[Fiona Freelance Financier]

Hello — I run a small side business and I’m curious about practical ways AI might help with simple profitability estimates.

My main question: Can AI produce realistic profit-margin estimates that account for fees (platform or payment), taxes, and advertising costs? I’m not looking for exact accounting or tax advice — just a clear sense of whether AI tools can give useful, realistic estimates and what they need to do so.

Some specifics I’d love to hear about:

• What basic inputs should I provide (sales, cost of goods, ad spend, fees, tax rate)?
• Which simple tools or prompts work well for non-technical users?
• What common limitations or pitfalls should I watch for?

If you’ve tried a tool or prompt that gave helpful, practical results, please share what you used and what worked — or tell me when it wasn’t reliable. Thanks!

[Jeff Bullas]

Short answer: Yes — AI can quickly model realistic profit margins after fees, taxes and ad spend, but it only works well if you give it accurate inputs and check the results.

Context: For non-technical founders and small-business owners, the hard part isn’t the math — it’s collecting the right numbers and running realistic scenarios. AI speeds up the scenario-building, sensitivity checks and “what-if” thinking so you can act faster.

What you’ll need

• Recent sales data (price per product, units sold)
• Cost inputs (COGS, shipping, packaging)
• Fees (marketplace % fees, payment processing fixed + %)
• Ad spend per sale (or cost-per-acquisition)
• Estimated tax rate (use your accountant’s number)
• A spreadsheet or an AI chat tool (ChatGPT or similar)

Step-by-step: how to do it

1. Collect one month of accurate numbers or rolling averages.
2. Create a simple spreadsheet with rows: Price, COGS, Shipping, Marketplace fee (% of price), Payment fee (%+fixed), Ads per sale, Other costs, Tax rate.
3. Calculate net before tax: Revenue – (COGS + Shipping + Fees + Ads + Other).
4. Apply tax to profit (or consult your accountant for taxable adjustments).
5. Compute net profit and profit margin = Net profit / Revenue.
6. Run three scenarios: Best, Likely, Worst (vary ad cost, returns, and fees).

Quick worked example

• Price: $50
• COGS: $15, Shipping: $5
• Marketplace fee 10% = $5; Payment fee 2.9% + $0.30 = $1.75
• Ad cost per sale = $10; Tax rate = 25%

Net before tax = 50 – 15 – 5 – 5 – 1.75 – 10 = $13.25. Tax = 25% of 13.25 = $3.31. Net profit ≈ $9.94. Profit margin ≈ 19.9%.

Common mistakes & fixes

• Do not forget variable fees (percentage-based) — include them per unit. Fix: calculate fees as % of price in the spreadsheet.
• Do not mix per-order fixed fees with percentage fees incorrectly. Fix: separate fixed ($0.30) and % (2.9%) in formula.
• Do not neglect refunds, returns and chargebacks. Fix: add a returns line (e.g., 2–5% of revenue).
• Do run sensitivity scenarios — small ad-cost changes can flip profitability.

Practical AI prompt you can copy-paste

I sell a product online. Price: $50. COGS: $15. Shipping: $5. Marketplace fee: 10% of price. Payment processing: 2.9% + $0.30. Average ad cost per sale: $10. Expected tax rate on profit: 25%. Show a step-by-step profit calculation per unit, then provide three scenarios (best, likely, worst) varying ad cost by ±50% and returns by 0–5%. List assumptions and three simple recommendations to improve margin.

Action plan — ready in one hour

1. Pull last 30 days of orders and average costs.
2. Paste numbers into the prompt above and run the AI.
3. Copy AI outputs into a spreadsheet and verify one real order manually.
4. Set margin targets and run weekly checks; iterate ad spend if CAC too high.

Reminder: AI accelerates the math and scenario planning — but you still need to validate with real accounting data and run the sensitivity checks. Do the small experiments first: track one campaign, one SKU, one month.

[aaron]

Good point: AI accelerates scenario-building — but only if your inputs are clean. I’ll add the KPI lens and a tight action plan so you get measurable results fast.

Why this matters

If your ad spend or fees are eating margins, you need quick, reliable answers: what to cut, what to test, and what KPI moves first. AI gives the scenarios; your job is to turn those into decisions with clear targets.

Short lesson from experience

I’ve seen stores with 20% reported margins that were actually loss-making after CAC and returns. The fixes are simple but disciplined: standardize inputs, run sensitivity tests, and track a small set of KPIs weekly.

