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Net Promoter Score Follow-up Email Sequence Strategy

Customer-service Customer Feedback intermediate 🤖 ChatGPT 👁 4 views

📝 The Prompt

Act as a customer success strategist and email marketing expert. Help me design an NPS follow-up email sequence that converts detractors, retains passives, and leverages promoters. My context: Product/service: [What you sell] Current NPS score: [If known] NPS survey trigger: [When you send the survey — post-purchase, after 30 days, quarterly] Detractor rate: [% scoring 0-6] Promoter rate: [% scoring 9-10] CRM/email tool: [HubSpot, Klaviyo, Intercom, Mailchimp, etc.] Team available for follow-up: [Can individuals respond personally or is it automated?] Please design three separate email sequences: 1. DETRACTORS (0-6) — Rescue sequence: - Immediate acknowledgment email - Personal outreach request - Resolution confirmation 2. PASSIVES (7-8) — Convert sequence: - Thank you + what would make it a 10? - Feature highlight based on their usage - Check-in after improvement 3. PROMOTERS (9-10) — Leverage sequence: - Thank you + referral ask - Case study / testimonial invitation - Loyalty reward or early access Write subject lines and email body for each message.

⚙️ Replace 7 placeholders: [What you sell] [If known] [When you send the survey — post-purchase, after 30 days, quarterly] [% scoring 0-6] [% scoring 9-10] [HubSpot, Klaviyo, Intercom, Mailchimp, etc.]

🎯 What this prompt does

This AI prompt helps you net promoter score follow-up email sequence strategy. Designed for customer feedback workflows in the customer-service category, it's a intermediate-level prompt you can copy directly into ChatGPT to get instant, production-ready results.

Use it when you need a intermediate prompt that produces clear, actionable output without wrestling with trial-and-error wording. Just copy, customize, and run.

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🚀 How to use this prompt

  1. Copy the prompt using the 📋 button above.
  2. Open ChatGPT (or Claude, Gemini, Perplexity, or your preferred LLM).
  3. Paste the prompt into a new chat. Replace 7 bracketed placeholders ([What you sell] [If known] [When you send the survey — post-purchase, after 30 days, quarterly] ) with your own details.
  4. Run the prompt and review the AI's response. Most outputs are usable immediately.
  5. Iterate if needed — if the tone, length, or structure isn't quite right, reply with "make it shorter", "use bullet points", or "make it more formal" and the AI will refine it.

💡 Tips for better results

  • Replace the bracketed placeholders ([What you sell], [If known], [When you send the survey — post-purchase, after 30 days, quarterly], [% scoring 0-6]) with your own specifics before sending.
  • If the first output isn't quite right, ask the AI to refine, rewrite, or add more detail — iteration is key.
  • For long outputs, ask for a section at a time (e.g. 'start with the introduction only') to keep quality high.
  • Combine this with other customer-service prompts to build an end-to-end workflow.
  • Save your favorite variations — small wording tweaks often produce noticeably different results.
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✨ What you'll get

When you run this prompt, expect ChatGPT to return:

  • A directly usable customer feedback output tailored to the details you provided
  • Clear structure (headings, bullets, or numbered sections) that you can drop into your workflow
  • Content that matches your specified tone and context
  • Results in under 30 seconds — no manual drafting required

Need a different angle? Just ask follow-up questions. The AI will adjust without you starting over.

🔄 3 variations to try

1

Make it more formal

Add "Use a formal, professional tone suitable for enterprise clients" at the start of the prompt.

2

Ask for multiple options

Append "Give me 5 alternative versions, each with a different angle or approach." after the main instruction.

3

Request structured output

Add "Return the response as a markdown table (or bullet list, or JSON)" so you can paste the result directly into your docs or code.

🏷 Tags

🔎 Find more prompts like this

Browse 50 more customer-service prompts or search the full library.

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