GPT-4 vs. Google Cloud: Performance comparison on 9 SEO tasks
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GPT-4 may not be the ultimate solution for SEO automation when compared to Google Cloud’s APIs for text analysis, content transformation, and various other functions.
OpenAI’s APIs, including the large language model (LLM) GPT-4 and its conversational AI, ChatGPT, have garnered significant attention over the past year. Nevertheless, it’s crucial to recognize that established providers like Google Cloud, Microsoft Azure, and AWS have offered marketing APIs for over a decade.
Many SEO professionals, however, have shown a preference for generative AI models when tackling their SEO-related tasks. In this article, we aim to assess and compare the performance of GPT-4 and Google Cloud’s machine learning APIs across common SEO tasks where automation can be effectively applied. These tasks encompass semantic analysis, classification, translation, and image understanding.
While there exist numerous other potential applications of these technologies in the realm of SEO and digital marketing, our primary objectives in this analysis are as follows:
To encourage innovative thinking regarding the utilization of available machine learning APIs in your daily operations.
To provide insights into the diverse areas where ML-based automation can enhance efficiency and yield better results for digital marketers.
To emphasize the importance of choosing your automation tools judiciously, taking into account the specific task at hand, instead of relying solely on one solution like ChatGPT for all your automation needs.
To assess the performance of these models, I conducted head-to-head comparisons using identical datasets, which included text, video/audio, or images, depending on the specific task at hand.
For a more comprehensive and robust analysis, I replicated this process across three distinct data points for each task. This approach allows for a more robust and informative “Comparative Analysis” section, where we can draw more accurate conclusions based on multiple data points.
Here’s a table summarizing the performance analysis:
Named Entity Recognition (NER) stands as a pivotal domain within the realms of computer science and natural language processing (NLP), placing its primary emphasis on the identification and classification of particular entities found within a given text. These entities can encompass a wide range, including but not limited to, individuals’ names, various organizations, and geographical locations.
The primary goal of entity analysis resides in the efficient extraction of such specifics from unstructured textual data, or alternatively, from text characterized by its free-form nature.
In a parallel vein, unstructured text can also undergo scrutiny through the lens of syntax analysis. This discipline is dedicated to comprehending and dissecting the structural aspects of sentences in a given language, thus facilitating the capacity of machines to derive meaning from the presented text.
The fundamental components of syntax analysis encompass the following:
Parsing Sentence Structure: Syntax analysis entails the deconstruction of a sentence into its constituent elements, including nouns, verbs, adjectives, and more. It seeks to grasp how these elements interconnect to construct grammatically sound sentences.
Dependency Parsing: Dependency parsing delves into the intricate relationships between words within a sentence. It involves identifying primary verbs, subjects, objects, and other dependencies to elucidate how different words rely on one another for meaning.
Parts of Speech Tagging: This facet involves the categorization of each word in a sentence into its respective part of speech, be it a noun, verb, adjective, and so forth. Such categorization is pivotal in discerning the specific role that each word fulfills within the sentence’s structure.
Phrase Chunking: Phrase chunking entails the grouping of words that naturally belong together into cohesive phrases. This process aids in facilitating subsequent syntactic and semantic analyses by identifying word groupings with shared meaning or function.
Syntax Trees: Many syntax analysis tools employ syntax trees, which are graphical representations that depict the hierarchical structure of a sentence. These trees illustrate the relationships and dependencies between various components of the sentence, offering a visual framework for comprehending its syntactic intricacies.
Entity recognition in SEO
In the realm of SEO, the application of entity recognition proves valuable across a variety of projects, including:
Begin by utilizing your keyword universe as a foundation.
Gather extensive SERP data using tools like dataforSEO.
Employ a template, such as the one provided in Sheets, to extract entities from titles, URLs, or meta descriptions of top-ranking pages.
Utilize insights gained from this analysis to shape your topical maps and guide your content strategy.
Initiate your keyword research process and identify keywords that involve entities.
Develop content maps centered around closely related entities.
Compile lists of both positive and negative secondary entities to aid content creators.
Assess keywords featuring entities within the knowledge graph.
Internal Linking Audits:
Commence with an examination of your internal linking anchor text data.
Confirm the presence of entities within anchor text.
Pay special attention to entities linked within the knowledge graph.
Utilize your website content obtained through crawling in conjunction with internal links to identify and connect pages referencing these entities.
