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Break Even Calculator Ecommerce

by LeroyCreates · GitHub ↗ · v1.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install break-even-calculator-ecommerce
Description
Calculate ecommerce break-even thresholds using price, cost, shipping, ad spend, and overhead assumptions so operators can make launch and scale decisions wi...
README (SKILL.md)

Break-even Calculator Ecommerce

Calculate the real no-loss line before deciding whether to launch harder, discount harder, or scale paid traffic.

This is not a generic margin calculator. It separates variable from fixed costs, models real ecommerce unit economics (returns, payment fees, platform takes), and translates math into actionable launch/hold/scale decisions.


Quick Reference

Decision Key Metric Green Yellow Red
Launch viability Contribution margin % > 40% 20–40% \x3C 20%
Ad scaling room Break-even CPA CPA \x3C 60% of CM CPA 60–90% of CM CPA > 90% of CM
Discount safety Margin after discount > 25% CM remaining 10–25% CM remaining \x3C 10% CM remaining
Free shipping Margin absorption Shipping \x3C 30% of CM Shipping 30–50% of CM Shipping > 50% of CM
Scale readiness Break-even units/mo \x3C 50% of current vol 50–80% of current vol > 80% of current vol

Solves

Ecommerce operators lose money not because they can't calculate margins, but because:

  • They use gross margin when they should use contribution margin
  • Platform fees, payment processing, returns, and packaging get excluded from "cost"
  • Ad scaling decisions are made on ROAS without knowing actual break-even ROAS
  • Discount and free-shipping policies are set without modeling margin impact
  • "Profitable" products turn unprofitable at scale because fixed costs aren't allocated
  • Teams confuse revenue growth with profit growth

Goal: Give operators a clear, reviewable break-even model that supports real decisions — not just a number.


Use when

  • You need a break-even view before launching or scaling a product
  • A team is changing price, discount, bundle, or free-shipping policy
  • Paid acquisition is growing but true profitability is unclear
  • Margin pressure is increasing and you need a decision baseline fast
  • Evaluating whether to run a promotion, flash sale, or bundle offer
  • Comparing profitability across SKUs, channels, or fulfillment methods
  • Building a case for price changes or cost reductions

Do not use when

  • You need full accounting, tax treatment, or cash-flow modeling
  • Core inputs are missing and nobody can provide reasonable assumptions
  • The task is valuation, forecasting, or board-level finance reporting
  • You only want gross revenue math without cost realism
  • Legal or compliance-sensitive financial reporting is required

Inputs

Gather these inputs — mark any assumptions explicitly:

Revenue side:

  • Selling price (or price range if testing)
  • Average order value (AOV) if bundling
  • Expected discount % or coupon structure

Variable costs per unit:

  • COGS / unit cost (landed cost including freight to warehouse)
  • Shipping to customer (outbound)
  • Packaging & pick-pack-ship labor
  • Payment processing fees (typically 2.5–3.5%)
  • Platform/marketplace fees (e.g., Amazon 15%, Shopify Payments 2.9%)
  • Return/refund rate and cost per return

Acquisition costs:

  • Ad spend or budget
  • Target or actual CPA (cost per acquisition)
  • Target or actual ROAS
  • Organic vs paid traffic mix if known

Fixed costs (if relevant):

  • Monthly overhead (warehouse, tools, staff)
  • Subscription/platform fees
  • Content/creative production costs

See references/cost-breakdown-guide.md for detailed cost taxonomy.


Workflow

1. Separate variable costs from fixed costs

This is the most common error. Be explicit about what scales with volume and what doesn't.

Variable (per-unit):

  • COGS, shipping, packaging, payment fees, platform fees, returns

Fixed (per-period):

  • Rent, salaries, SaaS tools, insurance, loan payments

Semi-variable (step functions):

  • Warehouse staff (fixed per shift, but add shifts at volume thresholds)
  • 3PL fees (often tiered)

Use the cost classification in references/cost-breakdown-guide.md to ensure nothing is missed.

2. Calculate contribution margin

Contribution Margin (CM) = Selling Price - Total Variable Costs per Unit
CM% = CM / Selling Price × 100

Include ALL variable costs:

  • COGS
  • Outbound shipping (if seller-paid)
  • Packaging + pick-pack
  • Payment processing (% of selling price)
  • Platform/marketplace fees (% of selling price)
  • Return cost allocation = (return rate × cost per return)

3. Calculate break-even points

Break-even units (with fixed costs):

BE Units = Fixed Costs / CM per unit

Break-even CPA:

BE CPA = CM per unit (before ad spend)

This is the maximum you can pay to acquire a customer and still break even on first order.

