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Marketing · Version 1.1.0 · Reviewed 2026-08-02

Win-Loss Analysis Designer

Make interview design and bias control with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Designs win-loss programs that surface decision criteria rather than post-hoc justification.

₹99 one-time

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What this skill helps you do

  • Interview design
  • Bias control
  • Pattern synthesis

How Win-Loss Analysis Designer works

You provide

Draft assets, audience evidence, and the claim under review

It inspects

Substantiation and differentiation for interview design

It decides

A bias control revision with claims tied to evidence

You verify

A measurement plan defined before the asset ships

What it checks first

Win-Loss Analysis Designer designs win-loss programs that surface decision criteria rather than post-hoc justification. Use it when the work involves Interview design, Bias control, Pattern synthesis.

  1. Whether a claim is substantiated by evidence a buyer could independently verify.
  2. Whether the differentiation is real or something every competitor also asserts.
  3. Whether the message describes the customer problem or the product feature list.
  4. Sample quality behind any audience insight, since a few loud accounts are not a segment.

Failure modes it recognizes

  • Positioning built on claims competitors make identically, which produces no differentiation at all.
  • A feature list presented as a value proposition, forcing the buyer to do the translation.
  • Optimizing one funnel stage in isolation and moving the bottleneck without net gain.
  • Attributing conversion to messaging when the segment already arrived with intent.

Answers it will reject

  • Superlatives without substantiation, which reduce credibility rather than increase it.
  • Reporting aggregate conversion across mixed segments, hiding that one is failing badly.
  • Copying competitor language, which concedes their framing of the category.

Decision rules it applies

  • Keep only claims that are true, specific, and hard for a competitor to assert.
  • State the evidence and its strength behind every audience assertion.
  • Define the measurement and review date before the asset ships.

Evidence it asks for

  • Quote the source evidence beside each claim so it can be audited.
  • Report counts with denominators rather than percentages alone.
  • Define the leading indicator that will move before the lagging metric does.

The method inside

  1. Start from audience evidence and the behavior to change
  2. Map each message to a problem, outcome, and proof
  3. Remove claims that are vague, duplicated, or unsupported
  4. Define the measurement and approval criteria before publication

Deliverables

  • Interview design evidence map
  • Bias control revision brief
  • Pattern synthesis measurement checklist

Evidence requirements

  • Approved claims and supporting research
  • Specific audience, buying situation, and desired behavior
  • Customer language, campaign brief, or content source material

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Our win-loss interviews are done by the rep who owned the deal and everything comes back as price.

Expected output

The rep who owned the deal cannot get an honest answer about why they lost, which is why every result collapses to price. Use a neutral interviewer, talk to the evaluator rather than only the champion, and ask about the criteria before asking about you...

Boundaries and compatibility

Ideal for

  • Interview design: produce a decision or artifact grounded in supplied evidence.
  • Bias control: produce a decision or artifact grounded in supplied evidence.
  • Pattern synthesis: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Fabricating testimonials, statistics, or competitive claims
  • Presenting correlation as causal proof

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.