Win/Loss Analysis
Find the patterns behind wins and losses across CRM notes, call transcripts, and deal data.
What you'll need
- CRM export of closed deals (won and lost)
- Call recordings or transcripts
- Deal metadata (size, sales cycle, competitor, rep)
- Exit survey responses (if available)
Put it to work
Save these instructions in a Claude Skill or Project, or in a ChatGPT Project or Custom GPT. Add your context, run the sample prompt, and check the result against your team's standards before anyone relies on it.
Reusable playbook
Learn the method, adapt the details, then put it to work.
Start here
Reusable instruction — Claude: save as a skill file at ~/.claude/skills/sales/win-loss-analysis.md · ChatGPT: paste into a Custom GPT's instructions or a Project.
Give the AI a clear role
You are a sales analytics expert who identifies actionable patterns in win/loss data.
Bring the right context
- {{crm_export_path}} - CRM export of closed deals (won and lost)
- {{call_transcripts_path}} - Call recordings or transcripts (optional)
- CRM export with deal metadata (size, sales cycle, competitor, rep)
- Call transcripts or recordings (optional)
- Exit survey responses (optional)
- Date range and focus areas
Run the method
Given a dataset of closed deals
- 1
Segment the Data
- By outcome (won/lost)
- By competitor involved
- By deal size
- By sales rep
- By industry vertical
- 1
Identify Patterns
- Common objections in losses
- Differentiators cited in wins
- Stage where deals stall
- Pricing sensitivity signals
- Champion vs. no champion impact
- 1
Quantify Insights
- Win rate by segment
- Average sales cycle by outcome
- Conversion rates by stage
- Competitor-specific win rates
- 1
Generate Recommendations
- Process improvements
- Training needs
- Messaging adjustments
- Qualification criteria updates
04Preview the deliverableSee the shape of a strong answer before you run the workflow.
Win/Loss Analysis Report
Period: [Date range] Deals Analyzed: [Count] Overall Win Rate: [Percentage]
Executive Summary
[3-4 key findings in bullet points]
Win Patterns
- Pattern
- [Pattern]
- Frequency
- X% of wins
- Impact
- [Description]
| Pattern | Frequency | Impact |
|---|---|---|
| [Pattern] | X% of wins | [Description] |
Loss Patterns
- Pattern
- [Pattern]
- Frequency
- X% of losses
- Recommended Action
- [Action]
| Pattern | Frequency | Recommended Action |
|---|---|---|
| [Pattern] | X% of losses | [Action] |
Competitor Analysis
| Competitor | Deals | Win Rate | Key Differentiator |
|---|
Stage Analysis
| Stage | Conversion Rate | Avg Days | Drop-off Reason |
|---|
Recommendations
- 1
Immediate (This Week): [Action]
- 2
Short-term (This Month): [Action]
- 3
Strategic (This Quarter): [Action]
Quotes Worth Noting
- "[Direct quote from lost deal]" — Insight: [Takeaway]
- "[Direct quote from won deal]" — Insight: [Takeaway]
Try this prompt
Analyze our Q4 closed deals: - 45 closed-won - 32 closed-lost - Primary competitors: Competitor A, Competitor B Focus on why we're losing to Competitor A (12 losses) and what's working when we win enterprise deals ($100K+). CRM export attached: q4-deals.csv
Before you trust the output
- Run analysis monthly or quarterly
- Include direct quotes from customers
- Track if recommendations are implemented and impact
Your next step
Want this workflow to run reliably every week?
Bring Win/Loss Analysis to our free live workshop. We'll show you how to turn the reusable instructions into a working AI Agent Skill—without writing code.