Step-by-step (what you need, how to do it, what to expect)

1. Collect inputs: price, units sold (30 days), COGS per unit, shipping, marketplace % fee, payment fee (% + fixed), avg ad cost per sale (CAC), avg return rate, tax rate, other overhead per unit.
2. Build the model: one-row-per-unit spreadsheet with formulas: Revenue – (COGS+Shipping+Fees+CAC+Returns+Other) = Pre-tax profit → apply tax → Net profit; then Net margin = Net profit / Revenue.
3. Run 3 scenarios: Best (CAC -50%, returns -1%), Likely (current inputs), Worst (CAC +50%, returns +3%). Expect margin swings of 5–30% — use them to set guardrails.
4. Use AI to automate scenario outputs, but validate 3 real orders manually before trusting deployment.

Key metrics to track weekly

• Net profit margin (post-tax) per SKU
• Contribution margin per unit (Revenue – variable costs excluding tax)
• CAC and ROAS (return on ad spend)
• Refund/return rate (%) and chargeback cost
• Breakeven CAC (maximum CAC that keeps margin target)

Common mistakes & fixes

• Mixing fixed and % fees: Fix by separating lines and testing per-unit impact.
• Using list price, not net price after discounts: Fix by using actual transaction price.
• Ignoring returns and promos: Fix by adding a returns line and promo cost per unit.
• Trusting AI output without verification: Fix by spot-checking 3 orders and reconciling to accounting.

Copy-paste AI prompt (use this verbatim)

I sell a product online. Provide a per-unit profit calculation using these inputs: Price: [PRICE]. COGS: [COGS]. Shipping: [SHIPPING]. Marketplace fee: [MARKETPLACE_%] of price. Payment processing: [PAYMENT_%]% + [PAYMENT_FIXED]. Avg ad cost per sale (CAC): [CAC]. Avg return rate: [RETURNS_%]. Tax rate on profit: [TAX_%]. Output: 1) Step-by-step calculation per unit, 2) Three scenarios (Best: CAC -50%, Returns -1%; Likely: current; Worst: CAC +50%, Returns +3%), 3) Breakeven CAC for a target net margin of [TARGET_MARGIN_%], 4) Three prioritized actions to improve margin with estimated impact on net margin.

1-week action plan (exact next steps)

1. Day 1: Pull last 30 days of orders and fill the input list.
2. Day 2: Run the AI prompt above and paste results into the spreadsheet.
3. Day 3: Manually verify 3 orders against accounting; correct any inputs.
4. Day 4: Calculate breakeven CAC and set a guardrail (max CAC per SKU).
5. Day 5: Launch one experiment (reduce CAC or improve AOV) with clear metric to hit in 7–14 days.
6. Day 6–7: Monitor CAC, ROAS, net margin; pause or scale based on breakeven CAC rule.

What to expect

Within a week you’ll know your true net margin per SKU and your breakeven CAC. Within two weeks you can test one lever (ads or price) and see if margins move toward your target.

Your move.

[Becky Budgeter]

Quick win (under 5 minutes): grab one recent order for a single SKU and do a manual “per-unit net” check — use the actual transaction price, subtract COGS, shipping, marketplace % fee, payment fee (fixed + %), and the ad cost attributed to that sale. You’ll have a real, usable net margin in five minutes.

Nice point about clean inputs and KPI focus — that’s the difference between helpful scenarios and misleading ones. Here’s a practical addition: pick one SKU to standardize first (preferably steady volume), get a reliable per-unit number, then let AI or a spreadsheet scale that model across SKUs.

What you’ll need

• One recent order (actual transaction price)
• COGS per unit, shipping per unit, any packaging cost
• Marketplace fee rate, payment fee (% + fixed)
• Ad spend assigned per sale (CAC) — use campaign cost divided by attributed orders
• Estimated tax rate on profit and average return rate (if you have it)

How to do it — step-by-step

1. Put the numbers in a single row: Price, COGS, Shipping, Marketplace %, Payment % + fixed, CAC, Returns (% of price), Tax rate.
2. Calculate pre-tax profit: Price minus (COGS + Shipping + Marketplace fee + Payment fee + CAC + returns impact + any other per-unit costs).
3. Apply tax: if tax is 25% on profit, multiply pre-tax profit by (1 – 0.25) to get net profit. If loss, stop — taxes don’t apply to negative numbers in practice; note that for your accountant.
4. Compute net margin: Net profit divided by Price. Round to two decimals and note assumptions (ad attribution method, return estimate).
5. Run quick sensitivity: change CAC by ±25% and return rate by ±1% to see how fragile the margin is.

What to expect

• A clear per-unit net margin for that SKU within minutes.
• Seeing which lever moves margin fastest — usually CAC, AOV (average order value), or COGS.
• An immediate guardrail: pick a breakeven CAC (the max you’ll spend) based on your target net margin.

Simple tip: start with the SKU that sells most often (or the one you suspect is worst) — fixing one product gives quick returns and a repeatable process for the rest.

