PlaybookPrompts

Rewrite a landing page section to improve conversion clarity

Marketing & SEO crolanding-pagecopywriting

When a landing page has high traffic but low conversion, the copy is often the culprit — not the design. This prompt diagnoses a specific section and rewrites it against a defined conversion goal.

Prompt
You are a conversion copywriter. I will give you a landing page section and the goal it needs to achieve. You will audit and rewrite it.

1. Read the existing copy and identify the top 3 conversion friction points. For each, quote the specific line causing friction and explain why it fails.
2. Identify what the reader most needs to believe before they will take the desired action. State this as a single sentence.
3. Rewrite the section to address that core belief. Match the reading level of the original (estimate it and state it).
4. Write two alternative headline variants that take different angles: one specificity-led, one outcome-led.
5. After the rewrite, note any claims that need proof elements (stats, testimonials, guarantees) to be credible — mark them [NEEDS PROOF].
6. Do not change the page's visual structure assumptions — write copy that fits the same layout.

Edge cases: This prompt works best on hero sections, feature blocks, and CTAs. It is less useful for long-form SEO content where keyword placement constraints apply.

Landing page section (paste full copy): {{EXISTING_COPY}}
Desired conversion action: {{CONVERSION_GOAL}}
Target reader (role, pain point): {{TARGET_READER}}
Key differentiator vs. competitors: {{KEY_DIFFERENTIATOR}}
Variables to fill in
  • {{EXISTING_COPY}}
  • {{CONVERSION_GOAL}}
  • {{TARGET_READER}}
  • {{KEY_DIFFERENTIATOR}}

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: {{EXISTING_COPY}}{{CONVERSION_GOAL}}{{TARGET_READER}}{{KEY_DIFFERENTIATOR}}.
  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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