Discover how AI is revolutionizing technical SEO with 11 game-changing strategies. From structured data to predictive analysis, here's how to stay ahead in the digital race.
ERIK BRAUNITZER | TECHNICAL SEO LEAD
October 10, 2024
In the constantly changing world of SEO, leveraging AI has become pivotal for gaining a competitive edge. As we look to 2025, AI continues to enhance technical SEO strategies, making them more efficient and effective. Here are the top 11 AI-driven technical SEO use cases that are shaping the industry.
#1 – Structured Data Markup Generation
AI tools can automate the generation of schema markup, which helps search engines better understand the content on a webpage. By using natural language processing (NLP), AI can generate accurate and comprehensive schema tags, improving the visibility of web pages in search engine results pages (SERPs) and enhancing rich snippets.
#2 – URL Rewrite Deployment
Managing URLs to ensure they are SEO-friendly can be a daunting task. AI solutions can help in a number of ways, from identifying server types to generating rewrite rules based on server type and desired structure (validation and testing, too). This helps maintain URL consistency, boosts keyword relevance, and improves the overall user experience.
#3 – Regex for Data
Regular expressions, or regex, are like a search tool for text. They use special patterns to find and match specific sequences of characters in a string, like a super-powered find-and-replace feature.
Regular expressions (regex) are powerful for tasks such as data filtering (e.g., GSC) and extraction (e.g., crawling tools). However, crafting them can be complex.
Generative AI tools (e.g., Jasper) can automatically generate regex patterns based on user requirements, reducing the complexity and time involved in manually writing these expressions. This is particularly useful for non-technical users.
Source: Jasper
#4 – Custom Coding in Tagging Platforms
Generative AI tools like Jasper can also significantly enhance the setup and management of Google Tag Manager (GTM) by automating the generation of JavaScript or HTML code, creating reusable templates, and performing error checks to ensure code accuracy and efficiency. It also aids in generating documentation and comments within the code, simplifying understanding and maintenance.
Moreover, AI assists in crafting integration scripts for seamless connectivity with third-party tools, streamlining the entire GTM process. By leveraging AI, marketers can save time, minimize errors, and enhance the precision and effectiveness of their data collection efforts.
#5 – N-gram Analysis
N-grams are sequences of "n" items (such as words or letters) from a given text. For example, in the phrase "I love AI," a 2-gram (bigram) would be "I love" and "love AI." They are used in text analysis to understand patterns and context.
A use case for n-grams and n-gram analysis in SEO is to detect duplicate content. By analyzing n-gram patterns, Generative AI can help both internally in identifying content overlaps and externally in ensuring a healthy link profile—one where a website is linked to diverse sources and text.
#6 – Fuzzy Lookups
Fuzzy lookups can be great for detecting duplicate content. Most crawling tools have this feature built in, but further analysis can be done via GenAI. GenAI can also help SEOs expedite 301 redirect mapping. This can be achieved by inputting lists of old URLs and asking AI to find the most likely match in a list of newer URLs.
Lastly, AI-driven fuzzy lookups can also be used to recognize synonyms and variations of keywords. This enhances keyword strategies by identifying additional ranking opportunities and ensuring content aligns with user search intent.
#7 – Regression Analysis to Identify Statistical Significance
Some GenAI tools like ChatGPT allow for easy data inputs (csv files) for statistical analysis. By employing regression analysis against datasets, AI tools can assess things like the impact of SEO content strategies against important ROI metrics such as revenue outcomes (and if and how paid search is affecting SEO and vice versa). Analyzing items like this helps identify financial drivers, enabling more targeted and effective (content) strategies.
#8 – Predictive Analysis
When given the right input or historical analytics data, genAI can analyze page interactions, including page views, time spent on pages, bounce rates, and conversion actions (e.g., purchases and sign-ups). From here, it then identifies patterns and correlations.
Using these patterns, AI develops predictive models to forecast user behavior. It predicts which users are likely to bounce quickly and which are likely to convert based on their initial interactions. It can later recommend UX optimizations (and A/B test) as well.
#9 – Automated Log File Analysis
AI enhances server log file analysis for SEO by identifying crawl patterns; errors like 4xx, 3xx, and 5xx; and optimization opportunities. It can help with user experience by detecting high-exit pages and unusual traffic patterns, etc.
AI can also help manage server resources by distinguishing between legitimate and malicious bots, protecting against security threats. Similarly, it can help quickly identify unusual spikes or drops, allowing SEOs to address issues from algorithm updates or technical disruptions proactively.
#10 – International SEO
International SEO is the practice of optimizing your website to attract and engage audiences from different countries and languages. Its purpose is to tailor your online presence so that search engines recognize your content as relevant to users in various global markets.
This involves using strategies like language or hreflang tags, which are HTML attributes that inform search engines about language and regional targeting of a webpage. It also includes a strategy around country-specific domains and localized (language-specific) content to ensure your business is visible and accessible to international audiences.
AI plays a crucial role in international SEO by automating language detection and ensuring accurate hreflang tag implementation. It assists in assigning correct ISO codes, optimizing content for multiple languages and regions, and enhancing global search visibility with precision and efficiency.
#11 – Modification and Splitting Up of Code for Render-Blocking Scripts
Render-blocking scripts are codes that delay webpage display by stopping the browser from showing content until they're fully loaded. This can slow down how quickly a page appears to users. AI tools can analyze website code to identify and modify render-blocking scripts, improving page load times.
It can do so by deploying optimization tactics, such as example code splitting, which is just breaking up code into small chunks to load pages faster. Equally as important, it can help identify where an asynchronous load could occur, which lets parts of a webpage load independently (rather than synchronously), speeding up the overall loading process or asynchronous loading, where AI helps enhance user experience and SEO performance.
Although it’s far from perfect, AI is the path to revolutionizing technical SEO by automating complex tasks, providing valuable insights, and enhancing overall efficiency. As we approach the end of 2024, embracing these AI-driven use cases will be crucial for maintaining and improving SEO performance in a competitive digital landscape.
Need help executing these strategies? Get in touch with the performance marketing experts at MERGE today.
