MARKETING
6 Ways ChatGPT Can Improve Your SEO
Most of the discourse surrounding the impact of artificial intelligence (AI) on SEO has been about content creation. This makes perfect sense. Large language models (LLMs) have fundamentally changed the speed at which businesses and individuals can produce blog posts, marketing copy, social media posts and much more.
I am not the first to provide the caveat that while AI tools can help you speed up your writing process, they can also open up your site to a variety of SEO risks including duplicate content, violations of Google’s E-E-A-T Guidelines, generally robotic copywriting that is devoid of brand voice and personality, and a host of other issues.
AI content generation is certainly something that can help SEOs and businesses in moderation. Google itself has essentially okayed the use of AI, as long as it is with intent to produce “helpful content”. This can be easier said than done.
In this article, I want to highlight some ways that free AI tools like ChatGPT can help SEO’s with all sorts of other tasks, other than creating content. There are a wide range of things that SEOs do everyday that can significantly be sped up or even completely done by free AI tools like ChatGPT. These can range from On-Page SEO optimizations to Technical SEO projects.
Let’s get into it.
1. Create Schema markup
Perhaps the most straightforward way in which tools like ChatGPT can simplify our work as SEOs is by writing schema markup for us. I will keep this section short, as the process in itself is fairly straightforward.
How to Use AI to Create Schema Markup
-
Write a ChatGPT prompt that describes the schema you want to create, and for which page.
-
QA the results and run them through a Schema validating tool.
-
Implement the schema. Submit your URL to Google.
-
That’s it!
Remember, ChatGPT typically will not visit a URL for you, so you will need to paste the entire text of your page in the prompt.
The response is a block of schema code that you can paste into a validator. Note that the response was not 100% perfect, hence the need to QA. ChatGPT missed the name of the publisher organization. Before dropping this code onto the published page in our CMS, I would change the name of the organization from “Example” to “Moz.”
2. Keyword clustering (sample python code)
Another time-saving SEO task that you can jumpstart with ChatGPT is the semantic grouping and categorization of keywords. This can be done within the user interface (UI) of GPT, or through a python script that utilizes OpenAI’s API.
Using the UI, I have had success grouping around 100 keywords at a time. The output will typically be an indented, bulleted list of all your terms categorized into buckets.
A python script gives you more flexibility to increase your number of max tokens and allow you to work with longer lists of keywords.
Below is an extremely simple python script that prompts OpenAI to come up with categories for a list of keywords.
import openai # Set up OpenAI API key and model ID openai.api_key = "YOUR_API_KEY" model_id = "text-davinci-003" # Define the prompt to use with the OpenAI API prompt = """ classify the following keywords into semantically related groups: apple london banana train car pizza sicily pasta """ # Use OpenAI's API to generate text based on the prompt response = openai.Completion.create( engine=model_id, prompt=prompt, max_tokens=1024, n=1, stop=None, temperature=0.7, ) # Extract the generated text from the response generated_text = response.choices[0].text # Print the generated text to the console print(generated_text)
The output will look like something like this. You can use this output to modify groupings in your keyword tracking tool of choice, such as Moz Pro. If you are familiar with using Pandas, you can turn the generated_text output into a dataframe for an easy CSV export.
Fruits: Apple, Banana Cities: London, Sicily Transportation: Train, Car Food: Pizza, Pasta
3. Generate meta descriptions
ChatGPT is exceedingly good at taking large amounts of text input and summarizing it. What better way for SEOs to utilize AI’s summarization capabilities than generating meta descriptions? Since meta descriptions are inherently summaries of pages, natural language processing (NLP) models do a good job of extracting the main ideas from multiple paragraphs of text and condensing them into one.
When feeding ChatGPT with text to summarize, you can also include a few keywords that you want it to include in its output. This is another instance where you will need outside data from a tool such as Moz Keyword Explorer to help you find focus keywords. Once you have an idea of the main keyword(s) of the page you want to optimize, you can include those in your meta description prompt. That prompt may look something like this:
“
Summarize the following text in 60 words, and include the following keywords: seo, content strategy [full page text]
”
In my experience, however, ChatGPT is not very good at limiting its responses to a certain word or character length. You may get something like this, and need to change or remove a few sentences.
Still, this simple task could potentially have saved you 10–15 minutes of working with a blank page (or CMS field) and given you a starting point for your meta description.
4. Create FAQs (and tag them with schema)
Another task that leverages ChatGPT’s summarization capabilities is the creation of frequently asked questions (FAQs).
Prompt GPT to create FAQs for a section of page copy that you paste into the tool, and AI will generate some sample FAQs for you. The responses it gives tends to be brief, which is ideal for tagging them with FAQ schema.
After you’ve reviewed and edited the FAQ suggestions that ChatGPT provides, circle back to tip #1 and paste them back into ChatGPT to generate FAQ schema that you can add to your page.
5. Topical research
While OpenAI’s free ChatGPT tool does not provide Keyword Volume or other important SEO keyword metrics, it can still be an effective engine for generating content ideas related to a given keyword.
When paired with a tool like Moz Keyword Explorer, the results can be powerful.
Begin the process as you would normally approach keyword research. Identify a list of keywords that you want to include in your page. Then, ask ChatGPT to create topic ideas related to these terms.
I find that prompting the tool for around 50 topics gives you a good sample of page ideas without repetition.
The results are not all going to be perfect titles for you to copy and paste into your CMS without reviewing them, but they can rapidly (and I mean RAPIDLY) give you a sense of direction for your editorial calendar, content marketing strategy or even social media posts. Each of the concepts identified here about SEO, focusing on the specified keywords, has the makings of a well-intentioned blog post topic.
6. SEO content briefs
Once you have done your keyword research and compiled terms that you would like to include into a new page on your website, try asking ChatGPT to use them to create a page outline for you, along with a possible page title.
This can serve as a great jumping-off-point for your editorial team (or you) to work with to write your full article. An outline or content brief for a page about keyword research may look something like this:
As is a recurring theme with the use of AI for SEO, the results are not perfect, but they can generate ideas for you to take and run with. For example, you may realize that this outline does not get into the concepts of Search Volume or Keyword Difficulty, which you wanted to address on your page. You can tweak your prompt to specify a few additional keywords that you’d like to include, or manually edit ChatGPT’s output to suit your needs.
My guess is as good as any regarding the direction AI will steer the digital marketing industry, and more specifically SEO. What I do know is that right now, there are so many ways in which AI can make tedious aspects of my job less time consuming, so I can focus my attention on more strategic and big-picture problems. Hopefully this list helps you do the same.
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.”
-
SEARCHENGINES7 days ago
Daily Search Forum Recap: September 27, 2024
-
SEO6 days ago
How to Estimate It and Source Data
-
SEO5 days ago
6 Things You Can Do to Compete With Big Sites
-
SEO7 days ago
9 Successful PR Campaign Examples, According to the Data
-
SEO5 days ago
Yoast Co-Founder Suggests A WordPress Contributor Board
-
SEARCHENGINES6 days ago
Google’s 26th Birthday Doodle Is Missing
-
SEARCHENGINES4 days ago
Daily Search Forum Recap: September 30, 2024
-
WORDPRESS1 day ago
WordPress biz Automattic details WP Engine deal demands • The Register