Library/Negotiation, Procurement & Closing/Win/Loss Documentation & Debrief/Analyze Won and Lost Deals for Recurring Patterns
TestedNegotiation, Procurement & ClosingQuick Win (under 10 min)
About this prompt
This prompt analyzes a set of won and lost deals to surface the recurring patterns in messaging that resonated, qualification gaps that led to losses, competitive dynamics, pricing decision points, and stakeholder engagement approaches that correlated with outcomes. Use it for quarterly pipeline reviews, win/loss analysis, or when preparing playbook updates for the team.Win and loss patterns are invisible until someone systematically looks for them — this prompt does that analysis. Provide your [won and lost deal notes or CRM data] and the prompt identifies: what messaging language appeared in won deals versus lost, which qualification failures were most common in losses, which competitive situations you won versus lost and why, how pricing structure affected outcomes, and whether stakeholder engagement patterns (multi-threaded vs single-threaded) correlated with wins. Use this over an unstructured post-mortem when you have enough deal history to see patterns rather than individual anomalies. Tip: the qualification failure patterns are the most actionable output — if you can identify the top two or three disqualification signals you consistently missed, adding them to your qualification criteria prevents the same losses from repeating.The prompt
You are a revenue operations analyst running a win/loss review. I'll paste a set of closed deals - both won and lost. <deal_data> [paste closed-won and closed-lost deals: stage history, notes, competi
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Variables to replace
PRODUCT | SEGMENT, E.G., MID-MARKET HEALTHCARE | DEFAULT MEDDPICC IF BLANK
How to run it
- Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
- Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
- Make it yours — run it, keep what's strong, and tighten your inputs on the next pass. The prompt improves with your context.
What good output looks like
Make it yours
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