win-loss-analysis
Skill117 starsChanged 4 months agoMITView on GitHub
Win/loss analysis for B2B SaaS. Analyzes sales call transcripts across 6 dimensions (product, messaging, GTM/sales, pricing, competition, customer context) to extract why deals are won, lost, retained, or churned. Produces aggregate patterns with verbatim-quote evidence, frequency counts, confidence levels, and strategic recommendations. Includes process steps, output template, quality checklist, extraction patterns, and a worked 5-transcript example.
We split this skill into the rules it gives an agent
10 rules. 0 are common, 10 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 10 edited versions we read keep these.
10
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 77
Before proceeding: at least one transcript provided.
- Distinctive: line 79
If inputs are missing: ask the user for transcripts.
- Distinctive: line 86
Bind every claim to evidence: Every extracted pattern cites a verbatim quote plus the speaker.
- Distinctive: line 112
Default to batch analysis mode: when multiple transcripts are provided.
- Distinctive: line 156
Quote verbatim: All insights must trace to specific transcript quotes.
- Distinctive: line 157
Never invent patterns: If a pattern appears in only one call, label it "Single mention — pattern unconfirmed."
- Distinctive: line 158
State frequency: Always note how many calls support each finding (e.g., "4 of 7 calls").
- Distinctive: line 159
Acknowledge gaps: If a dimension has no data, mark "Not discussed in transcripts."
- Distinctive: line 169
Single-dimension analysis: Only looks at win/loss by competitor, missing dimensions like deal size, ICP segment, or sales cycle stage → …
- Distinctive: line 203
If you run win/loss inside a connected environment, pull transcripts and deal context fresh:
“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
<!--
COMMUNITY SKILL — included under its original MIT license.
Source: https://github.com/matteotitta/genesys-skills by Matteo Titta (MIT).
License text: ../LICENSE-genesys-skills.txt
Curated into Growth Marketing OS by Mahmoud Omar (mahmoudomar.com) — this file
is NOT original work by the repo author; see /skills/community/README.md.
-->Win/loss analysis
Analyze sales call transcripts to extract actionable insights on why deals are won, lost, retained, or churned. Cross-reference findings with ICP, firmographics, and competitive context to produce strategic recommendations.
Knowledge type: win-loss-analysis
Maturity on first run: emergent → validated after team review
Claude Code triggers
Invoke when user says:
- "Win/loss analysis"
- "Analyze sales calls"
- "Why did we win/lose"
- "Churn analysis"
- "Retention analysis"
- "Sales call insights"
- "Deal outcome patterns"
- "Customer feedback synthesis"
- "Analyze these transcripts"
- "What patterns in our sales calls"
Do NOT invoke when:
- User wants general transcript analysis → use a generic transcript skill
- User wants competitor research → use
competitor-research - User wants a single customer interview write-up → use a transcript skill
- User wants sales enablement assets → use a sales-enablement skill
Input requirements
Required
| Input | Description | Source |
| Transcripts | Sales call transcripts with customer name and outcome | User provides |
| Outcome | Win/Loss/Retention/Churn for each call | User specifies or infer |
Optional (improve quality)
| Input | How it helps |
| Website URL per customer | Firmographics cross-reference |
| Product/ICP document | Define in-scope product capabilities |
| Market/GTM document | Positioning and competitive landscape |
| Sales notes column | Additional context (stage, deal size) |
| Competitor names | Pre-identify competitors to watch for |
Validation
Before proceeding: at least one transcript provided; outcome known or inferable from transcript; customer name identifiable.
If inputs are missing: ask the user for transcripts. Clarify if outcome should be inferred from transcript signals.
Transcript intake — normalize, redact, bind to evidence
Transcripts arrive in many shapes (Gong, Fireflies, Otter, Grain, Zoom/Avoma VTT, SRT, recorder JSON, or plain pasted text). Before Phase 1, normalize whatever you're handed into one shape — speaker-attributed turns with timestamps where present. Two rules apply to every transcript before analysis:
- Redact PII first. Mask end-client names, emails, and account numbers before processing; keep roles, company, and deal context. (Load-bearing for regulated industries.)
- Bind every claim to evidence. Every extracted pattern cites a verbatim quote plus the speaker; normalized, speaker-attributed turns make that attribution reliable.
Process
The analysis runs in 3 phases. Read references/process.md for the full step-by-step (4 transcript-processing steps, 4 aggregation steps, 4 synthesis steps, plus per-phase checkpoints and the process flowchart).
Phase summary:
- Transcript processing — classify outcome, identify speakers, extract customer context, pull verbatim quotes for the 6 dimensions
- Pattern aggregation — group by outcome, count frequency, rank patterns (3+ mentions), cross-reference by ICP/competitor/persona
- Insight synthesis — state pattern, provide evidence with frequency + confidence, identify opportunity, generate executive summary
Core frameworks
Analysis modes
| Mode | When to use | Output |
| Single call | Deep analysis of one transcript | Full insight extraction per dimension |
| Batch analysis | Multiple transcripts (3-20 calls) | Aggregated patterns with frequency counts |
| Comparison matrix | Win vs. loss OR retention vs. churn | Side-by-side pattern comparison |
Default to batch analysis mode when multiple transcripts are provided.
