand, you can use AI as a teacher.

Of course, there are drawbacks to this. These types of summaries might not fully represent an author’s work as content must be left out and elements such as tone of voice aren’t taken in to consideration.

However, as a whole, this is a very powerful thing. Now the knowledge base that exists around SEO is more accessible to the entire community.

Empowering a community of creators

Personally, I think the most exciting aspect of the implications of AI for the SEO community are the technical possibilities that it opens up. While many of us are technically minded, not everyone has a background in development.

ChatGPT is going to enable the SEO community to become creators.

With the right prompting, you’ll now be able to create code that you weren’t able to before. That’s going to significantly impact your effectiveness as a search marketer.

For example, Screaming Frog is now opened up so much more for SEOs. I recently needed to scrape the BreadcrumbList structured data of REI’s site. When doing similar tasks before, it’s taken hours of debugging, re-running crawls and even meetings with other members of our team.

I asked ChatGPT to create a Screaming Frog extract and fed it sample HTML. Within 5 minutes, I was able to get a working XPath that allowed me to extract exactly what I needed:

Asking ChatGPT to create a Screaming Frog extract

The process could be applied to many other tools. ChatGPT could help you create API calls, SQL queries, Python scripts and many other things. This will empower the community to create new things that might not have been possible for many people.

On top of one-off pieces of code, you’ll now be able to create tools that are fully customized to your exact needs.

I’ve never created a Chrome extension before. However, ChatGPT has the power to take the prompts you give it and turn it into a fully functioning extension.

With about 30 minutes of prompting and debugging, it was able to create a custom SEO extension that pulls data such as title tags, meta descriptions, H1s, URL and more:

Creating a custom SEO extension using ChatGPT

While there are great tools like this available, I could customize this extension to the exact specifications that I want.

You can even create tools that help improve your SEO efficiencies. My colleague Dan Hinckley was able to further iterate on this extension.

By connecting it to the ChatGPT API, he was able to create an SEO extension for our team that provides recommendations for title tags, H1, new content sections, and more:

1688405166 78 Why SEOs Need to Embrace AI

Now this gives the entire team at Go Fish Digital a new tool to use as part of their process. We can quickly find page-level SEO opportunities and can decide which ones are worth actioning on for a given recommendation.

I suspect that ChatGPT will produce other solutions similar to this in the community. By embracing the power of AI, SEO teams will be able to identify the needs that they have and create a solution that perfectly fits their internal processes.

Conclusion

To us, it’s clear that AI is going to have a significant impact on the SEO community. The data already shows that SEOs see these technologies as having the power to significantly disrupt the industry and are already incorporating tools like ChatGPT into their day-to-day processes. I believe the SEOs that adapt to these changes will be the ones that see the most success.

Marketers that are able to leverage AI to improve efficiencies, grow their knowledge base and build customized solutions to improve their processes will be well-positioned for whatever the future of search holds.

Chris will be speaking at MozCon 2023 this August in Seattle! Join us for inspiring sessions with our incredible lineup of speakers. 

We hope you’re as excited as we are for August 7th and 8th to hurry up and get here. And again, if you haven’t grabbed your ticket yet and need help making a case we have a handy template to convince your boss!

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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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