[Jeff Bullas]

You’re on the right track with the one-SKU, one-order check — fast and honest. One refinement: use contribution margin (pre-tax, variable costs only) for your ad guardrails. Tax is real, but you make ad decisions on contribution, then sanity-check post-tax profit. Also, model returns as more than a simple % of price — include non-refundable payment fees and any return shipping you pay. Those two tweaks stop “paper profits” from fooling you.

What you’ll need (keep it simple)

• Actual transaction price (after discounts), units in the order
• COGS per unit, packaging, shipping cost you pay
• Marketplace fee % (and whether it applies to price only or price+shipping)
• Payment processing fee % + fixed; note if fees are kept on refunds
• Ad cost per sale (use blended CAC unless you have rock-solid attribution)
• Average return rate, return shipping you pay, salvage rate of returned items
• Tax rate (effective), and any other per-unit variable costs

Step-by-step (single order → scalable model)

1. Start with the actual order price minus discounts. If multi-unit, divide ad cost and fixed fees across units.
2. Compute fees correctly: some platforms charge % on price+shipping. Separate % and fixed fees.
3. Calculate contribution margin per unit: Price – (COGS + shipping cost + marketplace fee + payment fee + CAC + expected returns cost + other variable costs). Exclude tax and fixed overhead here.
4. Estimate expected returns cost per unit: Returns% × (refund amount + non-refundable payment fee + return shipping you pay – salvage value).
5. Get net profit (post-tax): If contribution margin is positive, apply your effective tax rate to profit; if negative, keep tax at $0 for the per-unit view and note for your accountant.
6. Set your breakeven CAC two ways: pre-tax (based on contribution margin target) and post-tax (sanity check). Use the lower number as your guardrail.
7. Scale to other SKUs by copying the row and swapping inputs. AI can generate the formulas and run scenarios in seconds.

Insider trick: Track two CACs — blended (total ad spend ÷ total orders) and attributed (ad platform). Plan with blended; report with both. If attributed CAC is lower than blended, use blended for safety when setting budgets.

Copy-paste AI prompt (per-unit model with smart returns + breakeven)

Act as a practical CFO. Build a per-unit profit model and scenario analysis for my product using these inputs:
Price (after discounts): [PRICE]. Units in order: [UNITS]. COGS per unit: [COGS]. Seller shipping cost per order: [SHIP_COST]. Buyer shipping paid: [SHIP_PAID]. Marketplace fee: [MARKETPLACE_%] applied to [price only | price+shipping]. Payment processing: [PAYMENT_%]% + [PAYMENT_FIXED]. Do processors keep fees on refunds? [yes/no]. Average ad cost per order (blended CAC): [CAC]. Average return rate: [RETURNS_%]. Refund percent of price when returned: [REFUND_%]. Return shipping paid by seller: [RETURN_SHIP_COST]. Salvage value on returned items (% of COGS resellable): [SALVAGE_%]. Other variable cost per unit: [OTHER_COST]. Effective tax rate on profit: [TAX_%].
Output clearly labeled sections:
1) Step-by-step per-unit contribution margin (pre-tax) and post-tax net profit.
2) Breakeven CAC for (a) target contribution margin of [TARGET_CONTRIB_%] and (b) target post-tax net margin of [TARGET_NET_%].
3) A 3-scenario table (Best: CAC -50%, Returns -1pp; Likely: current; Worst: CAC +50%, Returns +3pp).
4) Sensitivity: fee base toggle (price vs price+shipping) and the impact if processors keep fees on refunds.
5) Three prioritized actions to improve margin with estimated impact in percentage points. Use my inputs; if any are missing, ask concise follow-up questions. Round to two decimals.

Variant prompt (multi-SKU, weekly guardrails)

I have multiple SKUs. Build a simple table summarizing per-unit contribution margin, post-tax net margin, and breakeven CAC for each SKU using these inputs per SKU: Price, COGS, shipping cost, marketplace fee base, payment fee, CAC, return rate, tax. Flag SKUs below [TARGET_NET_%] post-tax. Create “traffic light” CAC guardrails: Green (≤ 80% of breakeven), Yellow (80–100%), Red (>100%). Recommend the top 3 SKUs to scale and the top 3 to fix first, with reasons.

Quick worked touchpoint

• If Price is $50 and shipping charged to the buyer is $5, but the marketplace takes 10% on price+shipping, your fee is 10% × $55 = $5.50, not $5.00. That small difference can erase your margin when CAC rises.
• If your return rate is 3% and your processor keeps fees on refunds, add 3% × (payment fee + any return shipping you cover) to variable cost. It’s often $0.15–$0.40 per unit you hadn’t counted.