Break-even ROAS:

BE ROAS = Selling Price / (Selling Price - CM + CPA target)

Or more simply:

BE ROAS = 1 / (CM% before ad spend)

4. Run sensitivity analysis

Model how the break-even shifts when key inputs change. Focus on the variables the team can actually control:

Variable Test range Impact on
Selling price ±10–20% CM, BE units, BE ROAS
COGS ±5–15% CM, BE units
Ad CPA ±20–50% Profitability, scale room
Return rate ±3–10pp CM, effective margin
Discount depth 10/15/20/25% off CM, BE units, BE ROAS
Shipping policy Paid vs free vs threshold CM, conversion rate

Use references/sensitivity-template.md for structured output.

5. Translate to decisions

Don't just output numbers. Frame results as decisions:

Result Decision framing
CM > 40%, BE CPA has room Scale: Increase ad spend, test new channels
CM 20–40%, tight CPA room Optimize: Reduce COGS, improve conversion, test pricing
CM \x3C 20% Hold: Don't scale until unit economics improve
Discount breaks BE Don't discount: Use value-adds instead of % off
Free shipping kills margin Set threshold: Offer free shipping above $X AOV
High return rate crushing CM Fix product/listing: Returns are a product/expectation problem

6. Quality-check the model

Before presenting results, verify with assets/model-checklist.md:

  • Are all variable costs included?
  • Are assumptions labeled with confidence levels?
  • Does the model account for returns?
  • Is payment processing calculated on selling price (not COGS)?
  • Are platform fees applied correctly?

Output

Return a structured analysis package (see references/output-template.md):

  1. Assumptions table

    • Every input listed with source (actual data vs estimate vs industry benchmark)
    • Confidence flag: ✅ confirmed / ⚠️ estimated / ❓ assumed
  2. Unit economics breakdown

    • Revenue per unit → all variable costs → contribution margin
    • Show each cost line, not just totals
  3. Break-even results

    • Break-even units per month
    • Break-even CPA
    • Break-even ROAS
    • Current margin vs break-even margin
  4. Sensitivity analysis

    • 2–3 scenarios showing how key variables shift break-even
    • Highlight which variable has the strongest impact
  5. Decision recommendation

    • Launch / Hold / Scale / Optimize
    • Specific actions based on the numbers
    • Risk flags (e.g., "margin too thin for discounting")

Quality bar

Strong output should:

  • Show all math explicitly — no black boxes
  • Keep variable and fixed costs clearly separated
  • Include return/refund impact (most calculators ignore this)
  • Label every assumption with confidence level
  • Frame results as decisions, not just numbers
  • Help teams avoid "fake-profit" decisions

What "better" looks like

Better output goes beyond "your break-even is X units." It helps decide:

  • Whether the offer is viable at current costs
  • How much ad spend room exists before break-even
  • Whether discounting breaks the model
  • Which cost lever matters most (COGS? Shipping? Returns?)
  • Whether the business is near scale-ready or still too fragile
  • What would need to change to make the unit economics work

Examples

Example 1: DTC skincare product

Inputs:

  • Selling price: $45
  • COGS: $8.50
  • Shipping: $5.50
  • Packaging: $2.00
  • Payment processing (3%): $1.35
  • Platform fees: $0 (own Shopify store)
  • Return rate: 8%, cost per return: $7

Calculation:

  • Return cost allocation: 8% × $7 = $0.56/unit
  • Total variable cost: $17.91
  • Contribution margin: $27.09 (60.2%)
  • Break-even CPA: $27.09
  • If actual CPA is $18 → $9.09 profit per order → Scale

Example 2: Amazon marketplace electronics

Inputs:

  • Selling price: $29.99
  • COGS: $11.00
  • FBA fees: $5.50
  • Amazon referral (15%): $4.50
  • Return rate: 12%, cost per return: $9

Calculation:

  • Return cost allocation: 12% × $9 = $1.08/unit
  • Total variable cost: $22.08
  • Contribution margin: $7.91 (26.4%)
  • Break-even CPA: $7.91
  • If PPC CPA is $6.50 → only $1.41 profit per order → Optimize before scaling

