ThinkWithPrompt by SynotticThinkWithPrompt

Build a multi-channel outbound cadence for one buyer persona

A day-by-day sequence of emails, calls, and LinkedIn touches with a distinct angle at every step.

SalesFew-ShotStandardEmailFill-in Template

The prompt

Build an outbound prospecting cadence for one buyer persona. Persona: [TITLE, SENIORITY, TEAM - e.g. "VP of Finance at 200-1,000 person companies"] Their likely problems we solve: [2-4 PAINS, in their words if possible] What we offer and the main outcome: [PRODUCT + RESULT] Proof I can use: [CUSTOMER RESULTS, LOGOS, STATS - or "none yet"] Channels available: [email / phone / LinkedIn / video / direct mail] Cadence length: [e.g. 12 touches over 21 business days] My goal: [e.g. book a 20-minute intro call] Rules: - Every touch must add something new: a different pain, proof point, question, or resource. No "just bumping this up." - Emails under 90 words, one ask each, plain text, no attachments on first touch. - Calls: give a 2-sentence opener and the one question to ask if they pick up. - LinkedIn: connection note under 250 characters; no pitch in the connection request. - Space touches realistically; vary send days and times. - Use only the proof I provided. Where proof would help but is missing, write [ADD PROOF]. Return: 1. A table: Day | Channel | Angle | Purpose of this touch. 2. The full copy for every touch, including subject lines. 3. A short breakup email for the final touch that leaves the door open. 4. Three personalization slots I should fill per prospect, and where they go.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • PRODUCT + RESULT
  • CUSTOMER RESULTS, LOGOS, STATS - or "none yet"
  • email / phone / LinkedIn / video / direct mail
  • e.g. 12 touches over 21 business days
  • e.g. book a 20-minute intro call
  • ADD PROOF

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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