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OpenAI’s APIs, especially the large language model (LLM) GPT-4 and its chatbot ChatGPT, have gained significant popularity in the past year.
Despite the availability of marketing APIs from long-established providers like Google Cloud, Microsoft Azure, and AWS for over a decade, many search marketers prefer generative AI models for their SEO-related tasks.
This article compares the performance of GPT-4 and Google Cloud’s machine learning APIs on common SEO tasks where automation can be implemented, including semantic analysis, classification, translation, and image understanding.
While there are undoubtedly many other applications of these technologies for SEO and digital marketing, my aim with this analysis is to:
To test the models’ performance, I’ve put them against one another on the same data (i.e., same text, video/audio, or image, depending on the task).
I’ve repeated the process on three different data points for each task to allow me to draw better conclusions for the “Comparative Analysis” below.
Here’s a table quickly summarizing the performance analysis:
Legend:
✔ – Can do it, with limitations
❌ – Can’t do it
👑 – Excels at this task
Let’s dive into each category below.
Named entity recognition (NER) is an area in computer science and natural language processing (NLP) that focuses on detecting and categorizing specific entities within a text, like people’s names, organizations and places.
The main objective of entity analysis is to efficiently extract these details from unstructured text data (or otherwise – from free-form text).
You can also analyze unstructured text through syntax analysis. This area deals with understanding and analyzing the structure of sentences in a language, assisting machines to extract meaning from text.
The key aspects of syntax analysis include:
Parsing sentence structure
Dependency parsing
Parts of speech tagging
Phrase chunking
Syntax trees
So, what type of projects can these two approaches be used in SEO for?
In SEO, you can use entity recognition in the following projects:
SERP analysis
Keyword research
Internal linking audits
Competitor content analysis
Social comments analysis
In SEO, text analysis methods can be used if you want to:
Analyze content at scale
Identify n-grams
Determine successful title structures
Enhance entity analysis
Facilitate structured data automation
In the following section, I will review two machine learning approaches for grouping text – clustering and classification. Both are extremely important for SEO analysis and executing different tasks at scale.
It’s best to illustrate the difference between the two with a simple example from the SEO industry. Imagine working with a list of pages (a website’s URLs) that you want to label with appropriate category tags based on the pages’ content. In this case:
Here are some examples of how these approaches can be applied in SEO and why they are important.
Review the following scenarios and how classification can help improve your SEO strategy completion or speed up some processes.
You have a large website that you want to quickly understand the content of
You are conducting a content gap analysis or competitor research and trying to categorize multiple competitors’ content
Consider these practical applications of clustering in SEO and how it can enhance your strategy and efficiency:
You are trying to group together keywords in a big Keyword Universe
You are trying to understand a website’s content without having any idea what the content is about
The table below shows the benefits and limitations of using either of the models analyzed.
There are a few tasks in the image category where machine learning algorithms can assist you, like captioning images and image generation.
Currently, image generation can be done only by OpenAI’s DALL-E model; Google Cloud does not yet have a comparable alternative.
Image captioning, needless to say, is highly important for SEO for many reasons, including:
The table below compares the captioning capabilities of Google Cloud’s Vision AI and Vertex AI versus the recent introduction of GPT-4 with vision (or GPT-4V) vision capabilities, accessed via ChatGPT.
The only notable difference that can sway you to use one versus the other would be that you can easily integrate Google Cloud’s Vertex AI algorithm into a project and run it to caption hundreds, if not thousands, of images.
This can be extremely useful for image captioning for enterprise-level or large websites failing at this accessibility requirement. Despite the many conversions that this capability will soon be introduced with the GPT-4 API endpoint, this has not (yet) come to fruition, though it might soon.
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Content transformation refers to transforming the content to a different format, style, or length. It is important for SEO because of two factors: omnipresence and accessibility.
Omnipresence encapsulates the need for your brand or content to exist in different forms and platforms. This requirement is influenced by:
It’s important to know that Google won’t automatically treat it as duplicate content if you have the same content in both written (like a blog) and video formats (such as a long YouTube video or in shorter clips on Shorts/TikTok).
In short, the same content in different formats is not considered duplicate, as it benefits both the user (able to consume the content in whatever format they choose) and search engines (able to serve the content in different formats and satisfy the user’s search intent regardless of their preference).
Accessibility means offering diverse ways for people to consume your content, helping you reach a broader and more inclusive audience.
Think of different platforms (YouTube, TikTok, Spotify, Instagram, Pinterest, Medium) and content formats (text, image, video, audio).
Transforming your textual content into audio format, for instance, makes it accessible to people with visual impairments or those who prefer listening over reading.
You can use text-to-text content transformation to improve your organic reach in the following scenarios:
You have a high-performing blog or resource section but no presence on platforms like LinkedIn, X or Facebook Groups
Your email newsletters have substantial content but limited engagement
Your in-depth research or whitepapers are underutilized
You have comprehensive guides or reports in PDF format that are only accessible through gated content
You can use text-to-audio content transformation to improve your organic reach in cases where:
You have a high-performing blog or resource section but no presence on YouTube
You have data to support the hypothesis that your audience prefers audio content over reading
Here is how you can use audio-to-text or video-to-text content transformation to improve your organic reach:
You have a library of high-performing videos on YouTube but no blog
You have a series of successful webinars or online workshops
You have a running podcast or interview series
The table below compares three of Google Cloud’s models (depending on the task) versus GPT-4’s model. (You will notice that the latter does not have text-to-audio or video transcription out-of-the-box. However, this can be incorporated with custom agents.)
Remember that regardless of the development of machine learning models, translation does not replace localization, at least not yet.
This means that when it comes to SEO or user-facing content, you should use a content localization specialist as your last edit, ideally, also someone who can align the last draft of content with SEO research for that target market.
That said, automated translation can make a difference for medium and large websites or those working on a bit more constrained budget, as it is much quicker, cheaper, and scalable to implement than traditional translation.
When would you require content translation to improve your organic search performance? Consider the following as some potential examples:
You are expanding your business research into international markets
You are trying to attract searches from other locales
You are building a content and keyword strategy for a website in another language
The following table summarizes the insights from the analysis conducted by Dmitrii Lukianov.
Don’t just use a blanket model for all your tasks. You might be missing out on useful tools from Google Cloud because you’re focusing a lot on GPT-4.
I’ve pointed out the various tasks where you can use these APIs and how they can help your strategy, make tasks faster, and improve your organic visibility. Using APIs for specific tasks can also:
As noted, each model will have benefits and limitations for specific tasks, so it’s important to research what each model you use has been trained for and its ideal use before you start.
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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