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Win Loss Analysis

Community
growthack88
win-loss-analysis

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.

Overview

Publishergrowthack88
Repositorygrowth-marketing-os
Skill namewin-loss-analysis
Stars
125
Forks
25
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by growthack88 on GitHub. Read the source before you install it.

Installation

Install the Win Loss Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/growthack88/growth-marketing-os/tree/main/skills/community/win-loss
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/growthack88/growth-marketing-os.git /tmp/growth-marketing-os
mkdir -p .claude/skills
cp -r /tmp/growth-marketing-os/skills/community/win-loss .claude/skills/win-loss-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Win Loss Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Win Loss Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Win Loss Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

InputDescriptionSource
TranscriptsSales call transcripts with customer name and outcomeUser provides
OutcomeWin/Loss/Retention/Churn for each callUser specifies or infer

Optional (improve quality)

InputHow it helps
Website URL per customerFirmographics cross-reference
Product/ICP documentDefine in-scope product capabilities
Market/GTM documentPositioning and competitive landscape
Sales notes columnAdditional context (stage, deal size)
Competitor namesPre-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:

  1. Transcript processing — classify outcome, identify speakers, extract customer context, pull verbatim quotes for the 6 dimensions
  2. Pattern aggregation — group by outcome, count frequency, rank patterns (3+ mentions), cross-reference by ICP/competitor/persona
  3. Insight synthesis — state pattern, provide evidence with frequency + confidence, identify opportunity, generate executive summary

Core frameworks

Analysis modes

ModeWhen to useOutput
Single callDeep analysis of one transcriptFull insight extraction per dimension
Batch analysisMultiple transcripts (3-20 calls)Aggregated patterns with frequency counts
Comparison matrixWin vs. loss OR retention vs. churnSide-by-side pattern comparison

Default to batch analysis mode when multiple transcripts are provided.

6 analysis dimensions

#DimensionWin signalsLoss signals
1Product"Exactly what we need," feature praised"Missing [feature]," "Doesn't do [X]"
2Messaging"Now I understand why this matters""What does it actually do?"
3GTM/Sales"You really understand our problem""Demo didn't address our needs"
4Pricing"Fair price," "good value""Too expensive," "over budget"
5Competition"Chose you over [competitor]""Going with [competitor]"
6Customer context"Need this now," deadline-driven"No rush," "maybe next year"

Confidence scoring

LevelDefinitionWhen to apply
High3+ calls with consistent patternClear recurring theme
Medium2 calls or inferred from strong signalsEmerging pattern
LowSingle mention or indirect referencePossible outlier

Outcome classification

OutcomeDefinitionKey signals
WinDeal closed, contract signed"We're moving forward," pricing confirmed
LossDeal lost to competitor or no-decision"Going with [competitor]," "Not right now"
RetentionExisting customer renewing/expandingRenewal discussion, expansion
ChurnExisting customer leaving/reducingCancellation, "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

  1. Quote verbatim. All insights must trace to specific transcript quotes.
  2. Never invent patterns. If a pattern appears in only one call, label it "Single mention — pattern unconfirmed."
  3. State frequency. Always note how many calls support each finding (e.g., "4 of 7 calls").
  4. Acknowledge gaps. If a dimension has no data, mark "Not discussed in transcripts."
  5. 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

SkillRelationshipUsage
transcript analysisRelatedUse for general transcripts, not sales calls
sales enablementDownstreamFeed insights into battlecards and objection handlers
product messagingDownstreamUpdate messaging based on win patterns
competitor researchRelatedCross-reference competitor mentions

Reference files

FilePurpose
references/process.mdFull 3-phase step-by-step + flowchart
references/output-format.mdWin/loss report template + iteration prompts
references/quality.mdPre-delivery checklist + worked example + anti-examples
references/auto-update.mdFeedback signal detection + pattern rules
references/extraction-patterns.mdSignal patterns for each dimension
references/output-template.mdLegacy report template (kept for reference)
references/example-analysis.mdWorked 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:

SourceWhat to pullWhen
Meeting recorder (Granola, Gong, Fireflies, etc.)Sales call transcripts and deal discussionsAlways
Team chat (Slack, etc.)Deal discussion threads and competitive intelAlways

Fallback (no integrations): user-provided call transcripts or recordings; manual deal review notes.


Changelog

VersionDateChanges
2.02026-01-16Refactored to v2.0 template: structured phases, evidence-ready insights, iteration prompts, auto-update rules
1.0PreviousInitial skill creation with 6 dimensions

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Win Loss Analysis AI skill do?

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.

Why use Win Loss Analysis on TypingMind?

Because you install it once and use it with any model. Win Loss Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Win Loss Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/growthack88/growth-marketing-os/tree/main/skills/community/win-loss. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Win Loss Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Win Loss Analysis?

As many as you like. As long as a model supports skills, you can use Win Loss Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Win Loss Analysis AI skill free?

Yes. It is published on GitHub by growthack88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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