ThinkWithPrompt by SynotticThinkWithPrompt

Write a deal desk request to approve a discount

An internal approval request that justifies a discount or non-standard terms with the business case and trade-offs.

SalesFew-ShotStandardWriteFill-in Template

The prompt

Help me write a request to my deal desk or manager to approve a discount or non-standard terms. Deal: [CUSTOMER, PRODUCT, LIST PRICE, TERM] What I'm requesting: [e.g. 22% discount, net-60 payment, price lock, extra seats] Our discount policy and approval levels: [DETAILS or "unknown"] Why the buyer is asking: [BUDGET LIMIT, COMPETING QUOTE, PROCUREMENT TARGET] What I've already tried: [e.g. offered annual prepay, reduced scope] What we get in return: [e.g. 3-year term, signature by a date, case study, logo rights, upfront payment, larger volume] Strategic value: [e.g. new vertical, expansion potential, competitive displacement] Deal confidence: [HOW LIKELY TO CLOSE IF APPROVED, AND WHY] Write a request that: - States the ask and the deadline in the first two lines. - Shows the math: list price, proposed price, effective discount, total contract value, and what changes in margin or payback if I have that data. - Ties every concession to something we get back. - Names the alternative if denied (lose the deal, smaller deal, delay) honestly. - Offers a fallback position I would accept. Return the request (under 250 words), a one-line summary for Slack, and 3 questions approvers are likely to ask, with my best answers. Use [ADD] where I haven't provided a number.

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

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What to fill in

  • CUSTOMER, PRODUCT, LIST PRICE, TERM
  • e.g. 22% discount, net-60 payment, price lock, extra seats
  • DETAILS or "unknown"
  • BUDGET LIMIT, COMPETING QUOTE, PROCUREMENT TARGET
  • e.g. offered annual prepay, reduced scope
  • HOW LIKELY TO CLOSE IF APPROVED, AND WHY
  • ADD

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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