Finding Untapped SEO Content Opportunities Using AI and Regex

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Identifying untapped SEO content opportunities is crucial for driving organic traffic and improving search visibility. In our process, we utilized AI and Google Search Console (GSC) Regex to uncover informational queries that users are searching for but are not fully optimized within our content strategy. By filtering search data using a regex pattern, we extracted high-potential keywords that indicate user intent, such as “how,” “what,” “why,” and “best ways.” This approach allowed us to pinpoint gaps in our existing content and discover new content ideas with high search demand.

Finding Untapped SEO Content Opportunities Using AI and GSC Regex

Using AI, we generated an advanced regex pattern to refine our keyword extraction process, making it easier to identify queries that align with user needs. By applying this pattern in GSC, we filtered out valuable informational keywords, helping us create a data-driven content strategy. This process not only enhances content relevance but also improves rankings and engagement by targeting search intent effectively.

Using AI to Generate a Regex Pattern for Filtering Informational Keywords

Regex (Regular Expressions) is a powerful tool that enables SEO professionals to filter and analyze large sets of keyword data efficiently. In our process of discovering untapped SEO content opportunities, we leveraged regex to extract informational queries from Google Search Console (GSC). These queries typically indicate that users are seeking answers, explanations, or step-by-step guides. By focusing on informational keywords, we can enhance our content strategy by addressing user intent more effectively, leading to higher rankings and better engagement.

Why We Used AI to Create a Regex Pattern

Crafting an effective regex pattern manually can be complex, especially when trying to capture various keyword variations. To streamline this process, we utilized AI to generate a precise regex pattern that identifies informational queries. AI tools, like ChatGPT, can quickly process language patterns and create optimized regex sequences that would otherwise require extensive manual effort. This automation ensures accuracy and efficiency, allowing us to focus on strategic content planning rather than spending hours fine-tuning patterns.

Generating the Regex Pattern with AI

To create an effective regex pattern, we first identified common informational keyword triggers, such as:

How

What

Why

When

Where

Can

Does

Which

Who

Guide

Tutorial

Tips

Advice

Definition

Meaning

Example

List

Steps

Best time

History

Facts

Overview

Benefits

Explanation

These keywords indicate that users are looking for information rather than transactional or navigational content. With this list in mind, we prompted AI to generate a regex pattern that captures these terms accurately.

AI-Generated Regex Pattern

We used the following prompt in ChatGPT to generate the regex pattern:

“Create a regex for Google Search Console that finds informational keywords such as: [list of keywords]. Ensure it captures whole words and phrases correctly.”

The AI returned the following regex pattern:

This pattern ensures that the search queries are matched as whole words, preventing partial matches that could dilute the accuracy of our analysis.

Explanation of the Regex Pattern

Understanding how this regex works is essential for modifying and adapting it for future use:

\b ensures that only full words or phrases are matched, avoiding unintended matches (e.g., preventing “wholesale” from being detected when searching for “who”).

The | operator acts as an OR function, allowing multiple keywords to be matched within a single expression.

“ways to” and “best time” are included as phrases to capture specific user intents related to guides and optimal timing.

Applying the Regex Pattern in Google Search Console

After generating the regex pattern, we applied it within Google Search Console to filter queries that matched our informational criteria. The process involved:

Opening the Performance Report in Google Search Console.

Clicking on the New filter and selecting Query.

Choosing Custom (regex).

Pasting the AI-generated regex pattern.

Applying the filter to analyze the results.

This allowed us to extract valuable informational queries, helping us identify gaps in our content and prioritize topics that align with user intent.

Benefits of Using AI for Regex Generation

Using AI to generate regex patterns offers several advantages:

Time Efficiency: AI eliminates the need for manual regex creation, significantly reducing the time spent on keyword analysis.

Accuracy: AI ensures that all relevant keywords are included in the pattern while preventing unnecessary matches.

Scalability: The regex pattern can be easily adapted for different SEO tasks, such as filtering transactional or navigational queries.

Data-Driven Content Strategy: By identifying informational keywords, we can craft content that directly addresses user questions, improving engagement and search visibility.

Refining the Regex Pattern for Future Use

While the AI-generated regex pattern is highly effective, continuous refinement is necessary to keep up with evolving search trends. Some ways to enhance the pattern include:

Adding new informational keywords based on emerging search behavior.

Adjusting the pattern to exclude ambiguous terms that may lead to irrelevant matches.

Experimenting with different regex structures to optimize results.

