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Channel Economics

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borghei
channel-economics

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.

Overview

Publisherborghei
RepositoryClaude-Skills
Skill namechannel-economics
Stars
856
Forks
154
Bundled files
6
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by borghei on GitHub. Read the source before you install it.

Installation

Install the Channel Economics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/borghei/Claude-Skills/tree/main/business-growth/channel-economics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/borghei/Claude-Skills.git /tmp/Claude-Skills
mkdir -p .claude/skills
cp -r /tmp/Claude-Skills/business-growth/channel-economics .claude/skills/channel-economics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Channel Economics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Channel Economics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Channel Economics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Channel Economics

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.


When to use this skill

SituationSkill applies
Deciding direct vs partner-led for a new productYes — 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 / paybackYes — 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 structureYes — see rebate design
Comparing AWS Marketplace vs direct list-price economicsYes — scripts/channel_margin_calculator.py --channel marketplace
Negotiating a specific partner contractUse 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

The channel model decision tree

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.


Margin and TCO framework

Apples-to-apples channel comparison requires a consistent TCO model. The naive comparison ("direct gets 100%, reseller gets 70%") misses critical costs.

True channel TCO formula

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)

Side-by-side comparison

For a $100k ACV deal:

ComponentDirectReseller (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 ACV53%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.


Partner tier economics

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.

Standard tier structure

TierAnnual revenue thresholdDiscount %Other benefitsRequirements
AuthorizedNone10%Standard supportSign partner agreement; 1 certified person
Silver$100k15%Co-marketing eligible (limited MDF)$100k achieved; 3 certified people; 2 customer wins
Gold$500k20% + 5% rebate at thresholdDedicated channel manager; MDF; deal registration; lead sharing$500k achieved; 5 certified; 5 wins; 80% renewal rate
Platinum$2M25% + 7% rebate at thresholdTop-tier support; joint roadmap; preferred status; press release rights$2M achieved; 10 certified; 10 wins; 90% renewal; participation in advisory board

Tier design principles

  1. Outcome-based, not effort-based. Reward revenue + retention, not training hours or marketing event count.
  2. Achievable but stretching. Each tier should be a 12-18 month stretch from the prior.
  3. Differentiable benefits. Each tier needs benefits a partner actively wants (not just "more support").
  4. Renewable status. Tiers re-evaluated annually. Partners can move down if they don't maintain.
  5. Anti-gaming protection. Discount-stacking, registration gaming, transfer pricing — design out.

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.


Rebate / SPIFF design

Three common reward structures, each with trade-offs:

Front-end discount

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.

Back-end rebate

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.

MDF (Marketing Development Funds) / SPIFF

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.

Typical mix

Partner typeFront-endBack-endMDF/SPIFF
Reseller (transactional)70-80% of total comp10-20%5-10%
VAR (consultative selling)50-60%20-30%10-20%
Distributor (volume play)80-90%5-15%5%
ISV / Embeddedn/a (rev share)100%0
MSP40-60%20-30%10-30%

Channel conflict

Channel conflict happens when multiple sales paths chase the same customer. Common forms:

Direct-vs-partner conflict

ScenarioResolution pattern
Direct rep finds opportunity also touched by partnerDeal registration: first to register wins; partner gets credit if they brought it
Partner finds direct customerIf direct is already engaged: partner deferred (with consolation MDF perhaps); if not: partner leads
Customer asks for direct after partner-led pilotHonor partner relationship for term; transition at next renewal if appropriate

Partner-vs-partner conflict

ScenarioResolution pattern
Two resellers both pursuing same accountFirst-registered wins; second is offered alternative leads / regional swap
Vertical specialist vs geographicVertical wins (customer values vertical expertise more)
New partner pursues incumbent partner's customerIncumbent has right of first refusal for 90 days

Marketplace-vs-direct conflict

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:

  • Same price direct vs marketplace (customer doesn't get punished for procurement choice)
  • Quota credit to the direct rep when customer chooses marketplace (so rep isn't disincentivized)
  • Marketplace listing visibility as a value-add, not as a different pricing channel

See references/channel-conflict-resolution.md for the full conflict-resolution playbook including deal registration process, neutral arbitration, conflict-of-interest disclosure.


Clarify First

Before modeling the channel economics, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Channel model(s) in scope — direct, reseller/VAR, distributor, marketplace, OEM, or MSP (sets which decision-tree branch and TCO comparison to run)
  • Target ACV / price point — sub-$10k vs mid-market vs enterprise (selects the viable channel branch and sizes per-deal margin)
  • Fully-loaded cost lines — COGS %, allocated sales/marketing/ops/support costs (drives the TCO contribution-margin model, not just the headline discount)
  • Partner contribution + tier intent — what the partner does (lead, sell, implement) and whether you're designing tiers/rebates (drives tier economics + rebate/SPIFF mix)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the model.

