# Win loss reason extraction Human Guide

## What This Is For
Provides expert guidance for guidance for building sales bots that automatically extract and categorize reasons why deals are won or lost. It gives the agent a clearer input/output frame for win loss reason extraction: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Win loss reason extraction agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for win loss reason extraction.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Win loss reason extraction guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Frame the work around win loss reason extraction and ask for the context needed to do it well.
- Turn vague preferences into explicit choices before drafting the final output.
- Ask for a concrete deliverable, not just general advice.

## Decision Points And Nuance
The original skill emphasizes: Why Win/Loss Extraction Matters, The Knowledge Gap, Win Reason Categories, Common Win Reasons, Loss Reason Categories, Common Loss Reasons, Extraction Methods, Conversation Analysis, Pattern-Based Extraction, LLM-Based Extraction.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- "Why did we lose?" "I don't know"
- "keywords": ["missing feature", "doesn't do", "need X that you don't have"],

## Copy-And-Paste Prompt
```text
Use the Win loss reason extraction human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the louisblythe/salesskills skill entry for `win-loss-reason-extraction`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Win/Loss Reason Extraction

You are an expert in building sales bots that automatically extract and categorize reasons why deals are won or lost. Your goal is to help developers create systems that learn from outcomes to improve future performance.

## Why Win/Loss Extraction Matters

### The Knowledge Gap
```
Without extraction:
- "Why did we lose?" "I don't know"
- Same mistakes repeated
- No pattern visibility
- Intuition-based strategy

With extraction:
- Categorized loss reasons
- Trend identification
- Data-driven improvement
- Actionable insights
```

## Win Reason Categories

### Common Win Reasons
```python
WIN_REASONS = {
    "product_fit": {
        "keywords": ["exactly what we need", "perfect fit", "solves our problem"],
        "indicators": ["feature_match_high", "use_case_alignment"]
    },
    "price_value": {
        "keywords": ["fair price", "good value", "worth it", "roi makes sense"],
        "indicators": ["price_accepted", "roi_discussed_positively"]
    },
    "trust_relationship": {
        "keywords": ["trust you", "great to work with", "understood us"],
        "indicators": ["high_engagement", "personal_connection"]
    },
    "competitive_advantage": {
        "keywords": ["better than", "chose you over", "differentiated"],
        "indicators": ["competitor_comparison_won", "unique_feature_mentioned"]
    },
    "timing": {
        "keywords": ["right time", "urgent need", "perfect timing"],
        "indicators": ["fast_sales_cycle", "urgency_expressed"]
    },
    "champion_advocacy": {
        "keywords": ["fought for", "convinced the team", "my recommendation"],
        "indicators": ["strong_champion", "internal_selling"]
    }
}
```

## Loss Reason Categories

### Common Loss Reasons
```python
LOSS_REASONS = {
    "price": {
        "keywords": ["too expensive", "over budget", "cheaper option", "can't afford"],
        "indicators": ["price_objection", "budget_constraints"],
        "severity": "high"
    },
    "timing": {
        "keywords": ["bad timing", "not now", "maybe later", "next year"],
        "indicators": ["timing_objection", "delayed_decision"],
        "severity": "medium"
    },
    "competitor": {
        "keywords": ["went with", "chose", "competitor won", "using [competitor]"],
        "indicators": ["competitor_mentioned", "comparison_lost"],
        "severity": "high"
    },
    "no_decision": {
        "keywords": ["doing nothing", "staying with current", "not a priority"],
        "indicators": ["stalled", "no_urgency"],
        "severity": "medium"
    },
    "feature_gap": {
        "keywords": ["missing feature", "doesn't do", "need X that you don't have"],