commercial-policy

commercial-policy

Purpose

Design the rules of engagement that govern discounting off list price — the artifact that Deal Desk and AEs operate under. Three deterministic tools:

1. discount_matrix_builder.py — builds a 4-dimensional matrix (ARR band × term length × payment terms × strategic value tier), each cell carrying an approved discount band backed by current win-rate + NRR data, plus an approver tier (AE / Manager / Director / VP / CFO).
2. exception_router.py — when an asks-for-discount lands outside the matrix, routes it through the named approver chain, attaches required compensating commitments (multi-year prepay + named expansion path + reference commitment + MSA tightening), produces machine-readable audit-trail metadata, and flags precedent risk if 3+ similar exceptions have landed in the trailing quarter.
3. policy_linter.py — lints the matrix for governance defects: approver inversion, band inversion, margin-floor violation, coverage gaps, cliff edges, undefined strategic tiers, inconsistent margin floors, thin data backing.

The output is the policy itself (matrix + exception flow + lint report), not a per-deal application of it.

When to use

• A new Head of Commercial or Head of Deal Desk is writing the company's first formal commercial policy
• The existing matrix is older than 6 months and discount drift is showing in margin reviews
• Reps are citing "Maria approved 28% on Acme last quarter" as precedent and you need to break the precedent loop
• Q-over-Q exception count is rising and you suspect the matrix bands are mispriced
• CFO has tightened the margin floor and the matrix needs to be rebuilt against the new constraint
• A board / exec is asking "why do we discount this much?" and you need a data-backed defensible policy

Do NOT use this skill to:

• Approve a specific deal — that's commercial/skills/deal-desk
• Set the pricing model + list price — that's commercial/skills/pricing-strategist
• Author a proposal / SOW / MSA prose — that's business-growth/contract-and-proposal-writer
• Make the strategic "when do we hire a VP Sales" call — that's c-level-advisor/cro-advisor

Workflow

1. Audit current discount distribution. Pull the last 4 quarters of closed-won + closed-lost deals from CRM. Fill assets/policy_design_template.md (~20 minutes). Capture: arr, discount_pct, term_months, payment_terms_days, strategic_value, win_lost, nrr_12mo per deal.

1. Design the data-backed matrix. Run scripts/discount_matrix_builder.py --input policy_intake.json --profile {saas|enterprise-software|api|marketplace|services}. Output is a 4-dimensional matrix with approved discount band + approver tier + margin floor + observed win-rate + observed NRR per cell. Cells with n < 5 observed deals are flagged THIN.

1. Design the exception flow. Run scripts/exception_router.py --sample to see the structure. For each severity band of exception (0-5 pts over, 5-10, 10-20, 20+), the router enforces required compensating commitments. Codify the flow in your policy doc; the router becomes the operational implementation.

1. Lint the matrix. Run scripts/policy_linter.py --input matrix.json. Get a ranked findings report — BLOCKER / MAJOR / MINOR — across 10 lint rules. Resolve every BLOCKER before publishing the matrix to AEs.

1. Publish + quarterly review. Publish the matrix as a versioned artifact. Re-run the builder and the linter every quarter against the new 4-quarter rolling deal corpus. Cells where observed NRR < target_nrr are flagged for review.

Scripts

| Script | Purpose | Industry profiles |

|---|---|---|

| scripts/discount_matrix_builder.py | 4-dim data-backed matrix with approver tiers + margin floors | saas, enterprise-software, api, marketplace, services |

| scripts/exception_router.py | Routes exception requests with compensating commitments + audit trail | n/a (matrix-driven) |

| scripts/policy_linter.py | 10-rule lint pass over the matrix | n/a (deterministic across profiles) |

All three: stdlib-only, --help, --sample, --input <json>, --output {markdown,json}.

