/channel-economics

name: channel-economics
description: "Use for a direct vs partner channel review: fully loaded cost to serve, channel ROI and the best channel mix under constraints. Runs cost_to_serve_calculator.py, channel_roi_analyzer.py and channel_mix_optimizer.py."
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  tags: [commercial, channel-economics, cost-to-serve, channel-mix, channel-roi, direct-vs-partner, unit-economics]
  compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]

channel-economics

Purpose

Help Head of Commercial / RevOps / VP Sales answer three questions at the quarterly channel review:

1. **What does each channel actually cost to serve, fully loaded?** (direct headcount, channel manager attribution, partner discount, MDF, enablement time, support load, allocated overhead) 2. **What is the ROI of each channel under three lenses?** (cash ROI year-1, LTV-adjusted ROI, marginal ROI — next dollar of investment) 3. **What is the optimal channel mix subject to our strategic constraints?** (minimum direct floor, maximum partner concentration ceiling, sensitivity to CAC shifts)

The skill emits **per-channel verdicts** (DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT), a **sensitivity-tested mix recommendation**, and **the diminishing-returns inflection point**. It does not pick the strategy — humans do, with the numbers loaded honestly for the first time.

When to use

• Quarterly channel review: pipeline is 60/40 or 50/50 direct vs partner and you don't actually know which one is profitable
• Considering hiring a channel manager — need to know if the channel can clear the loaded-cost bar
• Partner program ROI question from the board ("we spent $X on MDF — what did we get?")
• A segment is over-indexed to one channel and you suspect mix dogma is blocking the other
• About to expand into a new region and need to decide direct-first vs partner-first
• M&A diligence: target company claims "partner-led at 70% gross margin" — need to validate after loading

**Do not use for:**

• Designing partner tiers, joint GTM motion, revshare splits → `partnerships-architect`
• SDR-to-AE routing, lead scoring, MQL definitions → `business-growth/revenue-operations`
• Strategic CRO decisions ("should we hire a VP Sales?", comp plan design) → `c-level-advisor/cro-advisor`
• Quarterly close, GAAP revenue recognition, channel-level P&L for historical reporting → `finance/financial-analysis`
• Per-deal discount approval → `deal-desk`
• Pricing model design → `pricing-strategist`

Workflow

Step 1 — Intake channel data

Fill `assets/channel_data_template.md` (≈ 20 min). Capture per channel: deal count TTM, ARR TTM, avg deal size, gross margin %, CAC, sales-cycle days, retention rate, expansion rate, partner discount %, all attributable costs (SDR / AE / SE / channel manager / CS / support / marketing / partner MDF / tooling / overhead allocation %).

The template surfaces the costs teams most often forget: partner enablement time, certification investment, channel-conflict resolution overhead, channel-manager headcount cost.

Step 2 — Compute cost-to-serve per channel

Run `scripts/cost_to_serve_calculator.py --input channel.json --output markdown`.

Output: fully-loaded cost-to-serve **per deal** AND **per dollar of ARR**, with direct costs broken out from allocated overhead, and a "true gross margin" line after channel-specific load. Flags double-counting and surfaces hidden costs.

Run once per channel. The "true gross margin" line is the input the next two scripts care about.

Step 3 — Compute ROI per channel under three lenses

Run `scripts/channel_roi_analyzer.py --input roi.json --profile saas --output markdown`.

Output: per channel, three ROI numbers (Cash year-1, LTV-adjusted, Marginal), the diminishing-returns inflection point, and a verdict: DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT.

Verdict logic is deterministic and surfaced in the report. Humans can override; the skill won't.

Step 4 — Optimize channel mix subject to constraints

Run `scripts/channel_mix_optimizer.py --input mix.json --profile saas --output markdown`.

Output: recommended mix that maximizes effective ARR subject to constraints (min direct %, max partner concentration), plus a sensitivity table (what if direct CAC rises 20%? what if partner discount widens 5 points?).

Step 5 — Decide

Take the three reports into the quarterly channel review. The skill recommends; the human commits.

Scripts

• `scripts/cost_to_serve_calculator.py` — fully-loaded cost-to-serve per deal AND per $ ARR, with hidden-cost surfacing
• `scripts/channel_roi_analyzer.py` — 3-lens ROI (Cash / LTV / Marginal) with verdicts and diminishing-returns inflection
• `scripts/channel_mix_optimizer.py` — constrained mix optimizer with sensitivity scenarios

All scripts: stdlib only. `--help`, `--sample`, `--input`, `--output` work on all three. Industry tuning via `--profile {saas,api,enterprise-software,marketplace,hardware}` on the two analyzers.

Quick example

# Emits fully-loaded cost-to-serve per channel (direct vs partner-led) for the built-in sample channel data
cd commercial/skills/channel-economics && python3 scripts/cost_to_serve_calculator.py --sample

References

• `references/channel_economics_canon.md` — Skok, Bessemer State of the Cloud, Tunguz, Pacific Crest / KeyBanc SaaS Survey, Ramanujam, Jay McBain (Canalys)
• `references/cost_to_serve_canon.md` — Kaplan & Cooper (ABC), Horngren, Jeremy Hope, IBM CTS case studies, McKinsey, Gartner, BCG
• `references/channel_anti_patterns.md` — Forrester, Tunguz, Hessling, HBR, SiriusDecisions, MIT Sloan, Gartner

Assumptions

• Channel economics is a **forward-looking** question. Historical channel P&L is finance's job; this skill loads forward economics for a decision.
• "Channel" means a coherent go-to-market motion (direct outbound, partner-led, marketplace, reseller, OEM). It does not mean a marketing