Analyze why deals are won

Win/Loss Analysis Skill

Turn raw deal outcomes and buyer feedback into a clear picture of why you win and lose — and what to do about it. The output should let a product marketer or revenue leader act on patterns, not anecdotes.

What This Skill Produces

• A win/loss report with the top reasons deals were won and lost, ranked by frequency and deal value
• Win/loss rates cut by segment, deal size, competitor, and source where the data allows
• Representative buyer quotes that make each theme concrete
• A prioritized action list mapped to product, marketing, sales, and pricing owners

Required Inputs

Ask for these if not provided:

• Deal data — a list of closed-won and closed-lost deals, ideally with amount, segment, competitor, and stage lost
• Feedback source — win/loss interview notes, CRM closed_lost_reason fields, survey responses, or call transcripts
• Time window and any segmentation you care about (segment, region, product line)
• Primary competitors to track explicitly
• The decision this feeds — a QBR, a roadmap review, a messaging refresh, an enablement push

If the data is thin, say so and analyze what exists rather than inventing outcomes.

Process

1. Normalize the reasons — collapse free-text loss reasons into a consistent taxonomy (price, product gap, timing/no-decision, competitor, champion left, poor fit, etc.).
2. Quantify — count wins and losses per reason; weight by deal value; compute win rate overall and by cut.
3. Separate controllable from structural — a missing feature is controllable; a genuine no-budget is not. Focus action on the controllable.
4. Pull evidence — attach 1–2 real quotes per major theme. Never fabricate quotes; mark [quote to add] if none is available.
5. Isolate competitor dynamics — where you lose to each competitor and on what basis.
6. Recommend actions — for each top theme, the single highest-leverage move and who owns it.

Output Format

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Win/Loss Analysis — [Period]

Scope: [N won · N lost · total value] · Segments: [list] · Source: [interviews / CRM / survey]

Headline

[2–3 sentences: overall win rate, the biggest swing factor, and the one thing to fix first.]

Why We Win (ranked)

#	Reason	% of wins	Notable in
1	[Reason]	[%]	[segment/competitor]

Evidence: "[buyer quote]"

Why We Lose (ranked)

#	Reason	% of losses	Controllable?	Est. value at stake
1	[Reason]	[%]	Yes/No/Partly	[$]

Evidence: "[buyer quote]"

Win Rate by Cut

Cut	Win rate	Read
[Segment / competitor / deal size]	[%]	[what it means]

Competitive Read

• vs [Competitor]: [where and why we win/lose, and the counter]

Actions

Theme	Recommended action	Owner	Effort	Expected impact
[Theme]	[Specific move]	[Product/PMM/Sales]	S/M/L	[win-rate or deal-value effect]

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Deeper Materials

• references/buyer-interview-craft.md — the interview craft that defeats buyer politeness — who calls, when, and the questions that get truth

Quality Checks

• Every reason is backed by counts, not vibes
• Losses are split into controllable vs structural
• Each major theme has a real quote or an explicit [quote to add]
• Actions name an owner and the highest-leverage single move
• Competitor findings are specific enough to change a battlecard

Anti-Patterns

• Do not treat "price" as a root cause without checking whether it's really value perception
• Do not average away segment differences — a 60% overall win rate can hide a 20% enterprise rate
• Do not fabricate buyer quotes or inflate sample size; state the n
• Do not list 15 actions — rank ruthlessly and name the top few
• Do not blame sales or product reflexively; let the data assign the theme

Example Trigger Phrases

• "Run a win/loss analysis on last quarter's closed deals"
• "Why are we losing enterprise deals to [Competitor]?"
• "Summarize these win/loss interviews into themes and actions"
• "Turn our CRM closed-lost reasons into a report for the QBR"