liqiongyu

sales-compensation

14
0
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npx skills add liqiongyu/lenny_skills_plus --skill "sales-compensation"

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# Description

Design a sales compensation plan (OTE & pay mix, quotas & ramp, commission mechanics, and retention-aligned incentives) and produce a Sales Comp Plan Pack. Use for AE/SDR/AM comp plans, early sales hires, and GTM scaling. Category: Sales & GTM.

# SKILL.md


name: "sales-compensation"
description: "Design a sales compensation plan (OTE & pay mix, quotas & ramp, commission mechanics, and retention-aligned incentives) and produce a Sales Comp Plan Pack. Use for AE/SDR/AM comp plans, early sales hires, and GTM scaling. Category: Sales & GTM."


Sales Compensation

Scope

Covers
- Designing compensation plans for revenue roles (typically SDR/BDR, AE, AM/CSM with expansion)
- Setting OTE, base/variable mix, quota, and ramp
- Defining commission mechanics (crediting rules, accelerators, clawbacks, payout timing)
- Aligning incentives with long-term customer value (retention / NRR), not just bookings
- Producing rep-facing plan docs + admin rules so payouts are actually operable

When to use
- “Design a comp plan for our first AEs/SDRs.”
- “What should our OTE and base/variable split be?”
- “Set quotas and a ramp plan for new reps.”
- “Create accelerators + rules so reps don’t game discounting.”
- “Our reps close bad-fit deals that churn—align comp with retention/NRR.”

When NOT to use
- You need legal/tax/HR advice, employment compliance guidance, or jurisdiction-specific plan language (use qualified professionals)
- You’re designing executive compensation or equity plans (different problem)
- You don’t yet have basic GTM foundations (ICP, pricing, what counts as a closed-won) — do that first, then return
- You want an overly complex “formula soup” plan that can’t be explained in one page or administered reliably

Inputs

Minimum required
- Company stage + GTM motion (inbound/outbound/PLG/enterprise) and primary sales roles
- What you sell + pricing model + typical ACV/ARR and sales cycle length
- Your economic constraints: gross margin, CAC payback target (or runway), budget for sales comp
- Target outcomes for the period (bookings/revenue/NRR) and the time horizon you care about (e.g., 90-day retention, annual renewals)
- Current baseline (if any): pipeline conversion, win rate, ramp time, churn/NRR
- Constraints: simplicity tolerance, payout timing preference, risk tolerance (for the company and for reps)

Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md, then proceed.
- If key data is missing, make explicit assumptions and include:
- Assumptions & unknowns
- Sensitivity ranges (e.g., quota/rates under low/base/high scenarios)
- Validation plan (what to measure in the next 30–90 days)

Outputs (deliverables)

Produce a Sales Comp Plan Pack in Markdown (in-chat; or as files if requested), in this order:

1) Context snapshot (roles, stage, goals, constraints, time horizon)
2) Comp philosophy (what behaviors you want; what you want to prevent)
3) Role → metric mapping (what gets paid on, and why it’s controllable)
4) OTE + pay mix table (base/variable split by role + rationale)
5) Quota + ramp model (quota by period + ramp schedule + any draw/guarantee)
6) Commission mechanics spec (crediting, rates, accelerators, splits, discount policy, payout timing, clawbacks)
7) Retention-alignment addendum (choose one approach; define measurement + timing)
8) Admin & governance (required CRM fields, payout process, disputes, exceptions, change control)
9) Rep-facing one-pager + FAQ (copy/paste)
10) Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (7 steps)

1) Intake + plan boundaries (what problem are we solving?)

  • Inputs: User context; references/INTAKE.md.
  • Actions: Confirm roles in scope, selling motion, time horizon (bookings vs retention), budget constraints, and “must-not” behaviors (discounting, churny deals, channel conflict). Identify what data you do/don’t have.
  • Outputs: Context snapshot + assumptions/unknowns + decision on time horizon.
  • Checks: Success is measurable (who/what/by when) and the plan scope is explicit.

