channel-economics
by alirezarezvanichannel-economics helps RevOps and commercial leaders compare direct, partner, marketplace, reseller, or OEM channels with fully loaded cost-to-serve, ROI lenses, and constrained channel-mix recommendations. Includes Python scripts, data templates, and guidance for channel-economics usage.
This skill scores 84/100, making it a solid listing candidate for directory users who need structured channel-economics analysis rather than a generic prompt. The repository provides a clear use case, concrete scripts, input templates, and reference material that should help an agent execute with relatively low guesswork, though adoption would be easier with explicit installation/setup guidance and stronger validation notes around model assumptions.
- Strong triggerability: the frontmatter clearly defines when to use it for direct vs. partner-led channel economics, quarterly channel reviews, ROI, cost-to-serve, and channel-mix questions.
- Operationally useful workflow assets: three stdlib Python scripts cover cost-to-serve, channel ROI, and mix optimization, each with sample/input usage and markdown output options.
- Good install-decision evidence: the data template, anti-pattern guide, and canon references explain required inputs, common pitfalls, and the business methodology behind the calculations.
- No install command or README is present, so users must infer setup from the skill path and script usage examples.
- The scripts use benchmark/profile assumptions and deterministic models; teams will need to validate inputs and assumptions before relying on recommendations for executive decisions.
Overview of channel-economics skill
What channel-economics is for
channel-economics is a commercial analysis skill for deciding whether direct, partner-led, marketplace, reseller, OEM, or similar go-to-market channels are actually profitable after fully loaded costs. It is best for Revenue Operations, Heads of Commercial, VP Sales, and finance-adjacent operators preparing a quarterly channel review, partner strategy reset, or channel-mix investment decision.
The real job is not “compare direct vs. partner revenue.” It is to answer: which channel earns money after CAC, partner discounts, MDF, enablement, support load, retention differences, deal velocity, and overhead allocation are included?
Best-fit use cases for Revenue Operations
Use the channel-economics skill when your CRM says one channel is growing but leadership is unsure whether that growth is efficient. It is especially useful when channel-sourced and channel-influenced deals are mixed, partner margin looks attractive on the surface, or pooled CAC/LTV metrics are hiding weak segments.
Typical outputs include fully loaded cost-to-serve, cash ROI, LTV-adjusted ROI, marginal ROI, channel verdicts such as DOUBLE-DOWN, MAINTAIN, DEFUND, or EXIT, and a recommended mix subject to constraints such as minimum direct coverage or maximum partner concentration.
What makes this skill more useful than a generic prompt
The repository includes deterministic Python scripts, not just prompt instructions. cost_to_serve_calculator.py calculates per-channel cost-to-serve, channel_roi_analyzer.py evaluates ROI through multiple lenses, and channel_mix_optimizer.py performs a discrete mix search with sensitivity checks. The supporting references also call out common channel anti-patterns, such as treating partner-attached direct deals as partner-sourced wins.
Where it can mislead if inputs are weak
The skill depends on clean definitions. If “channel” means marketing source in one row and sales motion in another, the analysis will be contaminated. If retention is pooled across channels, overhead allocation changes by segment, or partner discounts are omitted, the output may look precise while reinforcing bad assumptions.
How to Use channel-economics skill
channel-economics install and first files to read
Install with your skill manager if supported:
npx skills add alirezarezvani/claude-skills --skill channel-economics
Then review the source path:
commercial/skills/channel-economics
Read these files first: SKILL.md for the workflow, assets/channel_data_template.md for the JSON input schema, references/channel_anti_patterns.md for common classification mistakes, and the three scripts in scripts/ to understand exactly what each calculation expects.
Inputs the skill needs before it can produce useful analysis
Prepare one consistent dataset per channel. At minimum, include deal volume, gross revenue or ARR, attributed headcount costs, sales engineering, customer success, support, marketing, partner discount, MDF, enablement time, certification investment, tooling, allocated overhead, retention, average deal size, and investment level.
The template explicitly recommends leaving unknown values as null or clearly marking them as unknown rather than silently inserting zero. That matters because the scripts flag missing hidden-cost categories instead of pretending the channel is cheaper than it is.