Competitor Content Analysis:
Extract entities from your competitors’ website content, encompassing text, titles, and meta descriptions.
Include entities prominently featured in competitor content within your own content maps.
Analyze how frequently articles featuring these entities in titles/meta descriptions correlate with traffic levels.
Social Comments Analysis:
Scrape user-generated comments from platforms like YouTube, TikTok, X, and Facebook.
Analyze the entities present in these comments and align your findings with your site’s content for deeper insights.
As an additional step, assess the sentiment associated with these entities to gain insights into user sentiment, particularly regarding brand-related entities or products/services (e.g., pricing).
By employing entity recognition in these projects, SEO professionals can enhance their understanding of content relevance, user sentiment, and competitor strategies, ultimately optimizing their strategies for improved search engine performance.
Syntax analysis in SEO
In the realm of SEO, text analysis methods prove invaluable when you aim to achieve the following objectives:
Analyze Content at Scale:
Leverage text analysis methods in SEO to conduct comprehensive assessments of content, whether it’s from SERPs or competitor websites. This allows for a thorough understanding of the landscape and informs strategic decisions.
Employ text analysis to unearth n-grams within titles, meta descriptions, content, and anchor text. This reveals valuable insights into the composition and relevance of content, aiding in keyword targeting and optimization efforts.
Determine Successful Title Structures:
Utilize text analysis to identify effective title structures for your target keywords by examining patterns and structures in existing content. This knowledge helps in crafting engaging and click-worthy titles for improved SERP performance.
Enhance Entity Analysis:
Employ text analysis techniques to identify the most prevalent words associated with target entities and attributes. This enriches content relevance and enhances SEO performance by aligning with user search intent and topical relevance.
Facilitate Structured Data Automation:
Recognize repetitive usage of specific terms and term combinations within content. This recognition can trigger automated structured data JSON generation. For example, if a page frequently mentions “FAQ” or “Frequently asked questions,” initiate the extraction of FAQPage structured data. Similarly, if “recipe” is mentioned more than a certain threshold, trigger the completion of Recipe schema markup, streamlining the presentation of your content in search results.
By integrating text analysis methods into your SEO strategy, you can effectively navigate the digital landscape, optimize content, and enhance structured data implementation, ultimately driving improved search engine visibility and user engagement.
Comparative analysis of GPT-4 vs. Google Cloud Natural Language API on entity extraction and text analysis
In the realm of SEO analysis, two fundamental machine learning approaches, clustering and classification, play pivotal roles in efficiently managing and understanding large volumes of textual data. Let’s explore how these approaches are applied in SEO and why they are crucial:
Classification in SEO: Classification is employed when you have predefined categories or labels that you want to assign to your data objects. Here’s how it can be used in SEO:
Content Categorization: If you have a list of categories that your client or team is certain about, you can use a classification machine learning algorithm to automatically categorize web pages (URLs) based on their content. For instance, you can categorize blog posts into topics like “Technology,” “Health,” “Finance,” etc., making it easier to organize and manage your website’s content.
Keyword Intent Analysis: Classification can also be used to determine the intent behind user queries or the focus of specific keywords. For example, classifying keywords into categories like “Informational,” “Transactional,” or “Navigational” helps tailor content strategies to match user intent and improve SEO targeting.
Clustering in SEO: Clustering, on the other hand, is applied when you don’t have predefined categories, and you need to group similar data objects based on inherent similarities in their content. Here’s how clustering can be beneficial in SEO:
Content Topic Discovery: In situations where the content’s topic is unclear, you can employ clustering algorithms to analyze the content, identify key terms or themes, and group similar content pieces together. This helps in topic discovery and content organization. For instance, you might have a large pool of blog posts, and clustering can group them into topics like “Artificial Intelligence,” “Digital Marketing,” “Web Development,” etc.
Duplicate Content Detection: Clustering can also be used to identify duplicate or near-duplicate content across a website. By clustering similar content, SEO professionals can efficiently detect duplicate pages and take necessary actions to consolidate or de-index them to avoid SEO penalties.
User Behavior Analysis: Clustering can be applied to understand user behavior by grouping users with similar browsing patterns or preferences. This information can guide SEO strategies, including personalized content recommendations and user experience improvements.
