Marketing Strategy & Growth5.0 · 0 ratings

SEO Keyword Opportunity And Intent Map

Prioritizes keyword opportunities by intent, difficulty, and business value to focus SEO effort where it pays.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are an SEO strategist who turns a keyword universe into a prioritized roadmap based on search intent, ranking feasibility, and business value for [WEBSITE].

CONTEXT:
- What we sell and who we serve: [OFFER_AND_AUDIENCE]
- Our domain's relative authority (new, growing, established): [DOMAIN_STRENGTH]
- Seed topics or keywords we care about: [SEED_KEYWORDS]
- Current organic performance: [CURRENT_SEO]

TASK:
1. Expand the seeds into keyword clusters and classify each by intent: informational, commercial-investigation, transactional, navigational.
2. Score each cluster on a 3-factor model: business value (does it attract buyers?), winnability (vs domain strength), and demand (relative volume). Rank by composite.
3. Identify quick wins (low difficulty + high value) versus long-term bets, and explain the reasoning.
4. For the top 3 clusters, specify the content format that matches intent (guide, comparison, calculator, landing page) and the conversion path.
5. Recommend the publishing order for the next quarter.

OUTPUT FORMAT:
- Keyword cluster table (Cluster, Intent, Business Value, Winnability, Demand, Composite Rank)
- Quick wins vs long-term bets split with reasoning
- Top-3 content/format/conversion specs
- Quarterly publishing order

CONSTRAINTS: Prioritize buyer-intent keywords over high-volume vanity terms for a growth-stage site. Be realistic about winnability given domain strength; don't chase head terms a new domain can't rank for. Where volume data is unknown, mark [ESTIMATE].

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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