ecommerce-advisor

Imported from borghei/claude-skills (vertical-advisors/ecommerce-advisor/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill ecommerce-advisor. Copyright stays with the author (MIT + Commons Clause).

E-commerce Advisor

Strategic frameworks for e-commerce founders, operators, and brand builders. Most ecommerce decisions are unit-economics decisions — knowing the math is the difference between a brand that compounds and one that subsidizes itself out of existence.

Keywords

ecommerce, e-commerce, DTC, direct-to-consumer, Shopify, Amazon, retail, wholesale, 3PL, fulfillment, dropship, unit economics, contribution margin, CAC, AOV, LTV, returns, refunds, payment processing, interchange

Clarify First

Before building the model, 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.

• Single channel or blended: model each channel (DTC, Amazon, wholesale) separately; a blended model hides an unprofitable channel and makes contribution margin meaningless.
• AOV and COGS: average order value and landed cost of goods (drives gross margin, the top line of the model).
• Returns/refund rate: returns eat margin twice (the sale plus reverse logistics), so this swings contribution margin.
• CAC basis: true paid/blended CAC and what ad spend is counted (drives CAC payback period and break-even repeat rate).

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.

Quick Start

python scripts/ecom_unit_economics_calculator.py model.json

Calculates gross margin, contribution margin, CAC payback, and per-order profit from a structured input file.

Core Workflows

Workflow 1: Unit Economics Model

1. Build a JSON config: COGS, fulfillment cost, payment processing %, returns %, ad spend %, AOV
2. Run calculator: python scripts/ecom_unit_economics_calculator.py model.json
3. Identify the marginal cost line items eating most of the margin
4. Decide: cut costs, raise price, change channel mix, or kill the SKU

Time Estimate: 2-4 weeks for first robust model.

Workflow 2: Fulfillment Strategy

1. Read references/fulfillment_models.md
2. Score each model (DTC self-fulfilled, 3PL, Amazon FBA, dropship, retail) for your stage and SKU profile
3. Migrate fulfillment as you scale — most brands change models 2-3 times in their first 3 years

Time Estimate: 4-8 weeks per major fulfillment transition.

Workflow 3: Channel Strategy

1. Read references/channel_strategy.md
2. Decide channel mix: DTC site, Amazon, wholesale, retail, marketplace
3. Each channel has distinct unit economics — model them separately, never blend
4. Sequence: most brands start DTC, add Amazon, add wholesale / retail

Time Estimate: Continuous, with major decisions every 6-12 months.

Tools

ecom_unit_economics_calculator.py

Models per-order, per-month, and CAC-payback economics from a structured input.

python scripts/ecom_unit_economics_calculator.py model.json
python scripts/ecom_unit_economics_calculator.py model.json --json

Input model schema in the script's docstring; example in assets/unit_economics_template.json.

Outputs:

• Gross margin (% and absolute)
• Contribution margin (after marginal CAC)
• CAC payback period (months)
• Break-even repeat rate

Scope & Limitations

In Scope:

• Per-order unit economics for one channel: gross margin, contribution margin after acquisition cost, CAC payback and break-even repeat rate from ecom_unit_economics_calculator.py
• Fulfillment model choice across self-fulfilled DTC, 3PL, Amazon FBA, dropship and retail
• Channel mix and sequencing across DTC site, Amazon, wholesale, retail and marketplaces, with each channel modeled separately
• Decisions on cutting cost, raising price, changing channel mix or dropping a SKU

Out of Scope:

• Running paid acquisition campaigns (see marketing/paid-ads/)
• PCI DSS scoping for card payments (see ra-qm-team/pci-dss-specialist/)
• Company-level financial models, cash runway and fundraising (see c-level-advisor/cfo-advisor/)
• Inventory planning, demand forecasting or store setup on Shopify or Amazon

Important Caveats:

• The calculator takes one channel per JSON file and uses only the figures you enter. It does not pull data from a store, marketplace or ad account.
• It models per-order and first-year repeat economics. Working capital tied up in inventory is not modeled, although the skill flags it as the main cash-flow risk.
• Benchmarks such as a CAC payback under 6 months are rules of thumb. Fee levels on marketplaces and payment processors change, so update your inputs when they do.

Reference Guides

• references/fulfillment_models.md — DTC self, 3PL, FBA, dropship, retail — when each fits
• references/channel_strategy.md — DTC site, Amazon, wholesale, retail — economics per channel

Templates

• assets/unit_economics_template.json — Input file for the calculator with example values

Best Practices

• Model channels separately. A blended LTV across DTC and Amazon hides the fact that Amazon may be unprofitable.
• Returns and refunds compound. A 15% return rate eats into margin twice — once on the initial sale, once on the reverse logistics.
• Plan for inventory. Working capital tied up in inventory is the #1 cash-flow killer for product brands.
• Don't fall in love with revenue. $10M revenue at 5% contribution margin is worse than $3M revenue at 30% contribution margin.
• Be honest about CAC. Most DTC brands subsidize CAC and call it growth. CAC payback under 6 months is the bar.