PlaybookPrompts

Score and rewrite every CTA on a landing page

Marketing & SEO crocopywritinglanding-pages

Weak CTA copy is one of the fastest conversion fixes — but most audits treat it as an afterthought. This prompt systematically evaluates every call-to-action on a page and rewrites each one with alternatives ready to A/B test.

Prompt
You are a conversion copywriter. I'm sharing the CTA copy from a landing page for {{PRODUCT_OR_OFFER}}. The page's primary conversion goal is {{CONVERSION_GOAL}} and the target visitor is {{TARGET_VISITOR}}.

CTA list (paste button text and surrounding context for each):
{{CTA_LIST}}

1. Score each CTA on a 1–5 scale across three dimensions:
   - Specificity: does it tell the visitor exactly what happens next?
   - Value clarity: does it reinforce what the visitor gets, not just what they do?
   - Friction signal: does it use high-friction words like 'Submit', 'Register', or 'Sign up' with no qualifier?

2. For each CTA that scores below 4 average, write 2 alternative versions:
   - One that emphasises the outcome the visitor gets
   - One that reduces perceived commitment (e.g. 'See pricing' instead of 'Buy now')

3. Identify if any two CTAs on the page compete with each other or ask for different levels of commitment in the wrong sequence.

4. Recommend which single CTA change would most likely move conversion rate first, and explain the reasoning.

Note: If the page has only one CTA, steps 3 and 4 will be shorter — focus scoring effort on steps 1 and 2.
Variables to fill in
  • {{PRODUCT_OR_OFFER}}
  • {{CONVERSION_GOAL}}
  • {{TARGET_VISITOR}}
  • {{CTA_LIST}}

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: {{PRODUCT_OR_OFFER}}{{CONVERSION_GOAL}}{{TARGET_VISITOR}}{{CTA_LIST}}.
  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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