References

• references/discount_governance_canon.md — Discount governance evidence base: OpenView Partners benchmarks, David Skok (For Entrepreneurs) discount math, Tomasz Tunguz on discount distribution, Bessemer State of the Cloud, KeyBanc Capital Markets SaaS Survey, Bridge Group AE-compensation research, RevOps Co-op playbooks, Forrester deal-desk research. 8 sources.
• references/policy_design_canon.md — Policy-as-artifact design: SaaStr (Jason Lemkin), Winning by Design (Jacco van der Kooij) on commercial discipline, Forrester deal-desk maturity research, MIT Sloan on incentive-system gaming, McKinsey on commercial-policy effectiveness, Bain Pricing Power, Salesforce CPQ implementation guides. 7 sources.
• references/policy_anti_patterns.md — 8 named anti-patterns with sourced studies + countermeasures + lint-rule mapping: precedent-sets-policy, no-data-backing, no-compensating-commitments, approver/margin misalignment, no audit trail, cliff edges, undefined "strategic value", no quarterly review. 8 sources.

Assumptions

• The skill assumes the pricing model and list price already exist (set via commercial/skills/pricing-strategist). Commercial-policy governs discounts off list — it does not set list.
• The CFO owns the min_margin_pct constraint (margin floor). The CRO / Head of Deal Desk owns the max_discount_pct_without_exception constraint (band cap). The skill keeps these inputs separate by design (per Bain Pricing Power — mixing accountability is the most common cause of policy drift).
• Industry profiles bake in customary band widths. Companies with idiosyncratic economics should pass overrides via the input JSON.
• The matrix is data-backed but not data-driven: the band is set by the constraints + profile; observed data is annotation that tells you whether the cell is performing. If observed NRR < target, that's a signal to review the band, not to keep discounting deeper.
• "Strategic value" tiers (logo, expansion, lighthouse) are useful only if defined with concrete tests. The lint rule L06 enforces this.
• This is a policy-design skill, not a deal-approval skill. It never says "approve" — it produces the matrix + exception flow that deal-desk then applies.

Anti-patterns

• Setting discount bands without data backing. "VP Sales argued for it in a Slack thread" is not data backing. If you can't show win-rate and NRR for the band, the band is rhetoric. (Caught by data_backing per cell + lint L08.)
• Letting precedent set policy. "Maria approved 28% on Acme last quarter" is not a band — it's an exception that didn't break the policy. exception_router.py flags 3+ similar exceptions as a signal that the matrix is wrong, not the deal. (Anti-pattern AP-1.)
• Approving exceptions without compensating commitments. Discount-for-nothing is a leak (Winning by Design). Every exception severity band requires non-negotiable commitments. (exception_router.COMPENSATING_LIBRARY.)
• Cliff edges at round-number ARR thresholds. A hard $100K threshold produces deal-size gaming within 2 quarters (MIT Sloan agency theory). Smooth the gradient. (Lint L05.)
• "Strategic value" as an undefined catch-all. If "strategic" is undefined, within a quarter 60% of deals will be flagged strategic and the matrix is dead. Define with concrete tests. (Lint L06.)
• No quarterly review. Markets shift; matrices unchanged for 12 months are mispriced. Re-run the builder and linter every quarter. (Anti-pattern AP-8.)
• Mixing CFO and CRO accountabilities. CFO owns the margin floor; CRO owns the band cap. Same accountable owner = predictable drift toward whatever they're compensated on (Bain Pricing Power).
• Skipping the lint pass before publishing. BLOCKER findings (approver inversion, margin-floor violation, inverted bands) make the policy unsignable. Lint is the gate, not the after-action review.

Distinct from

| Sibling | Scope | Difference |

|---|---|---|

| commercial/skills/deal-desk | Applies the policy to one deal at a time | Commercial-policy designs the policy itself. Deal-desk consumes the matrix; commercial-policy produces it. |

| commercial/skills/pricing-strategist | Sets pricing model (per-seat / usage / value / tiered) + list price | Commercial-policy governs discounts off list. Pricing-strategist sets the menu; commercial-policy governs the menu's discount discipline. |

| c-level-advisor/cro-advisor | Strategic CRO judgment ("when do we hire VP Sales?", "is our motion product-led or sales-led?") | Strategic, not operational. Commercial-policy is the artifact CRO commissions; it isn't CRO judgment itself. |

| c-level-advisor/cfo-advisor | Margin floor + unit-economics judgment | The CFO supplies min_margin_pct to commercial-policy as an input. Commercial-policy operationalizes the CFO's constraint as per-cell margin floors. |

| business-growth/contract-and-proposal-writer | Authors proposal/SOW/MSA prose | Commercial-policy emits structured matrix + audit-trail JSON, not customer-facing prose. |

Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by /cs:grill-commercial or the Commercial orchestrator before the skill runs. Recommended answer + canon citation per question. Never bundled.

