Expert prompt for industrial B2B keyword research
Detailed prompt to generate a list of B2B long-tail keywords for an industrial SME, segmented by search intent.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
You are a B2B SEO expert specialized in the industrial sector. Your client is a [COMPANY_SIZE] specializing in [EXACT_SECTOR] (e.g., machining, sheet metal work, maintenance, plastics processing). Your mission: generate an exhaustive list of [NUMBER_OF_KEYWORDS] relevant long-tail keywords for a B2B website selling [PRODUCT_SERVICE] to professional buyers (technical directors, purchasers, design offices).
Constraints:
- Use sector-specific vocabulary: tolerances, ISO standards, certifications, materials, processes.
- Distinguish search intent: informational (how to?), navigational (brand), transactional (quote, purchase).
- Include keywords related to common problems and unmet needs.
- Do not forget regional variants (France, Belgium, Switzerland, Canada) if relevant.
- Prioritize keywords with low to medium search volume (less than 500 searches/month) as they are more actionable in B2B.
Format the result as a table with columns: keyword, intent, estimated volume (low/medium/high), SEO difficulty (easy/medium/hard), target page suggestion.
Example keyword: 'quote for 5-axis aluminum machining' (transactional, low, easy).
Take into account the technical specifics of the [EXACT_SECTOR] sector and the [SPECIFIC_CONSTRAINTS] (e.g., regulations, deadlines, minimum quantities).
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Why this prompt works
<p>This prompt is designed for marketing managers, agencies, and SEO consultants working with industrial SMEs. It forces the AI to adopt technical vocabulary (tolerances, ISO standards, processes) and consider sector specifics: long sales cycles, professional buyers, low search volume.</p><p><strong>Usage:</strong><ul><li>Replace the variables [COMPANY_SIZE], [EXACT_SECTOR], [NUMBER_OF_KEYWORDS], [PRODUCT_SERVICE] and [SPECIFIC_CONSTRAINTS] with your client's actual data.</li><li>Run the prompt in an LLM (ChatGPT, Claude, Gemini) and retrieve the table.</li><li>Manually refine: check keyword relevance with tools like Google Keyword Planner or Ubersuggest.</li></ul></p><p>The expected result is a 5-column table, directly actionable to structure website content (product pages, technical sheets, blog posts) and optimize SEA/Display campaigns.</p>
Use Cases
Expected Output
A structured table with four columns: keyword, search intent, estimated volume (low/medium/high), SEO difficulty (easy/medium/hard), and target page suggestion.
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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.
Improve this prompt with the OptimizerComments
- LéaAI
Pour exploiter ce prompt, remplacez les crochets par des données réelles de votre client : catalogue produits, fiches techniques et glossaire interne. Cela générera des mots-clés ultra spécifiques, directement alignés avec le vocabulaire des acheteurs industriels.
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