Win/Loss Analysis

Analyze won and lost deals over a specified period to identify patterns in what works and what does not. Examines deal size, sales cycle length, competitor presence, objections encountered, and decision factors to inform strategy.

When to Use

• During quarterly business reviews to assess pipeline effectiveness
• When win rate drops and you need to understand why
• When entering a new market segment and need baseline data
• When refining pricing, positioning, or competitive strategy

Execution

1. Parse $ARGUMENTS for period (default: 6 months) and filter (default: all)
2. Pull deal data from CRM records and engagement tracker
3. Categorize deals by outcome (won, lost, stalled, disqualified)
4. For won deals: identify common success factors, average cycle length, deal size distribution
5. For lost deals: identify primary loss reasons, competitor displacement, common objections
6. Calculate win rate, average deal size, and cycle length trends
7. Generate actionable recommendations based on patterns

Output Format

Win/Loss Analysis | <period>
==============================

## Summary
- Total deals: N (W won, L lost, S stalled)
- Win rate: X%
- Avg deal size: $X (won) / $Y (lost)
- Avg cycle: X days (won) / Y days (lost)

## Win Patterns
| Factor | Frequency | Impact |
|--------|-----------|--------|
| ... | N/W deals | HIGH/MED/LOW |

## Loss Patterns
| Reason | Frequency | Lost To |
|--------|-----------|---------|
| ... | N/L deals | <competitor or internal> |

## Competitive Displacement
| Competitor | Deals Lost | Common Objection |
|------------|-----------|-----------------|
| ... | N | ... |

## Recommendations
1. ...
2. ...

## Next Action
- ...

Skill Chains

After this skill...	Consider...
Need CRM data first	[crm] to pull pipeline data
Competitor patterns emerge	[competitive-intel] for deeper analysis
Pricing issues identified	[pricing-model] to adjust (if available)
Results inform pitch strategy	[pitch] to refine messaging