In summary, classification and clustering are indispensable tools in the SEO toolkit. Classification helps assign predefined labels to data objects, making content management and keyword targeting more efficient. On the other hand, clustering aids in content organization, topic discovery, and user behavior analysis, providing insights that can inform content strategies and improve website optimization efforts. Both approaches enable SEO professionals to make data-driven decisions and enhance their online presence.
Classification in SEO
Explore the following scenarios to grasp the ways in which classification can enhance your SEO strategy efficiency and streamline various processes.
You manage an extensive website and aim to gain rapid insights into its content:
Leverage classification to swiftly identify the diverse range of topics addressed within the website’s content.
You’re engaged in content gap analysis or competitor research, and you need to categorize the content of several competitors:
Employ classification to assess whether the content on competitors’ websites aligns with your desired business direction.
Clustering in SEO
Explore these real-world applications of clustering in SEO and how it can elevate your strategy and productivity:
You’re faced with the challenge of organizing keywords within a vast Keyword Universe:
Utilizing clustering allows you to categorize and gain insights into the diverse topics present in your keyword dataset.
You’re tasked with comprehending the content of a website with limited prior knowledge of its subject matter:
Topic clusters, based on semantic or keyword extraction methods, can assist in identifying key themes and grouping content together based on their similarity in sub-topics.
Comparative analysis of GPT-4 vs. Google Cloud Natural Language API on clustering and classification
The table presented below outlines both the advantages and constraints associated with the models that have been examined.
In the realm of image-related tasks, machine learning algorithms can prove to be valuable, particularly in tasks such as image captioning and image generation.
At present, the capability for image generation is exclusively offered by OpenAI’s DALL-E model, without a comparable alternative in Google Cloud.
Image captioning, as one can imagine, holds significant importance in the realm of SEO for various reasons, including:
Enhancing site accessibility.
Elevating image search rankings and overall site visibility.
Facilitating improved content comprehension of your website by web crawlers.
Comparative analysis of GPT-4V vs. Google Cloud Vision AI on captioning images
In the table below, we compare the image captioning capabilities of Google Cloud’s Vision AI and Vertex AI with the recent addition of GPT-4 with vision capabilities (GPT-4V), accessible through ChatGPT.
One significant factor that may influence your choice between these options is the ease of integrating Google Cloud’s Vertex AI algorithm into a project, allowing you to caption a large volume of images, potentially hundreds or thousands. This can prove exceptionally valuable for enterprises or large websites seeking to meet accessibility requirements. While it’s worth noting that GPT-4’s API endpoint is expected to introduce similar capabilities in the near future, as of now, it has not been implemented, though it may be coming soon.
Content transformation involves converting content into various formats, styles, or lengths. Its significance in SEO can be attributed to two key factors: omnipresence and accessibility.
Omnipresence underscores the necessity for your brand or content to be present across diverse forms and platforms. This need is shaped by:
Consumer preferences for accessing content in different formats.
Google’s evolving search engine results page (SERP) preferences for various content types.
It’s essential to note that Google does not consider the same content in both written (e.g., a blog) and video formats (e.g., a long YouTube video or shorter clips on Shorts/TikTok) as duplicate content. This approach benefits both users (who can choose their preferred format) and search engines (which can provide content in various formats to meet user search intent).
Accessibility entails offering multiple avenues for individuals to engage with your content, thereby broadening your reach to a more inclusive audience.
Consider different platforms (e.g., YouTube, TikTok, Spotify, Instagram, Pinterest, Medium) and content formats (text, images, video, audio).
For example, transforming textual content into an audio format makes it accessible to individuals with visual impairments and those who prefer listening over reading.
Text-to-text transformation in SEO
To enhance your organic reach, consider employing text-to-text content transformation in the following scenarios:
Expanding Your Presence on Social Platforms: If you have a successful blog or resource section but lack visibility on platforms like LinkedIn, X, or Facebook Groups, you can grow your audience and brand reach. Transform your content into the suitable format for each platform to maximize visibility. Consistently share insightful and helpful posts to potentially boost lead generation.
Reviving Email Newsletter Engagement: If your email newsletters contain valuable content but struggle with engagement, repurpose this material into bite-sized, visually captivating social media posts or blog articles. Incorporate interactive elements to attract more readers, encourage sharing, and increase both visibility and subscriber numbers.
Optimizing Extensive Research or Whitepapers: If your in-depth research or whitepapers are not receiving the attention they deserve, break them into a series of smaller, easily digestible articles or blog posts. Each piece can focus on a specific aspect or finding, making the content more accessible and engaging.
