Short answer: yes, but barely, if the $11 per order is right. At $11 you keep about $1.26 per order (4.4%). The 3.1 ROAS on your dashboard doesn't match your own numbers, so check that first.
Cost per order (price 29.00)
| Line | Calculation | Amount |
|---|---|---|
| Revenue kept after refunds | 29.00 × (1 − 6%) | 27.26 |
| Product cost, net of resold returns | 7.50 × (1 − 0.06 × 0.75) | −7.16 |
| Outbound shipping, packaging, pick and pack | 4.20 + 0.60 + 1.00 | −5.80 |
| Payment fees | 29.00 × 2.9% + 0.30 | −1.14 |
| Platform fee | 29.00 × 2% | −0.58 |
| Return shipping and handling | 6% × (4.20 + 1.00) | −0.31 |
| Contribution before ads | 12.26 (42.3% of price) | |
| Ads | −11.00 | |
| Contribution after ads | 1.26 (4.4% of price) |
Two assumptions are mine. Payment and platform fees are not refunded on returns. Resellable returned mugs go back into stock at full cost.
Targets
- Break-even CPA: 12.26 per order.
- Break-even ROAS: 1 / 0.423 = 2.36.
- ROAS for a 10% margin after ads: 1 / (0.423 − 0.10) = 3.10. That is a CPA of about 9.36 at a 29.00 order.
Your ROAS figure doesn't match your ad cost
- At 29.00 per order and 11 in ads, your real ROAS is 29 / 11 = 2.64. That is above break-even (2.36) but well under the 3.10 you need for 10%.
- A dashboard ROAS of 3.1 with 11 per order would mean an average attributed order of about 34.10. That could be multi-item orders or shipping revenue.
- It could also mean the "11 per order" is spend divided by all orders, while the platform's ROAS only counts the orders it claims.
- If the 3.1 is real on a 29.00 order, ads cost about 9.35 per order, and you'd be right at a 10% margin.
- Use total revenue divided by total ad spend for the period, not the platform's number. That is your blended ROAS, and it's the one to compare to 2.36 and 3.10.
Sensitivity (change in profit per order)
- Price ±10% (±2.90): about ±2.58. This is the biggest lever. A 10% discount deeper than your usual one would wipe out your profit.
- Ad cost ±10% (±1.10): ±1.10.
- Product cost ±10% (±0.75): about ±0.72.
- Return rate +1 point: about −0.29. The refunded revenue matters far more than the return shipping.
At 4.4% net, a small rise in ad cost or returns pushes you to break-even. The ad cost per order is the number to pin down.
What to verify
- Blended ROAS and cost per order over a period with a decent number of orders. I don't know how many orders your 11 rests on.
- Whether your payment processor refunds its fee on returns. If it does, you do slightly better than shown.
- Whether the 6% return rate and the 25% unsellable share come from real data.
- Whether you charge shipping to customers. I assumed none.
- Cash flow, since you buy the stock before you sell it.
If you can get ad cost per order down to about 9.35, or raise the price about 2 to 3 dollars, you reach 10%. I can run either scenario.
Real run in an isolated folder with only this skill installed. Only the Skill and Read tools were enabled, so no command was run and no file was written. The store and all numbers are invented and the model had no access to a real store. The per-order figures were checked by hand afterwards. The currency symbol in the answer is the model's own assumption: the request gave no currency.
What it does
Works out the profit of one order from the seller's own numbers: revenue kept after refunds, product cost (returned goods that can be resold go back to stock), shipping, packaging, pick and pack, payment and platform fees, and return costs. From that contribution margin it derives the break-even CPA and ROAS, the target ROAS for a margin you want, and the price needed for a target margin. It judges discounts by recomputing the whole model at the new price and reporting the sales increase needed to earn the same total profit, and it builds customer lifetime value from margin and measured repeat rates, never from revenue or assumed ones.
Good habits built in
It never invents a number, says where each input came from, flags platform-reported ROAS that does not match the order value, shows a sensitivity check (price, ad cost, product cost, return rate) and lists what to verify. A reference file holds the formulas and a worked example.
Good for
Checking whether a product or ad campaign makes money, setting prices, and deciding how far to discount.
Low risk: pure instructions with no scripts, no network access and no file writes. It is a calculation method, not tax, accounting or legal advice; fee rates, tax treatment and platform rules differ by country and change, so they are inputs you must confirm. Results are only as good as the numbers you provide and are not forecasts. It cannot see your store data, and it says which inputs are assumptions. In the trial the model added a currency symbol on its own, although none was given. Original skill by AIBars (MIT). Tried once on an invented mug store.