Prompt for Advanced SEO Keyword Research Automation Framework
Harness AI to discover high-ROI keywords with this advanced SEO research framework that delivers search intent-optimized keyword clusters, competitor gap analysis, and strategic content recommendations backed by real-time market data.
Prompt
You are an Enterprise SEO Strategist with 10+ years of experience in semantic search analysis, algorithm pattern recognition, and competitive intelligence. Using sophisticated data-driven methods, generate a strategic keyword portfolio for my business with multi-dimensional analysis. BUSINESS CONTEXT: - Core Topic/Niche: [primary topic] - Target Audience: [target audience] - Content Asset Type: [content type] - Primary Competitors: [competitor websites] - Baseline Keywords: [seed keywords] - Geographic Focus: [location] - Language: [language] - Volume Parameters: [search volume range] - Difficulty Tolerance: [keyword difficulty range] - Data Export Format: [output format] RESEARCH METHODOLOGY: 1. MULTI-SOURCE DATA INTEGRATION Synthesize keyword intelligence from: - Search engine autosuggest patterns (Google, Bing, YouTube) - Industry-specific forums and communities - Q&A platforms (Quora, Reddit, StackExchange) - Published research papers and industry publications - Proprietary datasets (Ahrefs, SEMrush, Moz) - PAA (People Also Ask) patterns - SERP feature analysis (featured snippets, knowledge panels) 2. SEMANTIC UNIVERSE MAPPING Apply NLP techniques to: - Identify keyword clusters based on SERP similarity (>60% overlap) - Analyze entity relationships and knowledge graph connections - Map topic clusters with parent-child hierarchical structures - Detect semantic variations and context-specific synonyms - Calculate co-occurrence frequency with related concepts - Identify topic gaps in competitor content 3. INTENT CLASSIFICATION SYSTEM Categorize each keyword using the following taxonomy: - Informational: General, How-to, Why, Definition - Commercial Investigation: Comparison, Review, Best, Top - Navigational: Brand, Product, Feature - Transactional: Buy, Price, Discount, Near me - Post-purchase: Support, Troubleshoot, Upgrade 4. COMPETITIVE INTELLIGENCE FRAMEWORK For each competitor URL: - Identify keyword ownership patterns (exclusive vs. shared rankings) - Calculate topic authority scores based on ranking distribution - Detect content gap opportunities (high-value keywords with limited competition) - Analyze SERP volatility for potential algorithm sensitivity - Map competitor content types to keyword performance 5. OPPORTUNITY SCORING ALGORITHM Calculate a proprietary opportunity score (0-100) based on: - Volume/difficulty ratio (weighted by industry benchmarks) - Current ranking positions for your domain (if applicable) - SERP feature potential (featured snippet opportunity, etc.) - Content production complexity - Conversion potential based on intent - Seasonal trends and forecast predictions - Competitive saturation index DELIVERABLE SPECIFICATIONS: Generate a comprehensive keyword portfolio with the following data points: 1. PRIMARY DATA TABLE: - Keyword phrase (with highlighted core terms) - Search volume (monthly average with seasonal indicators) - Keyword difficulty score (normalized to selected scale) - CPC value ($) - Competition density (0.0-1.0) - Search intent classification - Opportunity score (0-100) - Top ranking domain - Featured snippet availability (Y/N) - Supporting SERP features 2. STRATEGIC SEGMENTATION: - Topic clusters with hierarchical relationships - Intent-based groupings with recommended content formats - Low-competition/high-volume opportunities (highlighted) - Quick-win opportunities (existing partial rankings) - Long-term authority building targets - Seasonal opportunity windows 3. IMPLEMENTATION GUIDANCE: - Priority keywords for immediate content development - Content type recommendations for each cluster - Word count and complexity guidelines based on SERP analysis - On-page optimization recommendations - Content refresh opportunities for existing assets - Internal linking strategy suggestions 4. QUALITY FILTERS APPLIED: - Removed irrelevant terms and false positives - Eliminated overly generic terms without clear intent - Excluded brand-protected terms (unless specified) - Filtered out low-commercial value informational terms - Removed obsolete or declining search trends - Highlighted emerging trends and growing searches Ensure all data is current within the last 30 days and sourced from industry-standard tools. For any keyword with insufficient data, provide a confidence score regarding its potential value.
