agentleFS
mia-pppmia-ppp/PMM-Skills/skills/win-loss-intelligence/SKILL.md

win-loss-intelligence

Skill2 starsChanged 27 days agoMITView on GitHub

When the user wants to understand why opportunities are won or lost, analyze closed-won or closed-lost evidence, or find segment and competitor patterns. Use for win/loss synthesis and route resulting decisions to strategy owners; for sales collateral, see sales-enablement, and for research design, see customer-research.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When receiving a context-establishment handoff, read ../_shared/context-establishment.md.

    line 16
  2. Distinctive:

    Use decision-specific evidence readiness.

    line 21
  3. Very specific:

    Read ../_shared/evidence-gaps.md, ../_shared/ssot-consumption.md, and ../_shared/segment-selection.md when comparing segments.

    line 28
  4. Very specific:

    Use applicable SSOT as a comparison point, never as a file to edit.

    line 30
  5. Distinctive:

    Never invent reasons, quotes, counts, rates, outcomes, or competitor claims.

    line 77
  6. Distinctive:

    Do not imply causality from association or attribute a win/loss to one factor when evidence supports multiple conditions.

    line 81

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Win/Loss Intelligence

Explain why the company wins or loses, for whom, against what alternatives, under which conditions, and what the evidence suggests should be reviewed. Produce evidence and implications, not revised positioning or sales copy.

Context research handoffs

When receiving a context-establishment handoff, read ../_shared/context-establishment.md. Reuse its question, scope, source/finding IDs, approved context/version, GAP IDs and completion criteria. Analyze only your owned domain; return provenance-bearing findings, opposing evidence, limitations, completion status and unapproved implications to product-marketing-context. Use decision-specific evidence readiness; approval is separate. A hold affects only its decision. Do not write canon or automatically save chained results into bootstrap context. Standalone artifacts retain their existing format and may attach the shared return. Preserve IDs and status without duplicating studies.

Before analysis

  • Read ../_shared/evidence-gaps.md, ../_shared/ssot-consumption.md, and ../_shared/segment-selection.md when comparing segments.
  • Use applicable SSOT as a comparison point, never as a file to edit. Record relevant canon version/files and route contradictions through ssot-context-loop for human review.
  • Inventory available closed-won/lost interviews, CRM reason codes, calls, AE notes, opportunity data, competitor evidence, and research. Record date range, inclusion rules, counts, missingness, and known selection bias.
  • Distinguish buyer-reported reason, seller-reported reason, directly observed evidence, and inferred hypothesis. CRM loss codes are seller-entered signals, not buyer truth.

Analysis

  1. Define the question, cohort, and denominator. Separate closed outcomes from open or disqualified opportunities.
  2. Code supported themes across segment, persona, company size, use case, competitor/alternative, deal size, funnel stage, geography, capability, price, implementation, integration, security/compliance, procurement, timing, and status quo. Leave unsupported dimensions unscored.
  3. Separate frequency, severity/materiality, and confidence. Show counts and denominators where available. A small or biased sample cannot establish a rate or broad pattern.
  4. Identify win/loss drivers, competitor and segment patterns, product, positioning, messaging, proof, pricing/packaging, sales-process issues, false assumptions, and unresolved questions only where evidence supports them. Do not classify every loss as competitive.
  5. Include representative source-linked evidence. Use verbatim quotes only when present in actual source material. Preserve conflicting reports.
  6. Route implications to the owner. Do not automatically rewrite any artifact.

Required output

  1. Inputs and assumptions, using shared evidence labels.
  2. Executive summary and analysis question.
  3. Evidence base: sample, outcome counts, period, sources, exclusions, bias, and limitations.
  4. Win themes and loss themes, each with frequency, severity, confidence, and representative evidence where available.
  5. Supported breakdowns by relevant segment/persona/alternative/stage.
  6. Implications and handoffs, with unresolved questions and ranked evidence gaps. Suggested routes: Voice of Customer to customer-research, recurring objections to objection-intelligence, competitive evidence to competitor-profiling, positioning to positioning-strategy, messaging to messaging-framework, proof gaps to the evidence owner, price to pricing-strategy, and sales process to sales-enablement or revops.

Safety and quality checks

  • Never invent reasons, quotes, counts, rates, outcomes, or competitor claims.
  • Mark missing buyer evidence as Gap; never promote seller interpretation to customer truth. Keep Sourced / Assumed / Gap labels; identify derivations as interpretation with formulas/source inputs, not a fourth label.
  • Do not imply causality from association or attribute a win/loss to one factor when evidence supports multiple conditions.
  • New findings may request SSOT review. Only ssot-context-loop manages canonical changes and required human approval.
  • Internal analysis normally does not go to messaging-consistency-audit.
  • Output quality: output-quality-check can verify cohort/denominators, source/report type, frequency/severity/confidence separation, quotes, limitations, supported breakdowns, explicit handoffs, and no silent canon change.

Related skills

customer-research (research design and VoC), objection-intelligence, competitor-profiling, positioning-strategy, messaging-framework, pricing-strategy, sales-enablement, revops, ssot-context-loop, output-quality-check.

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