1. "What's your observed discount distribution across the last 4 quarters — and is the median inside or outside your current matrix?"

Recommended: pull the corpus before designing any band. If the observed median is outside the matrix, the matrix is rhetoric.

Canon: OpenView SaaS Benchmarks; RevOps Co-op playbooks. Anti-pattern AP-2.

1. "What's the win-rate AND the 12-month NRR for deals at your current 'max discount' band?"

Recommended: both, not one. A band with high win-rate but low NRR is buying logos with leaky-bucket retention. Tunguz benchmarks: top-NRR-quartile companies discount 6 pts less than bottom quartile.

Canon: Tomasz Tunguz; Bessemer State of the Cloud.

1. "Who at the company owns the margin floor, AND who owns the discount-band cap — are those the same person?"

Recommended: CFO owns floor; CRO/Head of Deal Desk owns cap. Same owner = drift toward what they're compensated on.

Canon: Bain Pricing Power — separation of accountability is the structural fix. Anti-pattern AP-4.

1. "How is 'strategic value' defined in your current policy — with concrete tests, or with adjectives?"

Recommended: concrete tests. "Top-20 named account in 2026 target list" is a test; "important customer" is not.

Canon: SaaStr (Lemkin); Forrester deal-desk research. Lint rule L06. Anti-pattern AP-7.

1. "For exceptions above your matrix max, what compensating commitments are required — and are they in writing before the approver signs?"

Recommended: minimum multi-year prepay + named expansion path; deeper exceptions require reference commitment + MSA tightening + executive sponsor.

Canon: Winning by Design (van der Kooij); McKinsey B2B pricing studies. Anti-pattern AP-3.

1. "Has the same kind of exception been approved 3+ times in the trailing quarter — and if so, is the matrix wrong?"

Recommended: 3+ similar exceptions means the band is mispriced. Rebuild the matrix; don't keep approving exceptions.

Canon: OpenView discount drift studies; exception_router._precedent_risk. Anti-pattern AP-1.

1. "When was the last time you re-ran the matrix against the previous 4 quarters of data?"

Recommended: quarterly. Annual review is too slow; the disciplined cohort revises quarterly.

Canon: OpenView benchmarks; RevOps Co-op. Anti-pattern AP-8.

1. "For every exception in the last quarter, is there a machine-readable audit-trail record — or is the approval in Slack and email?"

Recommended: structured record in CPQ or equivalent. Slack/email approvals don't survive year-2 renewal negotiations.

Canon: Salesforce CPQ best practices; Forrester deal-desk maturity research. Anti-pattern AP-5.

Walk depth-first. Lock 1-4 before opening 5-8. After all 8 are answered, invoke discount_matrix_builder.py → policy_linter.py → exception_router.py --sample in sequence to produce the policy artifact.

Quick examples

# Design the matrix
python3 scripts/discount_matrix_builder.py --sample
python3 scripts/discount_matrix_builder.py --input policy_intake.json --profile saas --output json > matrix.json

# Lint the matrix
python3 scripts/policy_linter.py --sample
python3 scripts/policy_linter.py --input matrix.json

# Walk the exception flow
python3 scripts/exception_router.py --sample
python3 scripts/exception_router.py --input request.json --output json

The sample matrix lints to FAIL with 4 BLOCKERs + 6 MAJORs + 2 MINORs — by design, to exercise every rule path. A real policy intake should lint to PASS or PASS_WITH_WARNINGS. The sample exception (42% on a $320K logo deal) routes to AE → Sales Manager → Director → VP Sales with 3 required compensating commitments (multi-year 36mo, prepay, named expansion path).