1. Overview and Purpose

Keyword gap analysis helps you find terms for which your competitor ranks but your site does not. By identifying these gaps, you can create or optimize content to capture additional organic search traffic. The process typically involves comparing keyword lists from your site and a competitor, then using a tool to surface missing opportunities.

2. Gather and Prepare Keyword Data

• Download keyword data for your site from Google Keyword Planner (or another keyword data source). Save the data as a CSV file.
• Download keyword data for a competitor from the same source. Save this data as a separate CSV file.
• Ensure the data sets include the keywords and related metrics you plan to analyze (e.g., search terms, ranking position, volume, or relevance where available).

Note: The two CSV files represent the target keywords of your site and the keywords for your competitor, forming the basis for the gap analysis.

3. Leverage AI Tools to Perform the Gap Analysis

AI tools can help process the two keyword lists and identify keywords where your competitor ranks but your site does not. The example approach below uses an AI assistant to perform the analysis and generate a comprehensive list of missing opportunities.

Example prompt for AI analysis

Based on the attached CSV files, perform a keyword gap analysis and provide Rank Math (or your business) with a comprehensive list of keywords that your competitor is ranking for but Rank Math isn’t.

Implementation steps aligned with this approach:

• Upload both keyword CSV files to the AI tool (for example, ChatGPT).
• Use the example prompt or a similar clear instruction to request a gap analysis.
• Run the prompt and review the resulting list of missing keywords.

Important notes:

• Outputs may vary depending on the AI tool and prompt wording, but the core goal is to surface missing keyword opportunities where your competitor holds rankings that you do not.
• Include any caveats related to data freshness and keyword coverage when interpreting results.

4. Interpreting and Prioritizing the Results

Once the AI-generated list of gaps is obtained, organize the keywords to enable actionable optimization. Consider the following criteria:

• Relevance: How closely the keyword matches your products, services, or content themes.
• Search intent alignment: Ensure the keywords reflect informational, navigational, or transactional intent appropriate for your pages.
• Potential impact: Prioritize terms with higher perceived traffic potential and strategic importance.
• Competitiveness: Assess keyword difficulty or ranking challenges to balance quick wins with long-term opportunities.

5. Translating Gaps into Actionable SEO Steps

The identified gaps should drive concrete optimization and content strategies. Consider the following actions:

• Content creation: Develop new pages or comprehensive guides targeting high-priority missing keywords.
• Content optimization: Update existing pages to incorporate the missing keywords where relevant and improve on-page SEO signals.
• Topic expansion: Create pillar content and supporting articles that cover related keywords and themes identified in the gap analysis.
• Internal linking: Connect new or updated content to relevant pages to boost topical authority and link equity.
• Monitoring with Rank Math: Track keyword rankings and page performance using Rank Math (or your preferred SEO tool) to measure progress over time.

6. Best Practices and Considerations

• Validate intent: Ensure targeted keywords match user intent and are actionable for your content strategy.
• Balance volume and difficulty: Prioritize keywords that offer meaningful traffic potential while remaining achievable.
• Avoid cannibalization: Ensure new or updated content serves distinct search intents and topics to prevent internal competition between pages.
• Quality over quantity: Focus on creating high-quality, relevant content rather than chasing a long list of low-value keywords.
• Data freshness: Regularly refresh keyword data and re-run gap analyses to capture shifts in search trends and competition.

7. Conclusion

Keyword gap analysis is a powerful method for uncovering opportunities where competitors outperform your site. By comparing your keyword data with a competitor’s, leveraging AI to surface missing terms, and translating findings into targeted content and optimization efforts, you can expand your organic visibility. Use tools like Rank Math to monitor progress and refine your strategy over time, ensuring that your site closes the gap on important keywords.

Overview of the Method

The core idea is to pinpoint who currently ranks for your target keywords without relying on paid ads or complex software. By focusing on organic results on the first page, you can determine the sites that dominate your niche and use that information to inform content gaps, benchmarking, and overall strategy.

An example scenario described in the material centers on a fitness blog targeting the keyword “best home workout plan.” The process begins with inspecting top pages and noting which domains appear on the first page of search results.

Step-by-Step Approach to Identify Competitors

Step 1: Define the Target Keyword

Start with a clear, representative keyword or phrase that aligns with your niche and content goals. This keyword becomes the basis for identifying who competes for attention in that topic space.

