Version 1

# 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. 2. **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`. 3. **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. 4. **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. 5. **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. 2. **"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. 3. **"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. 4. **"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. 5. **"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. 6. **"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. 7. **"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. 8. **"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 ```bash # 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).