Analyze won and lost deals systematically to uncover patterns, improve sales strategies, and increase win rates.
Copy & paste the prompt below into your preferred LLM. Unless a specific AI model is mentioned, you can use whichever you prefer.
You are a sales operations expert. Build a win-loss analysis program for my sales team. My context: - Company: [NAME] - Product/Service: [WHAT YOU SELL] - Average deal size: [AMOUNT] - Current win rate: [PERCENTAGE, OR "UNKNOWN"] - Sales team size: [NUMBER] - Primary competitors: [3-5 COMPETITORS] - CRM: [SALESFORCE / HUBSPOT / PIPEDRIVE / OTHER] - Current analysis: [NONE / INFORMAL / STRUCTURED] Build: 1. **Data Collection:** 25 data points to capture for every closed deal. Rep self-assessment questionnaire. Manager debrief template 2. **Interview Guides:** 15 questions for win interviews, 15 for loss interviews, 10 for "no decision" interviews. Email templates for requesting interviews 3. **Analysis Dimensions:** Win/loss by competitor, by lead source, by deal size, by industry, by rep. Sales process analysis. Feature comparison gaps 4. **Dashboard Templates:** Monthly win-loss dashboard with key metrics. Quarterly deep-dive report template. Executive briefing format 5. **Insight-to-Action Process:** How findings become sales enablement improvements, product feedback, marketing messaging changes, and competitive battlecard updates 6. **Implementation:** 30-60-90 day rollout plan. How to get sales team buy-in. Technology requirements 7. **Benchmarks:** Industry benchmark win rates. Healthy vs. concerning patterns You can't improve what you don't analyze. Every lost deal is a lesson; most companies just never learn it.
What separates "Win-Loss Analysis Framework" from an off-the-cuff AI question is precision. It applies context framing and negative constraints and analytical framing, which gives the model enough direction to produce buyer-psychology-informed messaging that handles objections and moves deals forward. Expect buyer-psychology-informed messaging that handles objections and moves deals forward. The constraints in this prompt prevent the model from falling back on vague, unhelpful responses.
These sales tips will help you get stronger results when using "Win-Loss Analysis Framework" and similar prompts in this category.
"Win-Loss Analysis Framework" is particularly useful in these situations. If any of these scenarios sound familiar, this prompt will save you significant time.
When you use "Win-Loss Analysis Framework" with ChatGPT, Claude, or Gemini, here is what to expect in the AI output.
Adapt "Win-Loss Analysis Framework" to your specific situation by modifying these key areas. The more context you add, the better the results.