Common mistakes

  1. Forgetting payment processing fees — 2.5–3.5% of every sale adds up fast
  2. Ignoring return costs — A 10% return rate with $8 return cost = $0.80/unit drag
  3. Using gross margin instead of contribution margin — Gross margin excludes shipping, fees, returns
  4. Not modeling discounts through — A 20% discount on a 30% margin product leaves only 10% margin
  5. Treating CPA as fixed — CPA rises as you scale (diminishing returns on ad spend)

Resources

  • references/output-template.md — Structured output format
  • references/cost-breakdown-guide.md — Comprehensive cost taxonomy for ecommerce
  • references/sensitivity-template.md — Sensitivity analysis framework
  • assets/model-checklist.md — Pre-delivery quality checklist
Usage Guidance
This skill is internally consistent and lightweight: it provides templates and formulas and does not request credentials or install code. Before using, ensure your inputs (COGS, return rates, fixed costs, CPA) come from reliable sources and label confidence levels as recommended; do not treat these models as formal accounting/tax or cash-flow forecasts. If you plan to automate feeding sensitive financial data into any agent, review data handling and storage policies first (this skill itself does not include code to store or transmit data).
Capability Analysis
Type: OpenClaw Skill Name: break-even-calculator-ecommerce Version: 1.1.0 The skill bundle is a well-structured set of markdown instructions and templates designed to guide an AI agent through ecommerce financial analysis. It contains no executable code, network requests, or attempts to access sensitive system data, focusing entirely on mathematical modeling and decision-making logic across files like SKILL.md and various reference templates.
Capability Assessment
Purpose & Capability
The name/description (ecommerce break-even modeling) aligns with the contents: formulas, templates, cost taxonomy, sensitivity templates and a model checklist. There are no unrelated requirements (no cloud credentials, no unusual binaries) that would contradict the stated purpose.
Instruction Scope
SKILL.md and supporting files strictly describe data inputs, calculation steps (contribution margin, BE units, BE CPA/ROAS), sensitivity analysis, and decision framing. The instructions do not ask the agent to read arbitrary files, access system state, or send data to external endpoints beyond producing the model output.
Install Mechanism
No install spec and no code files — this is instruction-only. That minimizes on-disk execution risk; nothing is downloaded or installed.
Credentials
The skill requests no environment variables, credentials, or config paths. All required inputs are business metrics the user is expected to provide manually; there are no disproportionate secret or system access requests.
Persistence & Privilege
The skill is user-invocable and not marked always:true. It does not request persistent presence or modify other skills/config. Autonomous invocation is allowed by platform default but combined with the low footprint here it does not introduce additional concerns.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install break-even-calculator-ecommerce
  3. After installation, invoke the skill by name or use /break-even-calculator-ecommerce
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
Version 1.1.0 — Adds expert reference materials, sensitivity modeling, and quality controls for more actionable ecommerce break-even analysis. - Added detailed cost taxonomy and sensitivity analysis templates for advanced modeling (`cost-breakdown-guide.md`, `sensitivity-template.md`). - Introduced a model checklist to help ensure input completeness and calculation accuracy (`model-checklist.md`). - Expanded SKILL.md with step-by-step workflow, quick reference tables, confidence labeling, and quality benchmarks. - Enhanced guidance for framing outputs as actionable launch/hold/scale decisions with risk flags. - Updated inputs, outputs, and use cases for greater clarity and decision support.
v1.0.2
Upgrade skill structure for launch and scale decision use, thresholds, and action framing.
v1.0.1
Add interactive clarification and Python-script workflow guidance
v1.0.0
Initial release
Metadata
Slug break-even-calculator-ecommerce
Version 1.1.0
License MIT-0
All-time Installs 5
Active Installs 4
Total Versions 4
Frequently Asked Questions

What is Break Even Calculator Ecommerce?

Calculate ecommerce break-even thresholds using price, cost, shipping, ad spend, and overhead assumptions so operators can make launch and scale decisions wi... It is an AI Agent Skill for Claude Code / OpenClaw, with 661 downloads so far.

How do I install Break Even Calculator Ecommerce?

Run "/install break-even-calculator-ecommerce" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Break Even Calculator Ecommerce free?

Yes, Break Even Calculator Ecommerce is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Break Even Calculator Ecommerce support?

Break Even Calculator Ecommerce is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Break Even Calculator Ecommerce?

It is built and maintained by LeroyCreates (@leooooooow); the current version is v1.1.0.

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