Step 2: Use Incognito Mode to Avoid Personalization

Open the search in incognito or private browsing mode to minimize personalized results. This helps ensure you’re seeing a more objective set of organic rankings rather than results tailored to previous browsing behavior.

Step 3: Review Organic Results on the First Page

Examine the organic results on the first page. The goal is to identify the top competitor pages that are ranking for your target keyword. In the example provided, the top pages included brands such as HealthHub, Anytime Fitness, and Nerd Fitness.

Step 4: Compile a List of Ranking Competitors

Beyond the initial three, note all competitor pages that appear on the first page of search results. This list forms the core set of online competitors to analyze and monitor.

Step 5: Expand Beyond the Top Results

The first-page results often span a range of content types, including information sites, blogs, and forums. For example, in addition to the primary pages, you may encounter sites like Healthline and various discussions or forum pages. This broader view helps capture a fuller picture of the competitive landscape.

Example Scenario: Analyzing “Best Home Workout Plan”

When targeting the keyword “best home workout plan,” the initial top competitor pages might include HealthHub, Nerd Fitness, and Healthline. As you scroll through the search results, you should pay attention to the organic listings that appear on the first page, including any forums or discussions that rank for this query.

• Top pages: HealthHub, Nerd Fitness, Healthline
• Other first-page results: a mix of forums and discussion platforms, along with additional content sites

Practical Tips for Reading and Using the Results

• Ignore ads and focus on organic results to understand true ranking dynamics.
• Take note of the types of content that rank well (e.g., guides, tutorials, reviews, forums).
• Evaluate the domains that consistently appear on the first page to identify main competitors.
• Use these findings to benchmark your own content and identify potential gaps to fill.

Conclusion

Finding online competitors using a simple, tool-free approach provides a clear view of the landscape in your niche. By defining a target keyword, using incognito mode, and focusing on first-page organic results, you can identify the leading pages and platforms that currently attract attention. This information serves as a foundation for competitive benchmarking, content planning, and opportunity identification without the need for advanced tools.

1. Align with AI-influencing sources through original research

Identify the groups, brands, and websites that most influence AI platforms within your niche. Original research helps you understand which sources AI systems actually rely on, rather than relying on broad statistics that may not reflect your domain.

• Compile a comprehensive keyword list relevant to your business (for example, 100 queries that a large language model (LLM) would need to search the web for in your niche).
• If uncertain, request an AI to generate a list of 100 queries related to your industry and location, using placeholders when needed.
• Organize the results in a spreadsheet, including fields for placeholders (e.g., city, region, ZIP code) and platform-specific sheets (one for ChatGPT, one for Perplexity, etc.).
• For each platform, maintain a separate data sheet and assign research tasks to team members or focus on one platform at a time if you work solo.
• Record which websites AI platforms cite for each query. Track the frequency of each site to determine overall influence in your niche and location.
• Use the resulting data to align your brand with the most cited sources, increasing relevance and potential recognition by AI systems.

This approach emphasizes your own data rather than relying on generic, broad statistics.

2. Leverage influential discussion platforms to shape AI perception

Independent research indicates that certain forums and discussion sites frequently appear in AI-driven search results. For example, Reddit threads are common in Google results and appear in AI overviews, while other platforms like Kora (a knowledge-sharing site) also contribute to AI sourcing.

• Use Reddit and Kora to reflect authentic user conversations about your products or services.
• Encourage satisfied customers to share their experiences on these platforms, which can influence how AI tools perceive your brand.
• Extract conversational queries from Reddit, Kora, and customer service logs to identify common questions AI platforms may surface.

Based on internal observations, Reddit appears in a large share of Google results and is cited in AI overviews, while Kora appears in a notable minority. Incorporating these channels can help AI platforms contextually align with your brand.

3. Generate conversational keywords with AI chat tools

AI chatbots can transform traditional keywords into conversational queries, which better reflect how users interact with AI systems. Use AI prompts to generate natural-language queries derived from your core keywords.

• Example workflow: Use an AI chatbot integrated with your SEO tool (for instance, a RankMath AI module’s RankBot) to convert a traditional keyword into a list of conversational questions.
• Prompt concept: “You are an expert in understanding how people search for things on LLMs. Generate conversational keywords based on the traditional keyword provided, making them natural and varied.”
• Review and refine the generated questions to cover related intents and variations, then target content around these queries.
• Complement this approach with traditional keyword tools (e.g., “People also ask” sections on Google and fourth-party tools like AlsoAsk) to broaden the set of related questions.