Turn a rough request into a complete prompt
Weak prompt: “Analyze our partner channel.”
Stronger channel-economics usage prompt:
“Use the channel-economics skill to compare direct, partner-led EMEA, and marketplace channels for a SaaS company. Treat channel as the sales motion, not the lead source. Use activity-driver overhead allocation consistently. Flag any channel-sourced deals that were internally first-touched. Calculate cost-to-serve, cash ROI, LTV ROI, marginal ROI, and recommend a mix with at least 45% direct pipeline and no partner above 35% concentration. Unknown values should be surfaced, not replaced with zero.”
This prompt improves output because it defines channels, constraints, attribution rules, industry profile, and the decision needed.
Practical script workflow
Start with assets/channel_data_template.md. Run cost_to_serve_calculator.py once per channel to expose loaded cost and missing hidden costs. Use those outputs to build the ROI input for channel_roi_analyzer.py, then feed comparable channel metrics into channel_mix_optimizer.py.
Useful commands to inspect behavior before using real data:
python scripts/cost_to_serve_calculator.py --sample
python scripts/channel_roi_analyzer.py --sample
python scripts/channel_mix_optimizer.py --sample
Use --output markdown when you want outputs that can be pasted directly into a planning memo.
channel-economics skill FAQ
Is channel-economics only for SaaS?
No. The scripts include profiles for saas, api, enterprise-software, marketplace, and hardware. The benchmarks differ by profile, such as payback targets, LTV/CAC floors, and LTV multipliers. SaaS teams may get the most familiar defaults, but the method is broader than SaaS if your inputs are mapped carefully.
How is this different from asking an AI to compare channels?
A generic prompt can summarize pros and cons. The channel-economics skill gives a more structured operating model: consistent cost categories, explicit hidden-cost checks, ROI lenses, sensitivity tests, and mix constraints. It is designed to reduce executive hand-waving around partner profitability.
Can a beginner use this skill?
Yes, if they can gather the data. The included channel data template explains what to enter and why. However, the user still needs enough RevOps or finance context to define attribution, allocate overhead consistently, and separate channel-sourced from channel-influenced deals.
When should I not use channel-economics?
Do not use it for top-of-funnel marketing attribution, campaign ROI, or partner relationship scoring without economic data. It is also a poor fit when leadership has not agreed on channel definitions, when costs cannot be attributed at all, or when the decision is purely strategic and intentionally ignores near-term economics.
How to Improve channel-economics skill
Improve channel-economics results with cleaner definitions
The biggest quality lever is a strict channel definition. Use coherent go-to-market motions such as direct-enterprise, partner-led-EMEA, marketplace, or reseller-SMB. Avoid mixing lead source, region, and sales motion unless the analysis intentionally needs that cut.
Also define “channel-sourced” narrowly: the partner originated the opportunity and brought it unqualified. If your AE sourced and qualified the deal, then a partner joined late for procurement or fulfillment, that is usually channel-influenced direct revenue with extra partner cost.
Provide better cost and retention assumptions
For cost-to-serve, include the unglamorous items: partner enablement time, certification, channel manager attribution, support burden, tooling, conflict resolution, and overhead. These are exactly the omissions that make partner-led channels look artificially profitable.
For ROI, use per-channel retention and expansion assumptions. Pooled retention can hide the fact that one channel closes faster but churns harder, while another has slower payback but stronger LTV.
Iterate after the first output
Treat the first result as a diagnostic, not the final board answer. Review which inputs were unknown, which hidden costs were flagged, and which channel verdicts are sensitive to small assumption changes. Then rerun with revised discounts, CAC increases, retention drops, or stricter concentration limits.
If the recommended mix changes dramatically after a 3-point retention decline or a 5-point partner discount increase, present the decision as conditional rather than absolute.
Common failure modes to watch
The most common failure modes are inconsistent overhead allocation, zero-filled unknowns, inflated partner sourcing claims, and using average ROI when marginal ROI is already declining. The channel-economics skill is strongest when you ask it to expose these weaknesses explicitly instead of only producing a polished recommendation.