By periodically updating our regex pattern, we ensure that our content strategy remains aligned with user intent and search engine algorithms.

Applying the Regex in Google Search Console to Identify Informational Queries

Informational queries play a crucial role in SEO as they target users in the awareness stage of their journey. Unlike transactional or navigational queries, informational queries signal that users are seeking knowledge rather than making immediate purchases. By identifying and optimizing for these queries, we can attract organic traffic, establish authority, and nurture potential customers until they are ready to convert.

To achieve this, we leveraged Google Search Console (GSC) along with AI-generated regex patterns to filter and analyze informational search queries effectively. This systematic approach allows us to uncover hidden opportunities, optimize existing content, and create new high-value pieces that align with search intent.

Step-by-Step Process of Applying the Regex in GSC

Once we had our AI-generated regex pattern designed to filter informational queries, the next step was implementing it in Google Search Console. This process involved accessing the Performance Report in GSC, applying the regex filter, and analyzing the extracted keywords for content opportunities.

1. Accessing Google Search Console Performance Report

The first step was to log into our Google Search Console account and navigate to the Performance Report. This report provides valuable insights into search queries that drive traffic to our website, including impressions, clicks, CTR, and position data.

We selected the Search Results tab under the Performance Report section.

By default, this report displays data for all queries, pages, and devices, but we focused specifically on search queries.

2. Applying a New Query Filter with Regex

To refine the data and extract only informational queries, we applied a custom regex filter:

Clicked on the “New” filter option.

Selected “Query” from the drop-down menu.

Chose the Custom (regex) option.

Pasted our AI-generated regex pattern:

Clicked Apply to filter the queries based on our regex pattern.

This step ensured that only queries containing informational keywords appeared in the performance report.

3. Analyzing the Extracted Informational Queries

After applying the regex filter, we observed a list of search queries that matched our informational criteria. These queries provided insights into the type of content users were searching for, helping us identify content gaps and optimization opportunities.

Key aspects we analyzed:

High-Impression Queries with Low Click-Through Rate (CTR): These indicate that content may not be well-optimized for user intent, requiring adjustments to meta titles, descriptions, or content structure.

High-CTR Queries with Lower Impressions: Such queries present an opportunity to enhance rankings through on-page optimizations and internal linking.

New or Emerging Queries: Identifying fresh content ideas based on user interests.

4. Prioritizing Keywords for Content Optimization

Once we had a list of informational queries, the next step was to prioritize them based on their potential impact. We considered:

Search Volume & CTR: Higher impression queries were prioritized for content expansion.

Keyword Difficulty & Competition: Easier-to-rank queries were selected for quick wins.

Alignment with Business Goals: Queries relevant to our industry and services were given preference.

We categorized keywords into:

Existing Content Optimization: Queries that matched pages already on our site but needed improvements.

New Content Opportunities: Completely untapped keywords for which we could create fresh content.

5. Mapping Queries to Content Strategy

After filtering and prioritizing queries, we mapped them into actionable content strategies:

A. Optimizing Existing Pages

For pages already ranking for informational queries but underperforming, we implemented:

Updating Content: Adding more comprehensive answers, step-by-step guides, and FAQs.

Enhancing Meta Tags: Making titles and descriptions more engaging and keyword-rich.

Improving UX & Readability: Breaking down content with bullet points, images, and subheadings.

Internal Linking: Strengthening connections between related articles to boost topic authority.

B. Creating New Content

For completely new queries, we developed:

How-To Guides: Step-by-step tutorials answering common questions.

Explainer Articles: In-depth breakdowns of industry terms and concepts.

Comparison & Listicles: Providing detailed comparisons, lists, and pros/cons analyses.

Case Studies & Examples: Real-world applications and success stories.

6. Measuring Success and Iterating

Applying regex in GSC is an iterative process. After implementing changes, we monitored performance to measure the impact.

Key Metrics to Track:

Impressions & Clicks: To see if content visibility improved.

CTR & Dwell Time: Indicating if content revisions enhanced engagement.

Search Rankings: Checking if keyword positions improved.

Conversion Metrics: Tracking lead generation and user actions from informational content.

We continuously refined our strategy based on these insights, ensuring sustained growth in organic traffic and authority building.

Analyzing the Extracted Data for SEO Insights

After applying the regex filter in Google Search Console (GSC) and gathering informational queries, the next crucial step is analyzing this data to extract meaningful SEO insights. This phase helps identify content gaps, optimize existing pages, and develop a data-driven content strategy that aligns with search intent.

