ecommerce-advisor

name: ecommerce-advisordescription: >  Strategic advisory for e-commerce founders on unit economics, fulfillment models,  payments, and channel strategy. Use when evaluating an ecommerce idea or modeling  unit economics, or mentioning DTC, Shopify, Amazon, 3PL, or CAC.license: MIT + Commons Clausemetadata:   version: 1.0.0  author: borghei  category: vertical-advisors  domain: ecommerce  updated: 2026-05-04  python-tools: ecom_unit_economics_calculator.py  tech-stack: ecommerce, retail  tags:     - ecommerce    - unit-economics    - fulfillment    - payments    - dtc

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.

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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

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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.

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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.

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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

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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.

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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

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Templates

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

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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.