---
name: okhp3-outcome-modeling-sales
description: >
  Forecast pipeline and compare salespeople, territories, accounts, and
  commercial allocations using expected contribution rather than close rate
  alone. Use when evaluating stage probability, quota, discount, retention,
  margin, customer lifetime value, or opportunity quality. Load
  `okhp3-outcome-modeling-core` first.
license: MIT
compatibility: >
  Requires user-supplied or approved CRM, finance, or customer data. Do not
  infer private customer facts or connect to a CRM without user authorization.
metadata:
  author: "Jamie Hill (OverKill Hill P³)"
  version: "1.1.0"
  category: "universal"
  origin: "okhp3/skillz"
  homepage: "https://overkillhill.com"
  author-github: "https://github.com/OKHP3"
  in_scope: "Pipeline, account, salesperson, territory, and commercial-value modeling; Margin-aware forecasting and constrained sales or resource allocation; Selection bias, opportunity quality, retention, and incremental lift checks"
  out_of_scope: "Inventing CRM, customer, quota, or pricing data; Ranking people without opportunity and territory context; Sending outreach, changing CRM records, or making employment decisions"
  status: enhanced-computational-payload
  tags: sales forecasting, pipeline, contribution margin, win rate, selection bias, allocation, retention
  triggers: close rate, quota forecast, pipeline, salesperson comparison, discount, margin, CAC, LTV, territory
  inputs: dated opportunity records, assignment context, probabilities, revenue, margin, cost, capacity, and baseline
  outputs: calibrated pipeline forecast, economic comparison, bias assessment, constrained allocation, and experiment plan
  runtimes: Portable prose by default; optional Python 3.9+ standard library helper for local JSON arithmetic
---

# okhp3-outcome-modeling-sales

**OverKill Hill P³** · [overkillhill.com](https://overkillhill.com) · [github.com/OKHP3](https://github.com/OKHP3)

Apply outcome modeling to sales and business decisions where raw wins can hide discounting, easy-opportunity selection, churn, service cost, or money left on the table. The primary object is economically efficient contribution, not a flattering headline metric such as close rate.

---

## Scope

| In scope | Out of scope |
|----------|-------------|
| Pipeline and revenue forecasts | Fabricated CRM or customer information |
| Margin, retention, expansion, and capacity-aware decisions | Personnel decisions based on a single metric |
| Rep, account, territory, and opportunity comparisons | Unauthorized outreach or CRM writes |

---

## Reframe the sales outcome

Do not treat a 100% close rate as proof of superior performance. First examine opportunity assignment, customer fit, deal size, competition, discounts, cycle time, retention, expansion, service burden, and capacity consumed.

Use an economic objective such as:

```text
expected contribution margin
- discount cost
- acquisition and service cost
- time and capacity cost
+ retention and expansion value
+ incremental lift above opportunity baseline
```

The exact objective must be agreed before ranking salespeople or allocating resources.

## Computational payload

Read `references/computational-model.md` for expected contribution, incremental
lift, and constrained allocation formulas. Read `references/glossary.md` before
using sales abbreviations. Reproduce the synthetic allocation with
`scripts/calculate-sales-allocation.py examples/sales-example.json`. The helper
uses local JSON, exhaustive search for small fixtures, and no CRM connection or
file writes.

## Workflow

1. Load `okhp3-outcome-modeling-core` and define the decision horizon, owner, and target.
2. Identify the opportunity, account, rep, territory, product, and time grains.
3. Build pipeline or account states using only information available at each forecast date.
4. Separate assigned opportunity quality from rep actions and outcomes.
5. Normalize revenue, margin, discount, cycle time, retention, and expansion measures.
6. Compare raw conversion with expected value, contribution, and incremental lift.
7. Check selection bias, territory effects, customer segment, competitive intensity, and reverse causation.
8. Use experiments or holdouts when the recommendation is an intervention such as discounting, routing, or outreach.
9. Allocate under capacity, budget, service, and risk constraints.

## Validation gates

Before ranking people or accounts, verify the as-of boundary, opportunity-assignment fields, margin definitions, and retention window. Reject a recommendation when the economic objective or causal comparison is undefined.

## Output contract

Return:

- target and forecast horizon;
- data quality and assignment boundary;
- pipeline or account state definition;
- raw outcome versus economic-value comparison;
- margin and discount analysis;
- selection-bias and confounding assessment;
- calibrated forecast or ranking;
- recommended allocation with constraints;
- retention, expansion, and downside risks;
- next measurement or experiment.

Never punish a salesperson for a low close rate until opportunity difficulty and assignment quality are modeled. Never reward a high close rate without testing whether price, fit, volume, margin, and retention justify it.

## References

- `references/computational-model.md` -- sales equations and example.
- `references/glossary.md` -- sales terms and abbreviations.
- `examples/sales-example.json` -- synthetic opportunity fixture.
- `scripts/calculate-sales-allocation.py` -- transparent small-allocation helper.
- `../okhp3-outcome-modeling-core/SKILL.md` -- shared objective, state, and validation contract.

## About

Built by [Jamie Hill](https://overkillhill.com) · [OverKill Hill P³](https://overkillhill.com)
Published at [github.com/OKHP3](https://github.com/OKHP3)
Part of the [OKHP3/skillz](https://github.com/OKHP3/skillz) Agent Skill library.
MIT License -- free to use, fork, and adapt. A nod to the source is appreciated.