The idea is to broaden keyword coverage from static terms to user-centered questions that AI systems are likely to surface.

4. Identify questions with Q&A sources and optimize content around them

Beyond traditional SEO, gather questions users pose in AI contexts and build content that answers them clearly. Use commonly referenced sources such as Google’s “People Also Ask” and third-party tools to identify relevant questions.

• Search traditional keywords and review the “People Also Ask” (PAA) results to identify related questions.
• Use tools like AlsoAsk to obtain two levels of related questions and expand your topic coverage.
• Organize the questions into content that answers the same search intent on a single page, avoiding the creation of separate pages for every question (to prevent thin content).

5. Apply a traditional content approach with a practical AI twist

AI platforms still source from pages that reflect traditional content principles. Combine the familiar structure with AI-focused refinements to improve understanding by AI and readability for users.

• Follow a silo structure: group related queries that share the same intent on a single page to improve topical relevance.
• Avoid splitting related queries into multiple pages, which can create thin content.
• Integrate comprehensive answers on a single page to address the primary intent, then expand with subtopics using H2/H3 headings.

6. Optimize for LLM token limits and emphasize key takeaways

Large language models have token limits. For pages that exceed typical limits, place the most important information at the top of the page so LLMs read it first. A top-level “Key Takeaways” section improves both AI and traditional search understanding.

• For pages over about 2,000 words, add a key takeaway section at the top that summarizes the main points concisely.
• Enhance the key takeaway with frequently asked questions (FAQ) styled content to provide context and support for AI understanding (FAQ schema).
• Construct the page with practical formatting: use a top-row key takeaways area, followed by sections and subheadings, while preserving proper heading hierarchy.

Example implementation includes using a WordPress-like blocks approach: a column block for the key takeaways, an FAQ block for questions and answers, and careful heading hierarchy to maintain accessibility and clarity.

7. Structure headings for quick AI comprehension and broaden indexing across engines

Headline and paragraph structure helps AI quickly identify the topic and provide precise answers. After each heading, the immediately following paragraph should deliver a concise, direct answer to the heading’s question or topic.

• Use a clear heading hierarchy: H2 for main topics, H3 for subtopics. The paragraph after each heading should deliver a short, punchy response to that heading’s prompt.
• Consider traditional keyword targets that drive sales (e.g., RankMath vs Yoast, RankMath Free vs Pro) and craft consumer-focused questions around them to guide AI responses.
• Indexing across multiple search engines improves AI coverage. For Google AI overviews, ensure your site is indexed in Google. For AI interfaces like ChatGPT, a strong presence on Bing can help, and Brave may leverage its own indexing approach.
• Brave indexing: install Brave, enable the web discovery project in settings, and browse your site to register with Brave’s index. In many cases, established sites may already be indexed, but this is a practical step for newer sites.
• Submit verification IDs in RankMath’s general settings under Webmaster Tools to help indexing on other traditional search engines.
• Consider the llms.txt file: a root-level text file that lists your most important pages for AI models. It helps prioritization but should be curated to avoid overloading AI with too many pages due to token limits.

The llms.txt concept reflects a proposed standard to help AI models identify important pages; its use should be thoughtful to avoid listing excessive pages.

Conclusion

The seven approaches outlined provide a practical framework for optimizing your brand or website for AI search platforms. They emphasize original research, participation in influential sources, conversational keyword generation, Q&A-focused content, structured content for AI, top-of-page takeaways with FAQ context, and multi-engine indexing strategies. By applying these techniques, your site can gain better visibility in AI-driven results and improve overall search performance.

If you have conducted similar research or additional tactics, sharing your insights can help create a valuable resource for the community.

This article reflects guidance drawn from practical demonstrations and discussions about AI search optimization. For further details, explore related videos and resources from credible sources in your niche.

Why AI Search Changes SEO

Traditional searches rely on short, keyword-focused queries. In contrast, AI search enables full conversational questions with layered intent, allowing users to ask in natural language and receive concise, targeted answers. This shift means keyword research must evolve from aiming for short-tail and long-tail phrases to understanding conversational queries, the intent beneath them, and how AI passes context across results. Adoption of AI search is currently growing slowly, so early movers who align content with conversational queries can gain a strategic advantage.

Step 1 — Identify Conversational and Situational Queries

The first step in the AI-era approach is to identify conversational and situational queries rather than solely pursuing traditional long-tail keywords. A practical example is the dog-food scenario: dog owners may search for foods suitable for their dog’s symptoms. The goal is to discover the common denominator that connects customer problems, situations, or needs back to the product, and then break that denominator into logical subcategories to generate realistic queries.

