This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Channel economics: design and analyze the financial structure of go-to-market channels. Use when picking a channel mix, modeling partner margin or TCO, designing partner tiers and rebates, or analyzing channel conflict.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
End-to-end financial modeling and design of go-to-market channels: direct sales economics, reseller / distributor margin structures, marketplace fees, partner tier economics, channel conflict resolution, and the TCO frameworks that compare channel options apples-to-apples.
This skill provides the financial backbone for channel strategy. For strategic partnership design (which channel to invest in, how to structure the partnership), see business-growth/partnerships-architect. For partner-deal-level approval mechanics, see business-growth/deal-desk.
| Situation | Skill applies |
|---|---|
| Deciding direct vs partner-led for a new product | Yes — start with channel model decision tree |
| Designing a partner tier structure (silver/gold/platinum) | Yes — see partner tier economics |
| Modeling a specific partner deal's margin / payback | Yes — scripts/channel_margin_calculator.py |
| Analyzing channel conflict (overlapping direct + partner deals) | Yes — see channel conflict + scripts/channel_mix_optimizer.py |
| Building a partner program rebate / SPIFF structure | Yes — see rebate design |
| Comparing AWS Marketplace vs direct list-price economics | Yes — scripts/channel_margin_calculator.py --channel marketplace |
| Negotiating a specific partner contract | Use business-growth/contract-and-proposal-writer for the contract; this for the economics |
| Strategic partnership design (joint go-to-market, OEM, white-label) | Use business-growth/partnerships-architect first |
Six core channel models. Most companies use a mix.
What's the product's complexity + price point?
Low complexity, low price (< $10k ACV):
├── Self-serve / PLG → no channel
├── E-commerce → direct via web
└── Marketplace (AWS / Azure / GCP / Salesforce AppExchange) → if buyer already there
Medium complexity, mid-market price ($10k - $250k ACV):
├── Inside sales / SDR-led direct → if buyer journey is well-understood
├── Reseller / VAR (Value-Added Reseller) → if local presence / language matters
├── Marketplace → if buyer prefers procurement via existing relationship
└── Embedded / OEM → if your product is a component in someone else's offering
High complexity, enterprise ($250k+ ACV):
├── Direct field sales → standard for high-touch enterprise
├── Strategic SI / Integrator (Accenture, Deloitte, etc.) → if implementation is a substantial project
├── ISV / Embedded → if you're a feature in a larger platform
└── Reseller / Distributor → for regional or vertical specialty
Operational / managed-service buyer:
└── MSP (Managed Service Provider) → if customer wants outsourced operations
See references/channel-models-direct-partner-marketplace.md for each model in depth: economic structure, typical margin splits, when each works / fails, contract patterns.
Apples-to-apples channel comparison requires a consistent TCO model. The naive comparison ("direct gets 100%, reseller gets 70%") misses critical costs.
Channel Contribution Margin
= Channel-attributed Revenue
− COGS
− Partner Discount/Commission
− Channel-specific Sales Cost (allocated)
− Channel-specific Marketing Cost (MDF, co-marketing)
− Partner Enablement Cost (training, certification)
− Channel Operations Cost (channel manager headcount)
− Channel-specific Support Cost (T1 partner support)
For a $100k ACV deal:
| Component | Direct | Reseller (30% off) | AWS Marketplace |
|---|---|---|---|
| Customer payment | $100,000 | $100,000 | $100,000 |
| Reseller / marketplace fee | $0 | -$30,000 (30% discount) | -$3,000 (3% AWS fee) |
| Revenue to us | $100,000 | $70,000 | $97,000 |
| COGS (15%) | -$15,000 | -$10,500 | -$14,550 |
| Sales cost (allocated CAC) | -$25,000 | -$5,000 | -$8,000 |
| Marketing cost (MDF / listing) | -$2,000 | -$8,000 | -$5,000 |
| Partner enablement (amortized) | $0 | -$3,000 | -$1,500 |
| Channel ops (amortized) | $0 | -$2,000 | -$1,000 |
| Support cost | -$5,000 | -$3,000 | -$5,000 |
| Net contribution | $53,000 | $38,500 | $61,950 |
| % of ACV | 53% | 38.5% | 62% |
The "30% discount" reseller deal is more like 14.5% margin difference once everything's counted. Marketplace can look better than direct on per-deal basis (Amazon's sales team brings the buyer) — but volume varies.
Use scripts/channel_margin_calculator.py --deal deal.yaml --channel <type> to model this for any deal.
See references/margin-and-tco-frameworks.md for the full TCO framework, per-cost-line guidance, and how to allocate "fully-loaded" sales / marketing / ops costs.