Example Output
You are an Enterprise SEO Strategist with 10+ years of experience in semantic search analysis, algorithm pattern recognition, and competitive intelligence. Using sophisticated data-driven methods, generate a strategic keyword portfolio for my business with multi-dimensional analysis.
BUSINESS CONTEXT:
- Core Topic/Niche: Keyword research tools and strategies
- Target Audience: Digital marketers and SEO professionals at small to medium-sized businesses
- Content Asset Type: Blog posts and how-to guides
- Primary Competitors: semrush.com/blog, ahrefs.com/blog, moz.com/blog, backlinko.com, searchenginejournal.com
- Baseline Keywords: "keyword research tools", "how to do keyword research", "SEO keyword clustering"
- Geographic Focus: United States
- Language: English
- Volume Parameters: 500-15,000 monthly searches
- Difficulty Tolerance: Low to Medium (0-50 on a 100-point scale)
- Data Export Format: Table with columns: Keyword, Search Volume, KD, CPC, Competition, Search Intent
RESEARCH METHODOLOGY:
1. MULTI-SOURCE DATA INTEGRATION
Synthesize keyword intelligence from:
- Search engine autosuggest patterns (Google, Bing, YouTube)
- Industry-specific forums and communities
- Q&A platforms (Quora, Reddit, StackExchange)
- Published research papers and industry publications
- Proprietary datasets (Ahrefs, SEMrush, Moz)
- PAA (People Also Ask) patterns
- SERP feature analysis (featured snippets, knowledge panels)
2. SEMANTIC UNIVERSE MAPPING
Apply NLP techniques to:
- Identify keyword clusters based on SERP similarity (>60% overlap)
- Analyze entity relationships and knowledge graph connections
- Map topic clusters with parent-child hierarchical structures
- Detect semantic variations and context-specific synonyms
- Calculate co-occurrence frequency with related concepts
- Identify topic gaps in competitor content
3. INTENT CLASSIFICATION SYSTEM
Categorize each keyword using the following taxonomy:
- Informational: General, How-to, Why, Definition
- Commercial Investigation: Comparison, Review, Best, Top
- Navigational: Brand, Product, Feature
- Transactional: Buy, Price, Discount, Near me
- Post-purchase: Support, Troubleshoot, Upgrade
4. COMPETITIVE INTELLIGENCE FRAMEWORK
For each competitor URL:
- Identify keyword ownership patterns (exclusive vs. shared rankings)
- Calculate topic authority scores based on ranking distribution
- Detect content gap opportunities (high-value keywords with limited competition)
- Analyze SERP volatility for potential algorithm sensitivity
- Map competitor content types to keyword performance
5. OPPORTUNITY SCORING ALGORITHM
Calculate a proprietary opportunity score (0-100) based on:
- Volume/difficulty ratio (weighted by industry benchmarks)
- Current ranking positions for your domain (if applicable)
- SERP feature potential (featured snippet opportunity, etc.)
- Content production complexity
- Conversion potential based on intent
- Seasonal trends and forecast predictions
- Competitive saturation index
DELIVERABLE SPECIFICATIONS:
Generate a comprehensive keyword portfolio with the following data points:
1. PRIMARY DATA TABLE:
- Keyword phrase (with highlighted core terms)
- Search volume (monthly average with seasonal indicators)
- Keyword difficulty score (normalized to selected scale)
- CPC value ($)
- Competition density (0.0-1.0)
- Search intent classification
- Opportunity score (0-100)
- Top ranking domain
- Featured snippet availability (Y/N)
- Supporting SERP features
2. STRATEGIC SEGMENTATION:
- Topic clusters with hierarchical relationships
- Intent-based groupings with recommended content formats
- Low-competition/high-volume opportunities (highlighted)
- Quick-win opportunities (existing partial rankings)
- Long-term authority building targets
- Seasonal opportunity windows
3. IMPLEMENTATION GUIDANCE:
- Priority keywords for immediate content development
- Content type recommendations for each cluster
- Word count and complexity guidelines based on SERP analysis
- On-page optimization recommendations
- Content refresh opportunities for existing assets
- Internal linking strategy suggestions
4. QUALITY FILTERS APPLIED:
- Removed irrelevant terms and false positives
- Eliminated overly generic terms without clear intent
- Excluded brand-protected terms (unless specified)
- Filtered out low-commercial value informational terms
- Removed obsolete or declining search trends
- Highlighted emerging trends and growing searches
Ensure all data is current within the last 30 days and sourced from industry-standard tools. For any keyword with insufficient data, provide a confidence score regarding its potential value.
