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
- Normalize the reasons — collapse free-text loss reasons into a consistent taxonomy (price, product gap, timing/no-decision, competitor, champion left, poor fit, etc.).
- Quantify — count wins and losses per reason; weight by deal value; compute win rate overall and by cut.
- Separate controllable from structural — a missing feature is controllable; a genuine no-budget is not. Focus action on the controllable.
- Pull evidence — attach 1–2 real quotes per major theme. Never fabricate quotes; mark
[quote to add] if none is available.
- Isolate competitor dynamics — where you lose to each competitor and on what basis.
- Recommend actions — for each top theme, the single highest-leverage move and who owns it.
Output Format
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] |
Quality Checks
Anti-Patterns
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"
1---2name: win-loss-analysis-23description: Analyze why deals are won and lost and turn it into an action plan. Use when asked to run a win/loss analysis, review closed-won and closed-lost deals, understand why the team is losing to a competitor, or summarize sales feedback into patterns. Produces a structured win/loss report with themes, win/loss rates by segment and competitor, representative quotes, and prioritized actions for product, marketing, and sales.4---56# Win/Loss Analysis Skill78Turn 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.910## What This Skill Produces1112- A win/loss report with the top reasons deals were won and lost, ranked by frequency and deal value13- Win/loss rates cut by segment, deal size, competitor, and source where the data allows14- Representative buyer quotes that make each theme concrete15- A prioritized action list mapped to product, marketing, sales, and pricing owners1617## Required Inputs1819Ask for these if not provided:2021- **Deal data** — a list of closed-won and closed-lost deals, ideally with amount, segment, competitor, and stage lost22- **Feedback source** — win/loss interview notes, CRM `closed_lost_reason` fields, survey responses, or call transcripts23- **Time window and any segmentation** you care about (segment, region, product line)24- **Primary competitors** to track explicitly25- **The decision** this feeds — a QBR, a roadmap review, a messaging refresh, an enablement push2627If the data is thin, say so and analyze what exists rather than inventing outcomes.2829## Process30311. **Normalize the reasons** — collapse free-text loss reasons into a consistent taxonomy (price, product gap, timing/no-decision, competitor, champion left, poor fit, etc.).322. **Quantify** — count wins and losses per reason; weight by deal value; compute win rate overall and by cut.333. **Separate controllable from structural** — a missing feature is controllable; a genuine no-budget is not. Focus action on the controllable.344. **Pull evidence** — attach 1–2 real quotes per major theme. Never fabricate quotes; mark `[quote to add]` if none is available.355. **Isolate competitor dynamics** — where you lose to each competitor and on what basis.366. **Recommend actions** — for each top theme, the single highest-leverage move and who owns it.3738## Output Format3940---4142# Win/Loss Analysis — [Period]4344**Scope:** [N won · N lost · total value] · **Segments:** [list] · **Source:** [interviews / CRM / survey]4546## Headline47[2–3 sentences: overall win rate, the biggest swing factor, and the one thing to fix first.]4849## Why We Win (ranked)50| # | Reason | % of wins | Notable in |51|---|---|---|---|52| 1 | [Reason] | [%] | [segment/competitor] |5354**Evidence:** *"[buyer quote]"*5556## Why We Lose (ranked)57| # | Reason | % of losses | Controllable? | Est. value at stake |58|---|---|---|---|---|59| 1 | [Reason] | [%] | Yes/No/Partly | [$] |6061**Evidence:** *"[buyer quote]"*6263## Win Rate by Cut64| Cut | Win rate | Read |65|---|---|---|66| [Segment / competitor / deal size] | [%] | [what it means] |6768## Competitive Read69- **vs [Competitor]:** [where and why we win/lose, and the counter]7071## Actions72| Theme | Recommended action | Owner | Effort | Expected impact |73|---|---|---|---|---|74| [Theme] | [Specific move] | [Product/PMM/Sales] | S/M/L | [win-rate or deal-value effect] |7576---7778## Quality Checks7980- [ ] Every reason is backed by counts, not vibes81- [ ] Losses are split into controllable vs structural82- [ ] Each major theme has a real quote or an explicit `[quote to add]`83- [ ] Actions name an owner and the highest-leverage single move84- [ ] Competitor findings are specific enough to change a battlecard8586## Anti-Patterns8788- [ ] Do not treat "price" as a root cause without checking whether it's really value perception89- [ ] Do not average away segment differences — a 60% overall win rate can hide a 20% enterprise rate90- [ ] Do not fabricate buyer quotes or inflate sample size; state the n91- [ ] Do not list 15 actions — rank ruthlessly and name the top few92- [ ] Do not blame sales or product reflexively; let the data assign the theme9394## Example Trigger Phrases9596- "Run a win/loss analysis on last quarter's closed deals"97- "Why are we losing enterprise deals to [Competitor]?"98- "Summarize these win/loss interviews into themes and actions"99- "Turn our CRM closed-lost reasons into a report for the QBR"
Mohit Aggarwal