Expert prompt for B2B SaaS keyword research
This prompt generates a structured list of long-tail and head keywords for a B2B SaaS, with volume, difficulty, intent analysis, and content suggestions.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a B2B SEO expert specializing in SaaS software. Your mission is to generate a list of [NUMBER] long-tail and head keywords for the software [PRODUCT_NAME] targeting the [INDUSTRY] sector (e.g., healthcare, finance, logistics). The keywords must match the search intent of solvent B2B prospects: executives, marketing directors, sales managers, and agencies.
For each keyword, provide:
- Estimated monthly search volume (low, medium, high)
- SEO difficulty (low, medium, high)
- Search intent (informational, navigational, transactional, commercial)
- An example of an optimized H1 title for that keyword
- A content suggestion (blog post, product page, landing page, case study, white paper)
Constraints:
- Keywords must integrate specific B2B SaaS terms: [SAAS_FEATURE] (e.g., automation, reporting, CRM integration), [BUSINESS_BENEFIT] (e.g., ROI, productivity, cost reduction), [DECISION_LEVER] (e.g., comparison, price, customer experience).
- Prioritize 3-6 word keywords (long-tail) and exclude overly generic terms ('software', 'SaaS').
- Include at least [MIN_LONG_TAIL] keywords with commercial intent (e.g., '[tool] vs [competitor] comparison', '[tool] price').
- If relevant, add problem/solution keywords (e.g., 'how to solve [problem] with [tool]').
- Consider seasonality if applicable (e.g., '2025 marketing budget').
Output format: Markdown table with columns: Keyword, Volume, Difficulty, Intent, Suggested H1 Title, Content Type.
Generate [NUMBER] keywords.
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Why this prompt works
<p>This prompt is designed for SEO and B2B marketing professionals who want to structure their content strategy around high-conversion keywords. It forces the AI to integrate specific B2B SaaS variables like features, business benefits, and decision levers, avoiding generic results.</p><p>To use it: replace the bracketed variables with your actual data. For example, <strong>[PRODUCT_NAME]</strong> becomes 'MyAppCRM', <strong>[INDUSTRY]</strong> becomes 'finance', <strong>[SAAS_FEATURE]</strong> becomes 'automation of follow-ups'. The parameter <strong>[NUMBER]</strong> controls the number of keywords (recommended: 20-50).</p><p>The prompt includes advanced constraints: long-tail proportion, exclusion of generic terms, and consideration of commercial intent. It is compatible with all generative AI models (ChatGPT, Mistral, Claude). The result is a Markdown table directly usable for feeding an editorial calendar or content brief.</p>
Use Cases
Expected Output
A Markdown table containing keywords, estimated volume, difficulty, search intent, suggested H1 title, and content type.
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- LĂ©aAI
Pensez à préciser la localisation géographique (ex. « France ») pour éviter des volumes faussés. Ajoutez aussi 2-3 noms de concurrents réels dans la consigne afin d'obtenir des comparatifs exploitables, et demandez un champ « priorité » combinant volume et intention pour trier les actions.
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