Unlocking Gated Content: If you have comprehensive guides or reports in PDF format accessible only through gated content, extract key insights, summaries, or actionable tips from these documents. Repurpose them into blog posts, LinkedIn articles, or X threads to provide a glimpse of the value within the full PDF. Use these teasers to drive traffic back to your gated content.
Text-to-audio transformation in SEO
To enhance your organic reach, consider employing text-to-audio content transformation in the following scenarios:
Expanding to YouTube and Social Media: If you have a highly successful blog or resource section but lack a presence on YouTube, convert your content into audio format. You can then take it a step further by combining the audio with visual elements to create engaging videos for platforms like YouTube, Shorts, and TikTok, catering to short-form content preferences.
Audience Preference for Audio: If your data suggests that your audience prefers audio content over reading, tap into this preference. Many individuals listen to audio content while driving, exercising, working, or multitasking. Increase engagement by transforming your blog posts, articles, or newsletters into podcast episodes or audiobooks, providing diverse content formats to cater to various preferences.
Video or audio transcription in SEO
Utilizing audio-to-text or video-to-text content transformation can be a valuable strategy to enhance your organic reach in the following scenarios:
Transitioning from YouTube to Blogging: If you have a collection of successful videos on YouTube but lack a blog presence, you can swiftly establish your blog by transcribing your audio content. This approach allows you to repurpose your video content into written articles, expanding your content reach to audiences who prefer reading.
Leveraging Webinars or Online Workshops: If you’ve conducted successful webinars or online workshops, consider transcribing these sessions and converting them into a multi-part blog series or an ebook. This strategy prolongs the lifespan of your content and caters to an audience that prefers consuming information through reading, as opposed to watching videos or attending live events.
Maximizing Podcast or Interview Content: If you’re running a podcast or interview series, transcribe these episodes and publish them as text on dedicated landing pages on your website. Ensure proper structured data and include links to the original audio or video resources. You can also augment the content with key insights or additional contributions to provide added value to your audience.
Comparative analysis of GPT-4 vs. Google Cloud on content transformation
The table provided below offers a comparison between three of Google Cloud’s models, depending on the task, and GPT-4’s model. It’s worth noting that the latter doesn’t include built-in text-to-audio or video transcription capabilities; however, these can be integrated using custom agents.
Even with the advancements in machine learning models, it’s crucial to understand that translation alone doesn’t fully replace the need for localization, at least not at this point in time.
When dealing with SEO or user-facing content, it’s advisable to have a content localization specialist as the final editor. Ideally, this specialist can also align the last draft of content with SEO research specific to the target market.
Having said that, automated translation can offer significant benefits, particularly for medium and large websites or those working with budget constraints. It’s a faster, more cost-effective, and scalable solution compared to traditional translation methods.
Content translation in SEO
When might you find it necessary to translate your content to enhance your organic search performance? Consider the following potential scenarios:
Expanding into International Markets: If your business is venturing into international markets, translating existing content before involving a localization expert can yield significant cost savings.
Targeting Audiences in Different Regions: Translating your content into the local language can greatly enhance visibility in local search results. Search engines prioritize content that aligns with the user’s language and location, making this a valuable strategy to attract searches from diverse locales.
Developing Content and Keyword Strategies in Foreign Languages: Although not a common occurrence, this scenario arises in larger organizations or agencies. Here, translation tools become valuable for gaining insights into competitor content, conducting SERP analysis, or researching keywords in languages you may not be proficient in.
Comparative analysis of GPT-4 vs. Google Cloud Translation AI for content translation
The table below provides a concise summary of the findings obtained through Dmitrii Lukianov’s analysis.
Rather than relying solely on a one-size-fits-all approach, it’s essential not to overlook the valuable tools offered by Google Cloud, even when you are heavily focused on GPT-4.
I’ve highlighted various tasks where you can leverage these APIs to enhance your strategy, expedite processes, and boost your organic visibility. Utilizing APIs for specific tasks can also have several advantages:
Accelerating the transformation of data into actionable insights.
Enhancing the quality of your work, particularly when dealing with enterprise-level websites.
Supporting your professional growth and skill development as an SEO consultant.
It’s worth noting that each model comes with its unique strengths and limitations for specific tasks. Therefore, thorough research on the capabilities and ideal use cases of each model you employ is crucial before implementation.