PlaybookPrompts

Build a 5-email nurture sequence from one campaign brief

Writing & Content emailcopywritingnurture-sequencecampaigns

A campaign brief rarely arrives pre-divided into email beats. This prompt takes a single brief and produces a sequenced set of emails with distinct jobs — awareness, education, objection handling, social proof, and conversion — so no two emails sound alike.

Prompt
You are an email copywriter. Using the campaign brief below, write a 5-email nurture sequence. Follow these steps.

1. ANALYZE the brief and identify: the core offer, the primary audience pain, and one objection a skeptical reader would have.
2. ASSIGN each email a specific job before writing it:
   - Email 1: Establish relevance and open a problem loop
   - Email 2: Educate — one insight that reframes the problem
   - Email 3: Handle the main objection directly
   - Email 4: Social proof or concrete outcome (use placeholder [CASE STUDY] if no evidence is in the brief)
   - Email 5: Direct conversion ask with a clear deadline or reason to act
3. WRITE each email with:
   - Subject line + preview text
   - Body copy under {{MAX_EMAIL_LENGTH}} words
   - One call-to-action per email (no more)
4. Keep the voice consistent with: {{BRAND_VOICE_NOTE}}
5. Do not repeat the same opening sentence structure across emails.

Campaign brief:
{{CAMPAIGN_BRIEF}}

Edge case: If the brief describes a complex B2B product with a long sales cycle, a 5-email sequence may be too compressed. Note this if relevant and suggest an alternative cadence.
Variables to fill in
  • {{MAX_EMAIL_LENGTH}}
  • {{BRAND_VOICE_NOTE}}
  • {{CAMPAIGN_BRIEF}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{MAX_EMAIL_LENGTH}}{{BRAND_VOICE_NOTE}}{{CAMPAIGN_BRIEF}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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Pair this prompt with a tool

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