Channel Economics

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 source.
• Cost-to-serve requires honest overhead allocation. The script validates that overhead % is consistent across channels — false partner-margin lift from inconsistent allocation is the #1 anti-pattern.
• LTV inputs (retention, expansion) are per-channel, not pooled. Partner-sourced customers often retain differently than direct-sourced — this difference is usually the largest economic variable and the most ignored.
• Industry profiles (--profile) tune defaults for benchmarks (e.g., SaaS direct CAC payback target ~12mo, enterprise ~18mo) — they don't override your numbers.
• This is a decision-support skill. Output is verdicts and a recommended mix, never an automatic resource reallocation.

Anti-patterns

• Treating "influenced" deals as "sourced" deals. A partner that touched a deal your AE already had is not channel-sourced revenue. Loading this as partner revenue inflates partner ROI and inflates direct CAC simultaneously.
• Inconsistent overhead allocation. Allocating 25% overhead to direct deals and 5% to partner deals because "the partner handles the overhead" is false. The partner manager, partner program, MDF, certification, and conflict-resolution all live in your P&L.
• Ignoring enablement time as a cost. Every hour your AE spends co-selling with a partner is a direct cost charged to the partner channel — most teams forget to load it.
• MDF without ROI tracking. Market Development Funds disbursed without an attributable pipeline ROI are just a partner-discount extension. The skill flags MDF with no return.
• Channel-mix dogma. "We're a partner-first company" / "we don't sell direct" blocks profitable segments. Mix should follow the math, not the slogan.
• Computing channel ROI without retention differential. If partner-sourced customers churn 5 points higher than direct, ignoring it overstates partner LTV by 30-50%. Per-channel retention is mandatory input.
• No cost-attribution for channel-manager headcount. A $200k channel manager managing $4M of partner ARR is $50 of channel-manager cost per $1k ARR — material to the verdict.
• Confusing this skill with partnerships-architect. That skill designs the partner program. This skill tells you whether the program pays for itself.