---
name: win-loss-analysis
description: Analyses closed-won and closed-lost opportunities to reveal repeatable patterns by segment, source, size, competitor, stage, reason, and sales motion. Use for win-loss reviews, sales performance analysis, loss-reason coding, revenue insights, or quarterly sales planning.
license: Apache-2.0
metadata:
  adlass.categories: "sales-crm/pipeline, sales-crm/competitive-intelligence"
  adlass.industries: ""
  adlass.tags: "win-loss, sales-analysis, loss-reasons, competition, revenue, patterns"
  adlass.adaptation: "mapping"
  adlass.source: "original"
  adlass.version: "1"
---

# Win-loss analysis

## Purpose

Convert closed-opportunity records into reliable patterns that improve sales, marketing, product, and pricing decisions. Preserve the difference between recorded reasons and evidence-supported explanations.

## Scope

Outcome, segment, source, size, cycle length, stage exit, competitor, reason, notes, product, price, process, and seller attributes.

**Excluded:** blaming individuals, changing historical records, and generalising from unsupported anecdotes.

## Data basis

- Closed opportunity table and reason taxonomy.
- Call notes, surveys, and competitor fields in scope.
- Historical periods for comparison.

## Result

A coded opportunity sheet, pattern report, and action recommendations.

## Quality criteria

- Population and exclusions are stated.
- Every coded reason traces to source evidence or is labelled recorded-only.
- Win and loss cohorts use comparable definitions.
- Small samples are flagged.
- Recommendations name the pattern and affected segment.

## Instructions

Use the configured reason taxonomy before adding new categories. Separate primary and contributing reasons when evidence supports both. Report counts and rates with denominators. Do not infer causation from correlation. Call out missing outcome interviews, inconsistent coding, and periods that cannot be compared.

## Adapt before use
Keep a clear audit trail throughout the run. Use the source wording and field values that support each material conclusion, and retain uncertainty when the evidence does not decide the issue. Separate a missing record from a negative result and a contradictory record from an exception. Prefer a short, prioritised result over unsupported completeness. Check that counts, identifiers, dates, and labels agree across the written result and the structured output. When a rule, source, or mapping is unavailable, name the limitation and explain how it affects interpretation. Do not silently infer ownership, approval, timing, or business intent.

- Map opportunity and outcome fields.
- Define reason taxonomy, segment dimensions, and minimum sample rule.
- Set comparison periods and recommendation owners.