2) Define role responsibilities + “what gets paid on”

  • Inputs: Role definitions; pipeline stages; revenue recognition basics; retention model.
  • Actions: Choose 1 primary performance metric per role (e.g., ARR bookings, qualified meetings, expansion ARR, gross profit). Define “crediting” rules (when a deal counts, splits, renewals).
  • Outputs: Role → metric mapping + crediting rules draft.
  • Checks: The metric is (a) measurable, (b) attributable, and (c) reasonably controllable by the rep.

3) Set OTE + base/variable mix (pay risk where it belongs)

  • Inputs: Talent market bands (if known), role seniority, sales cycle, role risk, stage.
  • Actions: Set OTE targets and the base/variable mix per role. Choose a default mix (often ~50/50 for many AE roles) and adjust based on cycle length, product maturity, and expected rep autonomy.
  • Outputs: OTE + pay mix table with rationale and guardrails.
  • Checks: OTE is economically viable for the business and believable to candidates; pay mix matches controllability and sales cycle length.

4) Build quota + ramp model (make “on target” realistic)

  • Inputs: Targets; baseline conversion (or assumptions); expected ramp time; territory/segment definitions.
  • Actions: Create a quota model (top-down + bottom-up cross-check). Define ramp schedule, draw/guarantee (if used), and what happens if the plan changes mid-year.
  • Outputs: Quota + ramp tables (low/base/high scenarios).
  • Checks: A rep at OTE can realistically hit quota with the assumed pipeline and conversion.

5) Define commission mechanics (simple, compute-able, enforceable)

  • Inputs: OTE/Quota; metric definitions; discount/margin constraints.
  • Actions: Set rates, accelerators/decelerators, and payout timing. Add guardrails: discount approval thresholds, deal qualification minimums, splits/overlays, clawbacks/chargebacks, and edge-case rules.
  • Outputs: Commission mechanics spec + 2–3 worked payout examples.
  • Checks: A Sales Ops/admin can calculate payouts from CRM data without manual interpretation.

6) Add retention/quality alignment (avoid paying for churn)

  • Inputs: Retention/NRR goals; churn timing; implementation/onboarding reality; data availability.
  • Actions: Choose one retention-alignment approach (e.g., partial holdback until 90 days, commission adjustment on early churn, pay on collected revenue, or NRR multipliers). Define measurement windows and how disputes are handled.
  • Outputs: Retention-alignment addendum (chosen approach + rationale + admin rules).
  • Checks: The approach is understandable to reps and proportional to their influence on retention.

7) Quality gate + finalize (rep-ready + admin-ready)

  • Inputs: Draft pack.
  • Actions: Run references/CHECKLISTS.md and score using references/RUBRIC.md. Produce the rep-facing one-pager + FAQ. Always include Risks / Open questions / Next steps and a 30–90 day validation plan.
  • Outputs: Final Sales Comp Plan Pack.
  • Checks: The plan can be explained in one page, computed from CRM fields, and aligns incentives with business + customer outcomes.

Quality gate (required)

Examples

Example 1 (first AE comp plan, seed-stage SaaS):
“Use sales-compensation. We’re seed-stage B2B SaaS, $12k ACV, 45-day cycle. Hiring first 2 AEs. Goal: $600k ARR this year. Output: a Sales Comp Plan Pack with OTE/pay mix, quotas+ramp, commission mechanics, and a rep-facing FAQ.”

Example 2 (retention-aligned comp, churn problem):
“Use sales-compensation. Reps optimize for bookings and we churn in the first 90 days. We want comp to reflect retention/NRR without being overly complex. Output: a Sales Comp Plan Pack with a retention-alignment addendum and clear admin rules.”

Boundary example:
“Write a legally binding compensation agreement for California employees and tell me what’s compliant.”
Response: explain this skill produces a comp-plan spec and rep-facing materials, but legal/compliance review must be done by qualified counsel.

# Supported AI Coding Agents

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