Categorizing the Queries Based on Intent

Once we have a list of informational queries, the first step is to categorize them into relevant intent-based clusters. This segmentation allows us to group similar queries together and analyze patterns. For instance:

Basic informational queries: Questions that indicate users are in the awareness stage (e.g., “What is technical SEO?” or “Why is backlinking important?”).

How-to guides and tutorials: Users looking for step-by-step instructions (e.g., “How to optimize meta descriptions” or “Steps to improve website speed”).

Comparisons and recommendations: Queries that suggest decision-making (e.g., “Best SEO tools for beginners” or “SEO vs. PPC – which is better?”).

Historical and factual searches: Users looking for background knowledge (e.g., “History of Google algorithm updates” or “SEO trends 2025”).

By classifying these queries, we can tailor our content strategy to better address each user’s needs.

Identifying Content Gaps and Opportunities

Analyzing extracted queries helps uncover content gaps—topics that users are searching for but are either missing or underdeveloped on our website. We can cross-check our existing content to see whether these queries are already addressed or require new articles. Key strategies include:

Expanding thin content: If a page ranks for an informational query but lacks depth, we can enhance it with additional details, examples, and visuals.

Creating new content: If we identify high-volume queries that we haven’t covered yet, they become prime candidates for blog posts, FAQs, or pillar pages.

Optimizing for long-tail keywords: Many informational queries are long-tail in nature. Targeting these can help attract highly relevant traffic with less competition.

Evaluating Performance Metrics for Content Optimization

Once we have the queries, we analyze their performance in GSC using metrics like impressions, clicks, click-through rate (CTR), and average position. This data provides insights into how well our content is resonating with users.

Low CTR, high impressions: If a page appears frequently in search results but isn’t getting clicks, we may need to improve meta titles and descriptions to make them more compelling.

High clicks, low conversions: If users land on our content but don’t take the desired action (e.g., signing up for a newsletter or exploring related pages), we need to refine internal linking and calls to action.

Low impressions: If a relevant page isn’t appearing in search results, we may need to strengthen its on-page SEO, build backlinks, or enhance topical authority.

Leveraging Insights for Future SEO Strategies

The insights gained from this analysis help refine our broader SEO strategy. By continuously monitoring search queries and adapting content accordingly, we ensure that our website remains aligned with evolving user intent and search trends. Regularly updating content, targeting emerging queries, and optimizing based on performance data create a sustainable approach to driving organic traffic and improving search visibility.

Creating a Data-Driven Content Strategy from Extracted Keywords

Once we have successfully extracted informational queries using AI-generated regex in Google Search Console (GSC), the next crucial step is to build a content strategy based on this data. A data-driven approach ensures that we create content that aligns with user search intent, increases organic traffic, and enhances search engine rankings. Here’s how we transform these insights into an actionable SEO content strategy.

Categorizing the Extracted Keywords by Search Intent

Not all informational queries serve the same purpose. Some users seek definitions, others need tutorials, while some look for comparisons or in-depth explanations. We categorize extracted keywords into different intent-driven groups such as:

Definition-Based Queries: Keywords like “What is…” or “Meaning of…” suggest users need a straightforward definition.

How-To Queries: Keywords containing “How to…” or “Steps to…” indicate a need for tutorials or step-by-step guides.

Comparative Queries: Searches that include “Which is better,” “vs.,” or “Best” are focused on comparisons.

Fact-Based Queries: Keywords like “History of…” or “Facts about…” signal a demand for detailed explanations.

By segmenting queries, we ensure that each keyword gets content tailored to its specific user intent.

Prioritizing High-Value Keywords

The next step involves filtering the extracted keywords based on their potential value. Factors to consider include:

Search Volume: Using tools like Google Keyword Planner, Ahrefs, or SEMrush, we determine the monthly search volume of each query.

Competition Level: Low-competition keywords provide quick wins, while high-competition ones require more comprehensive content strategies.

Relevance to Business Goals: We prioritize keywords that align with our niche and offerings to drive qualified traffic.

This prioritization ensures that our content efforts focus on high-impact opportunities rather than spreading resources thinly.

Mapping Keywords to Content Types

After identifying high-value keywords, we map them to appropriate content formats. For example:

Blog Posts: Ideal for in-depth guides, tutorials, and comparisons.

FAQs & Knowledge Base: Best for quick answers to common questions.