6 analysis dimensions
| # | Dimension | Win signals | Loss signals |
| 1 | Product | "Exactly what we need," feature praised | "Missing [feature]," "Doesn't do [X]" |
| 2 | Messaging | "Now I understand why this matters" | "What does it actually do?" |
| 3 | GTM/Sales | "You really understand our problem" | "Demo didn't address our needs" |
| 4 | Pricing | "Fair price," "good value" | "Too expensive," "over budget" |
| 5 | Competition | "Chose you over [competitor]" | "Going with [competitor]" |
| 6 | Customer context | "Need this now," deadline-driven | "No rush," "maybe next year" |
Confidence scoring
| Level | Definition | When to apply |
| High | 3+ calls with consistent pattern | Clear recurring theme |
| Medium | 2 calls or inferred from strong signals | Emerging pattern |
| Low | Single mention or indirect reference | Possible outlier |
Outcome classification
| Outcome | Definition | Key signals |
| Win | Deal closed, contract signed | "We're moving forward," pricing confirmed |
| Loss | Deal lost to competitor or no-decision | "Going with [competitor]," "Not right now" |
| Retention | Existing customer renewing/expanding | Renewal discussion, expansion |
| Churn | Existing customer leaving/reducing | Cancellation, "not getting value" |
Output
Produce a single win/loss report markdown file. Template + iteration prompts library: references/output-format.md.
Pre-delivery quality checklist + worked example + anti-examples: references/quality.md.
Auto-update protocol (feedback signals, pattern detection, skill-update template): references/auto-update.md.
Anti-hallucination guardrails
- Quote verbatim. All insights must trace to specific transcript quotes.
- Never invent patterns. If a pattern appears in only one call, label it "Single mention — pattern unconfirmed."
- State frequency. Always note how many calls support each finding (e.g., "4 of 7 calls").
- Acknowledge gaps. If a dimension has no data, mark "Not discussed in transcripts."
- Distinguish roles. Tag who said what — prospect vs. sales rep vs. champion.
Gotchas
- Correlation as causation. Reports "deals with longer sales cycles were lost" as if cycle length caused the loss → always distinguish patterns from causes. Use "associated with" not "caused by".
- Small sample bias. Draws conclusions from 2-3 deals instead of waiting for sufficient data → flag sample size prominently. Minimum 5 wins and 5 losses for reliable patterns.
- Missing verbatim quotes. Summarizes what buyers said instead of extracting exact quotes → verbatim quotes are the primary deliverable. Summaries are secondary.
- Single-dimension analysis. Only looks at win/loss by competitor, missing dimensions like deal size, ICP segment, or sales cycle stage → cross-tabulate across at least 3 dimensions.
- Conflates product feedback with sales insights. Mixes "they wanted feature X" with "they didn't trust our team" → separate product gaps from sales execution issues. They feed into different downstream work.
Integration with other skills
| Skill | Relationship | Usage |
| transcript analysis | Related | Use for general transcripts, not sales calls |
| sales enablement | Downstream | Feed insights into battlecards and objection handlers |
| product messaging | Downstream | Update messaging based on win patterns |
| competitor research | Related | Cross-reference competitor mentions |
Reference files
| File | Purpose |
references/process.md | Full 3-phase step-by-step + flowchart |
references/output-format.md | Win/loss report template + iteration prompts |
references/quality.md | Pre-delivery checklist + worked example + anti-examples |
references/auto-update.md | Feedback signal detection + pattern rules |
references/extraction-patterns.md | Signal patterns for each dimension |
references/output-template.md | Legacy report template (kept for reference) |
references/example-analysis.md | Worked example with 5 transcripts |
Data integration
Level: 0 — Context (heavy pulls)
If you run win/loss inside a connected environment, pull transcripts and deal context fresh:
| Source | What to pull | When |
| Meeting recorder (Granola, Gong, Fireflies, etc.) | Sales call transcripts and deal discussions | Always |
| Team chat (Slack, etc.) | Deal discussion threads and competitive intel | Always |
Fallback (no integrations): user-provided call transcripts or recordings; manual deal review notes.
Changelog
| Version | Date | Changes |
| 2.0 | 2026-01-16 | Refactored to v2.0 template: structured phases, evidence-ready insights, iteration prompts, auto-update rules |
| 1.0 | Previous | Initial skill creation with 6 dimensions |
Other versions10 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- matteotitta/genesys-skills★ 4661% same
Wrote their own version · 9
- jbalbu01/sales-enablement-plugin★ 15same name
- whyashthakker/agent-skills-marketing★ 15same name
- prosyo-ai/gtm-skill★ 13same name
- the-nam-shub/e5-real-skills★ 10same name
- nandanosql/all_in_one_pm_skills★ 1same name
- sidchaudhary/gtm-skills★ 1same name
- TahaSheikh12/ai-native-product-marketing-workspace★ 1same name
- Insightful-Pipe/marketing-strategy-skills★ 0same name
- poorvith-mp/skills-sales-support★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository19 other files it gives its agents.
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- onboardingSkill
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- experiment-coachSkill
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- mena-adsSkill
- performance-media-buyerSkill
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