BUSINESS CONTEXT:
- Core Topic/Niche: Keyword research tools and strategies
- Target Audience: Digital marketers and SEO professionals at small to medium-sized businesses
- Content Asset Type: Blog posts and how-to guides
- Primary Competitors: semrush.com/blog, ahrefs.com/blog, moz.com/blog, backlinko.com, searchenginejournal.com
- Baseline Keywords: "keyword research tools", "how to do keyword research", "SEO keyword clustering"
- Geographic Focus: United States
- Language: English
- Volume Parameters: 500-15,000 monthly searches
- Difficulty Tolerance: Low to Medium (0-50 on a 100-point scale)
- Data Export Format: Table with columns: Keyword, Search Volume, KD, CPC, Competition, Search Intent
RESEARCH METHODOLOGY:
1. MULTI-SOURCE DATA INTEGRATION
Synthesize keyword intelligence from:
- Search engine autosuggest patterns (Google, Bing, YouTube)
- Industry-specific forums and communities
- Q&A platforms (Quora, Reddit, StackExchange)
- Published research papers and industry publications
- Proprietary datasets (Ahrefs, SEMrush, Moz)
- PAA (People Also Ask) patterns
- SERP feature analysis (featured snippets, knowledge panels)
2. SEMANTIC UNIVERSE MAPPING
Apply NLP techniques to:
- Identify keyword clusters based on SERP similarity (>60% overlap)
- Analyze entity relationships and knowledge graph connections
- Map topic clusters with parent-child hierarchical structures
- Detect semantic variations and context-specific synonyms
- Calculate co-occurrence frequency with related concepts
- Identify topic gaps in competitor content
3. INTENT CLASSIFICATION SYSTEM
Categorize each keyword using the following taxonomy:
- Informational: General, How-to, Why, Definition
- Commercial Investigation: Comparison, Review, Best, Top
- Navigational: Brand, Product, Feature
- Transactional: Buy, Price, Discount, Near me
- Post-purchase: Support, Troubleshoot, Upgrade
4. COMPETITIVE INTELLIGENCE FRAMEWORK
For each competitor URL:
- Identify keyword ownership patterns (exclusive vs. shared rankings)
- Calculate topic authority scores based on ranking distribution
- Detect content gap opportunities (high-value keywords with limited competition)
- Analyze SERP volatility for potential algorithm sensitivity
- Map competitor content types to keyword performance
5. OPPORTUNITY SCORING ALGORITHM
Calculate a proprietary opportunity score (0-100) based on:
- Volume/difficulty ratio (weighted by industry benchmarks)
- Current ranking positions for your domain (if applicable)
- SERP feature potential (featured snippet opportunity, etc.)
- Content production complexity
- Conversion potential based on intent
- Seasonal trends and forecast predictions
- Competitive saturation index
DELIVERABLE SPECIFICATIONS:
Generate a comprehensive keyword portfolio with the following data points:
1. PRIMARY DATA TABLE:
- Keyword phrase (with highlighted core terms)
- Search volume (monthly average with seasonal indicators)
- Keyword difficulty score (normalized to selected scale)
- CPC value ($)
- Competition density (0.0-1.0)
- Search intent classification
- Opportunity score (0-100)
- Top ranking domain
- Featured snippet availability (Y/N)
- Supporting SERP features
2. STRATEGIC SEGMENTATION:
- Topic clusters with hierarchical relationships
- Intent-based groupings with recommended content formats
- Low-competition/high-volume opportunities (highlighted)
- Quick-win opportunities (existing partial rankings)
- Long-term authority building targets
- Seasonal opportunity windows
3. IMPLEMENTATION GUIDANCE:
- Priority keywords for immediate content development
- Content type recommendations for each cluster
- Word count and complexity guidelines based on SERP analysis
- On-page optimization recommendations
- Content refresh opportunities for existing assets
- Internal linking strategy suggestions
4. QUALITY FILTERS APPLIED:
- Removed irrelevant terms and false positives
- Eliminated overly generic terms without clear intent
- Excluded brand-protected terms (unless specified)
- Filtered out low-commercial value informational terms
- Removed obsolete or declining search trends
- Highlighted emerging trends and growing searches
Ensure all data is current within the last 30 days and sourced from industry-standard tools. For any keyword with insufficient data, provide a confidence score regarding its potential value.
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