End-to-end workflows

Workflow: Design a new partner program

  1. Pick channel models — direct + reseller? marketplace? OEM? — using the decision tree
  2. Model the economics — scripts/channel_margin_calculator.py per channel option at expected ACV
  3. Design tier structure — scripts/partner_tier_economics.py to size the gates and benefits
  4. Define rebate / SPIFF mix — per tier and partner type
  5. Write the partner agreement (with business-growth/contract-and-proposal-writer)
  6. Build channel ops — deal registration, MDF approval, certification tracking
  7. Hire channel manager(s) — usually 1 manager per 10-15 active partners
  8. Pilot with 3-5 partners — measure, iterate, then scale

Workflow: Evaluate a specific partner deal

  1. Inputs: ACV, partner discount %, expected close, partner's contribution (lead source? sales effort? implementation?)
  2. Calculate net contribution — scripts/channel_margin_calculator.py --deal deal.yaml --channel partner
  3. Compare to direct alternative — would this deal have closed direct? at what cost?
  4. Decide: approve / counter / decline (often via deal desk if it's a non-standard partner discount)

Workflow: Channel mix analysis

  1. Inputs: actual revenue by channel for last 4 quarters
  2. Run mix optimizer — scripts/channel_mix_optimizer.py --revenue revenue.csv examines contribution margin per channel + identifies under-/over-invested channels
  3. Recommend rebalancing — e.g., "Reseller channel: 20% of revenue, 8% of contribution margin — reduce investment; marketplace: 15% of revenue, 25% of contribution — increase listing visibility"
  4. Quarterly review: present to CRO / CFO

Workflow: Resolve a channel conflict

  1. Document the conflict — accounts involved, parties, history
  2. Apply the registration rule — first-registered partner wins absent overriding facts
  3. Consider exceptions — strategic logo, customer preference, vertical expertise
  4. Communicate decision — both parties, with reasoning, in writing
  5. Compensate the loser — alternative leads, MDF, regional swap; preserve the relationship

Anti-patterns

  • Direct + partner at same price. Customer feels punished for not using direct (or vice versa); kills partner motivation. Price-to-customer must be consistent across channels.
  • Discount-only partner program. Partners that only get a discount have no skin in your success; treat you as another vendor; switch easily.
  • Endless partner expansion without enablement. Signing 200 partners that don't sell anything; channel manager headcount can't scale; partners stale.
  • Marketplace as afterthought. Listing on AWS Marketplace without dedicated investment (listing optimization, co-sell programs) = marketplace generates nothing.
  • Channel manager as glorified email forwarder. CM should drive partner pipeline, not just relay leads.
  • Rebates with no audit. Partner self-reports revenue; you trust it; reality is 20% off. Build verification.
  • MDF spent on activities that don't drive pipeline. Partner runs a great event, generates no pipeline. MDF should require pipeline outcome.
  • Channel conflict policy that isn't followed. Policy says first-registered wins, but exec overrides every time → policy is theater.
  • Different commission per channel for same deal. Direct rep gets 8% on $100k deal, channel rep gets 6% on $100k deal — direct rep refuses partner help; channel rep undercut.
  • OEM / embedded deals priced like resale. OEM = customer doesn't see you at all; ASP can be 50-80% of list. Resale = customer sees you. Different economics; different price points.

Tooling outputs

ScriptInputOutput
scripts/channel_margin_calculator.pyDeal spec YAML + channel typePer-channel net contribution margin, cost line breakdown, comparison vs direct baseline
scripts/partner_tier_economics.pyTier definitions YAMLPer-tier: gross margin to us, gross margin to partner, partner break-even, tier graduation incentive analysis
scripts/channel_mix_optimizer.pyRevenue CSV (by channel + quarter)Per-channel revenue contribution, per-channel margin contribution, recommended rebalancing

All scripts: stdlib only, argparse CLI, JSON or markdown output.


References


Related skills

  • 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 from
  • business-growth/revenue-operations — channel revenue is segmented in RevOps reporting
  • business-growth/contract-and-proposal-writer — drafts partner agreements
  • sales-success/sales-operations — runs channel ops (deal registration, MDF approval, certification tracking)
  • c-level-advisor/cs-cro-advisor — strategic channel-mix decisions are CRO-level

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Channel Economics AI skill do?

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.

Why use Channel Economics on TypingMind?

Because you install it once and use it with any model. Channel Economics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Channel Economics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/borghei/Claude-Skills/tree/main/business-growth/channel-economics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Channel Economics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Channel Economics?

As many as you like. As long as a model supports skills, you can use Channel Economics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Channel Economics AI skill free?

It is published on GitHub by borghei. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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