PlaybookPrompts

Map a 5-email nurture sequence from a lead magnet topic

Marketing & SEO email-marketingnurtureconversion

Most lead magnets drop contacts into a generic welcome flow. This prompt builds a tight 5-email sequence where each email follows logically from the lead magnet's specific promise, improving open rates and list quality.

Prompt
You are an email marketing strategist. Using the lead magnet details below, design a 5-email nurture sequence.

1. For each email, produce: Subject line (under 50 characters), Preview text (under 90 characters), Core message in 2 sentences, Single CTA.
2. Email 1 must deliver the lead magnet and set expectations for the sequence — do not pitch.
3. Email 2 must extend one idea from the lead magnet with a concrete tip not covered in it.
4. Email 3 must introduce a pain point the lead magnet does not solve, and position {{PRODUCT_OR_SERVICE}} as the solution.
5. Email 4 must include a social proof element (case study snippet, stat, or testimonial format — I will fill in the real data).
6. Email 5 must be a direct offer email with a clear deadline or reason to act now.
7. After each subject line, rate its curiosity gap (1–5) and explain in one sentence.

Edge cases: If {{LEAD_MAGNET_TOPIC}} is very broad (e.g., 'marketing tips'), the sequence will be generic. Narrow it to a specific outcome before using this prompt.

Lead magnet title and topic: {{LEAD_MAGNET_TOPIC}}
Target audience: {{TARGET_AUDIENCE}}
Product or service being promoted: {{PRODUCT_OR_SERVICE}}
Sending cadence (e.g., every 2 days): {{SEND_CADENCE}}
Variables to fill in
  • {{LEAD_MAGNET_TOPIC}}
  • {{TARGET_AUDIENCE}}
  • {{PRODUCT_OR_SERVICE}}
  • {{SEND_CADENCE}}

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: {{LEAD_MAGNET_TOPIC}}{{TARGET_AUDIENCE}}{{PRODUCT_OR_SERVICE}}{{SEND_CADENCE}}.
  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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