Analyze sales win/loss patterns

**Role:** You are a Product Manager analyzing win/loss patterns from {number_of_deals} recent sales deals for {product_name}.

**Context:**
Win/loss analysis is one of the most under-utilized product research tools. Lost deals reveal product gaps, positioning weaknesses, and competitive vulnerabilities. Won deals reveal what actually drives purchase decisions — which may be different from what marketing emphasizes. This analysis bridges the gap between what product managers think matters and what actually drives market outcomes.

**Task:**
Analyze the win/loss data systematically. Apply step-back thinking: before analyzing individual deals, identify the deal characteristics that correlate with wins vs. losses (segment, deal size, competitive context, buying process).

**Input Available:**
- {product_name}: Product name
- {win_loss_data}: Deal data including — outcome (won/lost), reason given, competitor involved, deal size, customer segment, sales cycle length, key objections raised
- {number_of_deals}: Total deals analyzed
- {win_rate}: Overall win rate for context
- {competitors}: Key competitors appearing in the data

**Output Format:**
1. Win/loss patterns: Key characteristics that predict wins vs. losses
2. Top reasons for winning: With frequency and example quotes from buyers
3. Top reasons for losing: With frequency, competitor involved, and product gap identified
4. Competitive win rates by competitor: Where are you strong/weak vs. each competitor?
5. Segment analysis: Win rates by customer segment/size/vertical
6. Product gap prioritization: Features or capabilities most frequently cited in losses — ranked by frequency and deal value impact
7. Positioning recommendations: What to emphasize more/less based on win patterns
8. Sales enablement needs: Objections that sales needs better answers for

**Guardrails & Quality Control:**
- Reasons salespeople report for losses may differ from actual buyer reasons — flag where you suspect reporting bias
- Small sample sizes (<20 deals) limit statistical reliability — acknowledge uncertainty
- Separate product gaps from pricing objections from relationship factors — they require different responses
- A feature frequently requested by lost deals is not automatically worth building if it is a commodity feature competitors offer but users don't truly value