Prompt framework commonly used in this process (described for use with ChatGPT):

I'm promoting a product or service, and We classify this as product. Always start with a please. Identify the common denominator that connects customer problems, situations, or needs back to this product. Then I'll give an example right here. Then break the denominator into logical subcategories categories. Then I give examples. Finally, provide conversational and situational queries that real people would ask online. Group under each subcategory that naturally point toward my product as the solution. This is the format that I want. Feel free to use the search tool. Let's say that we are promoting a subscription box for healthy meals. We can add that into the placeholder and hit Enter or Return. In just a while, we will have a list of conversational queries that are related to our business.
      

Examples of applying this approach include potential product lines such as a subscription box for healthy meals or other services (e.g., social media management, budgeting apps, or baby strollers). The method is designed to surface the kinds of conversational and situational queries real people would pose online and to organize them under subcategories that point toward the product as the solution.

Step 2 — Connect Conversational Queries to Traditional Keywords

Large language models (LLMs) do not rely solely on their training data; they also consult web pages related to a query. Most pages on the web are optimized for traditional keywords. Therefore, to ensure AI systems can reference relevant pages when answering conversational queries, marketers should create content around traditional keywords that correspond to the layered intents identified in the conversational queries.

A practical way to explore traditional keywords tied to a conversational query is to use an AI search platform such as Perplexity. For example, asking a query like “I want to stop eating out so much, but I don’t have time to cook. What should I do?” prompts Perplexity to search the web and surface traditional keywords from the results. By clicking on Sources, you can see SEO titles such as:

• Meal Prep for Busy People
• Healthy Meal Prep ideas
• Meal Prep Shortcuts for Busy People
• How to Stop eating out

These traditional keywords are the phrases that AI-powered systems may rely on when constructing answers to the original conversational query. The goal is to capture these traditional keywords and prepare content that targets them directly.

Additionally, the Steps tab in Perplexity can reveal what the AI is searching for, helping to identify the traditional keywords to target. The result is a spreadsheet-like list of traditional keywords corresponding to the conversational query.

Step 3 — Estimating Search Volume for Conversational Queries

There are no tools that accurately predict search volume for conversational queries. Even the presented method is not guaranteed to be precise. The approach described involves extracting traditional keywords from the conversational query and then using a traditional keyword tool to estimate volumes, followed by synthesizing those insights into an estimate for the conversational query.

How this estimation works (illustrative examples from the material):

• Example 1: Conversational query related to meal prep for busy people.
  • Traditional keywords identified include: Meal Prep for Busy People, Healthy Meal Prep ideas, Meal Prep Shortcuts for Busy People, How to Stop eating out.
  • Using Google Keyword Planner with the traditional keyword list (set worldwide), the data yields monthly search volumes for each keyword.
  • Intents are grouped and summed by similarity:
    • First intent: meal prep-related keywords (highest volume 10K–100K).
    • Second intent: phrases about avoiding eating out (volume 100–1K).
  • Estimated volume for the conversational query is the sum of these layered intents, approximately 10.1K to 101K searches per month.
• Example 2: Conversational query about foods for senior dogs with morning stiffness.
  • Traditional keywords identified include: dog food for senior dogs, dog food for arthritis, anti-inflammatory foods for dogs, arthritis diet tips, joint supplements for dogs, etc.
  • Three layered intents are identified, and their highest volumes are combined to estimate a conversational query volume of roughly 21K to 210K monthly searches.

The underlying principle is to account for layered intents—group related traditional keywords that would be triggered by the same conversational concept—and estimate the total volume accordingly. The emphasis is on creating content around the most impactful traditional keywords rather than chasing the most or least competitive terms.

Practical takeaway: build a customer avatar to map out conversational queries, then select the group of traditional keywords with the highest predicted impact to target first. The goal is to inform content strategy with intent-driven data rather than focusing solely on competition.

Step 4 — Content Strategy in the AI Era

In the AI search era, the objective is not merely to rank for traditional keywords in search engines. The broader goal is to ensure your content—across written content, video, and other formats—appears in AI-driven answers by being present across the traditional keywords that genuinely help customers. In other words, focus on helping your audience with relevant content that aligns with their problems, so when AI systems assemble answers, your brand is referenced as a trusted solution.