Multi-tier partner programs (Authorized → Silver → Gold → Platinum) are common. Designed badly, they reward effort that isn't valuable; designed well, they reward outcomes that drive growth.
| Tier | Annual revenue threshold | Discount % | Other benefits | Requirements |
|---|---|---|---|---|
| Authorized | None | 10% | Standard support | Sign partner agreement; 1 certified person |
| Silver | $100k | 15% | Co-marketing eligible (limited MDF) | $100k achieved; 3 certified people; 2 customer wins |
| Gold | $500k | 20% + 5% rebate at threshold | Dedicated channel manager; MDF; deal registration; lead sharing | $500k achieved; 5 certified; 5 wins; 80% renewal rate |
| Platinum | $2M | 25% + 7% rebate at threshold | Top-tier support; joint roadmap; preferred status; press release rights | $2M achieved; 10 certified; 10 wins; 90% renewal; participation in advisory board |
Use scripts/partner_tier_economics.py --tiers tiers.yaml to model tier economics: gross margin per tier, partner-side incentive, break-even revenue per partner per tier.
Three common reward structures, each with trade-offs:
Partner buys from you at a discount; sells to customer at list (or close). Margin = the spread.
Pros: Simple. Cash flow goes to partner immediately. Cons: Hard to incentivize specific behaviors. Discount is locked in regardless of performance.
Partner pays full price (or near it); earns rebate quarterly / annually based on revenue / tier achievement.
Pros: Ties reward to actual achievement; behaviors can be incentivized (e.g., bonus for selling new products). Cons: Cash-flow burden on partner. Complex to administer.
Per-deal or per-period bonuses for specific actions: bring leads, attend events, certify staff.
Pros: Highly targetable. Rewards specific behaviors you want. Cons: Easy to game; admin overhead high; partners often expect it without producing.
| Partner type | Front-end | Back-end | MDF/SPIFF |
|---|---|---|---|
| Reseller (transactional) | 70-80% of total comp | 10-20% | 5-10% |
| VAR (consultative selling) | 50-60% | 20-30% | 10-20% |
| Distributor (volume play) | 80-90% | 5-15% | 5% |
| ISV / Embedded | n/a (rev share) | 100% | 0 |
| MSP | 40-60% | 20-30% | 10-30% |
Channel conflict happens when multiple sales paths chase the same customer. Common forms:
| Scenario | Resolution pattern |
|---|---|
| Direct rep finds opportunity also touched by partner | Deal registration: first to register wins; partner gets credit if they brought it |
| Partner finds direct customer | If direct is already engaged: partner deferred (with consolation MDF perhaps); if not: partner leads |
| Customer asks for direct after partner-led pilot | Honor partner relationship for term; transition at next renewal if appropriate |
| Scenario | Resolution pattern |
|---|---|
| Two resellers both pursuing same account | First-registered wins; second is offered alternative leads / regional swap |
| Vertical specialist vs geographic | Vertical wins (customer values vertical expertise more) |
| New partner pursues incumbent partner's customer | Incumbent has right of first refusal for 90 days |
Customer can buy via AWS Marketplace OR direct. If price is lower direct, customer feels gamed. If price is same, why not just use marketplace? Common resolution:
See references/channel-conflict-resolution.md for the full conflict-resolution playbook including deal registration process, neutral arbitration, conflict-of-interest disclosure.
Before modeling the channel economics, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.
Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.
scripts/channel_margin_calculator.py per channel option at expected ACVscripts/partner_tier_economics.py to size the gates and benefitsbusiness-growth/contract-and-proposal-writer)scripts/channel_margin_calculator.py --deal deal.yaml --channel partnerscripts/channel_mix_optimizer.py --revenue revenue.csv examines contribution margin per channel + identifies under-/over-invested channels| Script | Input | Output |
|---|---|---|
scripts/channel_margin_calculator.py | Deal spec YAML + channel type | Per-channel net contribution margin, cost line breakdown, comparison vs direct baseline |
scripts/partner_tier_economics.py | Tier definitions YAML | Per-tier: gross margin to us, gross margin to partner, partner break-even, tier graduation incentive analysis |
scripts/channel_mix_optimizer.py | Revenue CSV (by channel + quarter) | Per-channel revenue contribution, per-channel margin contribution, recommended rebalancing |
All scripts: stdlib only, argparse CLI, JSON or markdown output.
In Scope:
scripts/channel_margin_calculator.py, including an all-channel comparisonscripts/partner_tier_economics.pyscripts/channel_mix_optimizer.pyOut of Scope:
business-growth/partnerships-architect/)business-growth/deal-desk/)business-growth/contract-and-proposal-writer/)business-growth/pricing-strategy/)sales-success/sales-operations/)Important Caveats:
business-growth/partnerships-architect — strategic partnership design (this skill = the economics; that one = the strategy)business-growth/deal-desk — approval mechanics for partner deals (this skill = "what does it cost"; deal desk = "should we approve")business-growth/pricing-strategy — sets list pricing that channel economics deviates frombusiness-growth/revenue-operations — channel revenue is segmented in RevOps reportingbusiness-growth/contract-and-proposal-writer — drafts partner agreementssales-success/sales-operations — runs channel ops (deal registration, MDF approval, certification tracking)c-level-advisor/cs-cro-advisor — strategic channel-mix decisions are CRO-level아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.