Infographics & Visual Content: Effective for historical data, facts, and list-based queries.

Video Content: Useful for “how-to” guides and tutorial-based searches.

By aligning keywords with the right content format, we improve engagement and user satisfaction.

Developing a Content Calendar

Consistency is key in SEO. Based on our keyword mapping, we create a content calendar to schedule blog posts, video releases, and knowledge base updates. The calendar includes:

Publication Dates to maintain regular posting frequency.

Target Keywords to ensure each post serves an SEO purpose.

Content Format & Outline to streamline the creation process.

A well-structured content calendar keeps the strategy organized and execution smooth.

Monitoring Performance and Refining Strategy

Once content is published, we continuously monitor its performance using GSC, Google Analytics, and SEO tools. Metrics such as click-through rates (CTR), bounce rates, and keyword rankings help refine our approach. If certain keywords underperform, we optimize content with better formatting, internal linking, or additional insights.

Tracking and Iterating Based on Performance Data

The Importance of Continuous Optimization

Once we have implemented our content strategy based on the extracted informational keywords, the process doesn’t stop there. SEO is a continuous cycle of monitoring, refining, and optimizing content for better search visibility and user engagement. By tracking performance data, we can determine what’s working, what needs improvement, and where new opportunities lie. Without a systematic approach to iteration, even the best content strategies can become outdated and ineffective over time.

Utilizing Google Search Console for Performance Tracking

Google Search Console (GSC) remains a crucial tool in tracking the effectiveness of our SEO strategy. After applying our regex filter and publishing content optimized for the discovered queries, we must revisit GSC to analyze key performance indicators (KPIs). Important metrics to track include:

Impressions: To gauge how often our content is appearing in search results.

Clicks: To measure how many users are engaging with our pages.

Click-Through Rate (CTR): A key indicator of whether our titles and meta descriptions are compelling enough.

Average Position: Helps assess our content’s ranking and potential areas for improvement.

By regularly checking these metrics, we gain insights into whether our optimizations are having the desired impact or if further refinements are needed.

Identifying Underperforming Content

One of the key benefits of tracking SEO performance data is the ability to identify underperforming content. If certain pages are receiving low impressions or clicks, it may indicate:

A need for improved title tags and meta descriptions to boost CTR.

A misalignment with search intent, requiring content adjustments.

Poor keyword targeting, necessitating better optimization for relevant queries.

By pinpointing these issues early, we can take targeted actions to improve visibility and engagement.

Enhancing Content Based on Data Insights

Once we’ve identified which pages need improvement, the next step is making strategic updates. Some key enhancements include:

Optimizing Title Tags & Meta Descriptions: If CTR is low, testing different headlines and descriptions can make content more enticing.

Adding Internal Links: Strengthening internal linking can improve user engagement and signal importance to search engines.

Expanding or Refining Content: If the average position is low, adding more detailed information, examples, or updated insights can boost relevance.

Enhancing Readability & UX: Improving formatting, readability, and multimedia elements can reduce bounce rates and improve time on page.

These refinements ensure that our content remains competitive and continues to attract valuable organic traffic.

Leveraging AI for Performance-Based Content Adjustments

AI tools can further refine our content strategy by:

Analyzing search trends to suggest new keyword variations.

Identifying latent semantic indexing (LSI) keywords for broader topic coverage.

Generating A/B test variations of meta descriptions and headings.

Providing predictive analytics on which topics are likely to gain traction in the future.

By integrating AI-driven insights with GSC data, we can make informed decisions that align with evolving search behaviors.

Establishing a Feedback Loop for Continuous Improvement

A structured feedback loop ensures that we don’t just analyze performance data but actively use it to refine our content. This process includes:

Reviewing GSC data at regular intervals (weekly or monthly).

Implementing necessary optimizations based on performance insights.

Testing variations to see what resonates best with users.

Monitoring the impact of these changes and iterating further.

Over time, this approach helps us refine our SEO strategy, ensuring sustained organic growth.

Wrapping Up

Leveraging AI and Google Search Console (GSC) Regex for SEO content discovery enables a data-driven approach to identifying high-value informational queries. By generating a precise regex pattern with AI, we efficiently filtered search data to uncover gaps and opportunities, optimizing existing content and guiding new content creation. This method enhances search visibility, engagement, and authority by aligning with user intent. Continuously refining regex patterns ensures adaptability to evolving search trends, making this an invaluable strategy for sustainable SEO growth.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.