Decision guidance from the material includes:

• Identify conversational queries first, then determine the traditional keywords AI would surface in response.
• Use traditional keyword research tools to estimate volumes for those keywords and to approximate the volume for the conversational query via layered intents.
• Prioritize content that provides clear, helpful answers to customer needs and demonstrates relevance to buyer intent.
• Aim for broad coverage of relevant traditional keywords across multiple content formats to improve the likelihood of brand mention in AI-driven answers.

Practical Prompts and Examples

A practical example used in the discussion involves a product or service and a demonstration of how prompts can structure the output. The prompts are designed to classify the product, identify the common denominator tying customer needs to the product, break that denominator into subcategories, and generate related conversational and situational queries that point toward the product as the solution.

I'm promoting a product or service, and We classify this as product. Always start with a please. Identify the common denominator that connects customer problems, situations, or needs back to this product. Then I'll give an example right here. Then break the denominator into logical subcategories categories. Then I give examples. Finally, provide conversational and situational queries that real people would ask online. Group under each subcategory that naturally point toward my product as the solution. This is the format that I want. Feel free to use the search tool. Let's say that we are promoting a subscription box for healthy meals. We can add that into the placeholder and hit Enter or Return. In just a while, we will have a list of conversational queries that are related to our business.
      

A practical takeaway from this approach is that prompts can help generate structured, testable query sets that map to product advantages. The method scales to various industries, including consumer goods, services, and digital products.

Tools, Limitations, and Next Steps

• No existing tool can perfectly predict the search volume for conversational queries. The method described provides a reasoned estimation based on traditional keywords and layered intents.
• Perplexity can help identify traditional keywords associated with a conversational query by examining the sources and the topics those pages target.
• Google Keyword Planner can be used to estimate traditional keyword volumes; combine this with the layered-intent approach to approximate the conversational query volume.
• The overarching aim is to connect with customers by appearing across traditional keywords that address real problems, thereby increasing the likelihood that AI-driven answers reference your brand.

Examples of Application

Conclusion

The AI search era changes how keyword research should be conducted. Start with identifying conversational and situational queries, then map these queries to traditional keywords that AI systems will surface. Estimate the search volume of the conversational query by aggregating layered intents from traditional keywords, and prioritize content that helps customers and reinforces your brand as a trustworthy solution. This approach emphasizes helpful, intent-driven content across formats, with the aim of ensuring your brand is mentioned in AI-driven answers as consumer needs evolve. The method described here is a practical framework for adapting SEO and content strategy to AI-powered search.

Overview of the Solution

Viral Content Pilot is described as a tool that enables the creation of genuine email lists in about 60 seconds without the need for landing pages. A single link can be shared anywhere—on YouTube, social media, or a blog post—and real emails can be collected instantly. The core mechanism claimed is advanced Google authorization that verifies every subscriber, reducing the presence of fake emails on the list.

How Viral Content Pilot Works

1. Share a single link across channels. The link can be posted on YouTube, social media, a blog post, or other platforms.
2. Receive real email submissions. Visitors who click the link and submit their information are added to the list promptly.
3. Verify subscribers with Google authorization. The system uses advanced authentication to confirm the validity of each subscriber.
4. Avoid traditional landing pages. The process does not require building or maintaining landing pages to collect emails.

Key Benefits

• Speed and simplicity. Build a list in about a minute without configuring landing pages.
• Improved list quality. Verifying subscribers helps reduce fake emails.
• Wide reach with a single link. Shareable link works across YouTube, social channels, and blog posts.
• Potentially higher conversion. The approach is associated with notable increases in conversion rates in user reports.
• Free access for evaluation. A full-featured trial is available to test the results before committing.

Reported Impact and Claims

The provider claims that users have seen conversion rates increase significantly, citing examples such as moving from around 4% to over 68% in some cases. These figures are described as observed by users and may vary by context and implementation.

Getting Started

The solution is described as offering a free, full-featured trial. This allows potential users to test the capabilities and observe results firsthand before making any commitments.

If you are interested, you can begin with the free access to explore how the single-link approach and subscriber verification work for your audience.

Conclusion

Viral Content Pilot presents a streamlined approach to building an email list by removing the need for landing pages, ensuring subscriber authenticity, and leveraging a single shareable link across platforms. The combination of real-email verification and a no-landing-page workflow is positioned as capable of delivering faster list growth and higher-quality subscribers. A free, full-featured trial is available to assess the results for your audience.