청중 질문 키워드 연구 템플릿

• •Google Sheets API 인증 정보
• •OpenAI API Key

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              "value": "Mid-30s professional living in a suburban area with a household income of $65,000-80,000. Works in healthcare administration with a stable 9-to-5 schedule and has two school-age children. Values reliability and practicality over flashy features.  Vehicle Needs: Seeks a dependable mid-size sedan or small SUV in the $22,000-32,000 range, preferably 1-3 years old. Prioritizes safety ratings, good gas mileage for the daily 20-mile commute, and enough space for car seats and groceries. Brand loyalty leans toward Honda, Toyota, or Mazda based on reputation for longevity.  Buying Process: Methodical researcher who spends 6-8 weeks comparing options online before visiting dealerships. Reads consumer reviews, checks reliability ratings, and calculates total cost of ownership. Prefers dealerships with transparent pricing and family-friendly service departments. Typically trades in every 6-7 years when repair costs start climbing or family needs change.  This persona represents the backbone of the used car market - practical buyers focused on transportation solutions rather than automotive enthusiasm."
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              "content": "=Output format:\nA list of 50 questions in a JSON array called \"questions\". each question in the array has an additional element which represents intent. Intent is either informational, navigational, commercial, transactional.\n\nYour Task:\nWhen analyzing the target customer profile, think through what questions they would actually ask ChatGPT, Claude, or Google AI Mode. Consider how they would phrase requests for investment advice, research help, analysis, and decision support. Think about their natural conversation patterns, the context they'd provide, and how they'd iterate on responses. Draw from your deep understanding of search behavior patterns from SEMrush and Ahrefs data to predict conversational AI query evolution.\n\nGenerate question examples - focusing on natural conversational queries, multi-turn interactions, and the specific ways this audience leverages AI for investment research and decision-making, backed by your professional marketing intelligence expertise.\n\nTarget customer profile:\n {{ $json.customer_profile }}"
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        "content": "## Audience Problem Keyword Research Workflow\n### This n8n template generates relevant keywords and questions from a a customer profile. Keyword data is enriched from ahref and everything is stored in a Google Sheet. This is great for market and customer research. Understanding search intent for a well defined audience and gives relevant actionable data in a fraction of time that manual research takes.\n\n### How it works\n* We'll define a customer profile in the 'Data' node\n* We use an OpenAI LLM to fetch relevant search intent as keywords and questions\n* We use an SEO MCP server to fetch keyword data from ahref free tooling\n* The fetched data is stored in the Google sheet\n\n### How to use\n* Make a copy of [this](https://docs.google.com/spreadsheets/d/10SEHuy5bYMrq_j1Tr2HBcM9I4O6ShYVV_k2tKEfxteI/edit?usp=sharing) Google Sheet and add it in all Google Sheet nodes\n* Make sure that n8n has read & write permissions for your Google sheet. For my self-hosted n8n instance I was using a [Google Service Account](https://docs.n8n.io/integrations/builtin/credentials/google/service-account/)\n* Add your OpenAI account ([API Key](https://docs.n8n.io/integrations/builtin/credentials/openai/#using-api-key)) in the LLM nodes\n* Add your customer profile in the 'Data' node\n* Add MCP credentials for [seo-mcp](https://github.com/cnych/seo-mcp). Make sure you set the environments correctly:\n```json\n\"command\": \"uvx\",\n\"args\": [\"--python\", \"3.10\", \"seo-mcp\"],\n\"env\": {\n  \"CAPSOLVER_API_KEY\": \"CAP-xxxxxx\"\n}\n```\n* Execute workflow :)\n\n### Requirements\n* CapSolver account and API key ([register here](https://dashboard.capsolver.com/passport/register?inviteCode=p-4Y_DjQymvt)) to use [seo-mcp](https://github.com/cnych/seo-mcp)\n* Google Drive account"
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