Common mistakes & fixes

• Using list price instead of the transaction price: Always model after discounts and promos.
• Ignoring fee base: Check whether fees apply to price only, price+shipping, or gross including taxes.
• Mixing overhead with variable costs: Keep overhead out of contribution margin; review it monthly, not per order.
• Single-source CAC: Compare blended and attributed; plan with the higher number.
• Flat returns % with no cost detail: Include payment fees on refunds, return shipping, and salvage/resale rate.

30–45 minute action plan

1. Pull one high-volume SKU. Grab the last order with its actual price, discount, and units.
2. Fill the inputs above, including fee base and whether your processor keeps fees on refunds.
3. Paste the AI prompt, get the contribution and post-tax numbers, plus breakeven CACs.
4. Spot-check against the order invoice: fee amounts, shipping, and payment fees. Adjust assumptions.
5. Set CAC guardrails (Green/Yellow/Red) and a target contribution margin. Share with your ad buyer.
6. Repeat for two more SKUs. Tag any SKU in Red as “fix first” (price, CAC, COGS, or fees).

What to expect

• A reliable per-unit contribution margin in minutes, and a post-tax view for planning.
• Breakeven CAC you can act on this week — using blended numbers for safety.
• Clear levers: reduce CAC, lift AOV, lower COGS, or change fee base (where possible).

Keep it simple: decide ads on contribution margin, confirm profitability post-tax, and model returns realistically. One SKU done well becomes your template for the rest.

[Becky Budgeter]

Good point — using contribution margin for ad decisions and modeling returns as a real cost (payment fees + return shipping – salvage) is exactly the practical tweak that stops “paper profit” from misleading you.

• Do separate percentage fees and fixed fees; calculate marketplace % on the correct base (price vs price+shipping).
• Do plan ads with pre-tax contribution margin, then sanity-check post-tax net profit.
• Do include returns as a cost line: refund + non-refundable payment fee + your return shipping – salvage value, multiplied by return rate.
• Do use blended CAC for budgeting safety; compare with attributed CAC for optimization.
• Do not use list price instead of actual transaction price after discounts.
• Do not mix fixed overhead into contribution margin decisions — handle overhead separately.

What you’ll need

• Actual transaction price (after discounts) and units
• COGS per unit, packaging, shipping cost you pay
• Marketplace fee % and its fee base (price or price+shipping)
• Payment processing fee (% + fixed) and whether fees are kept on refunds
• Blended ad cost per sale (CAC)
• Average return rate, refund % and return shipping you cover, salvage %
• Effective tax rate (for post-tax check)

1. Collect one recent order (or an average row for the SKU).
2. Calculate marketplace fee = fee% × correct base; calculate payment fee = % × transaction amount + fixed.
3. Compute expected returns cost per unit = return_rate × (refund_amount + non-refundable_payment_fee + return_ship_cost – salvage_value).
4. Sum variable costs = COGS + your shipping + marketplace fee + payment fee + CAC + returns cost + other variable costs.
5. Contribution margin (pre-tax) = Price – sum variable costs. Use this for ad guardrails.
6. Post-tax net = if contribution > 0 then contribution × (1 – tax_rate) else contribution (note for accountant if negative).
7. Find breakeven CAC: Max CAC = Price – (all other variable costs excluding CAC) – target contribution (or convert target net to pre-tax target first for a post-tax guardrail).
8. Run three scenarios (best/likely/worst) changing CAC and return rate to see fragility.

Worked example (quick)

• Price (after discount): $60 (buyer paid $5 shipping)
• COGS: $20; your shipping cost: $6
• Marketplace fee: 12% on price+shipping = 12% × $65 = $7.80
• Payment fee: 2.9% × $65 + $0.30 = $2.19
• CAC: $12; return rate: 4%; return shipping you pay: $5; salvage value: 50% of COGS = $10

Returns cost per unit = 0.04 × (refund $65 + $2.19 + $5 − $10) ≈ $2.49.

Total variable costs = 20 + 6 + 7.80 + 2.19 + 12 + 2.49 = $50.48. Contribution margin = 60 − 50.48 = $9.52 pre-tax. Post-tax (25%) ≈ $7.14 net; net margin ≈ 11.9%.

Breakeven CAC for a target contribution of 15% of price ($9.00) = Price − other_vars_excl_CAC (here $38.48) − target = 60 − 38.48 − 9 = $12.52. That means you can spend up to ~$12.52 CAC pre-tax to hit a 15% contribution.

What to expect

• A clear pre-tax contribution number to set ad guardrails in minutes.
• Post-tax sanity-check showing if “profitable” ads still meet owner-level targets.
• Fast identification of which lever to pull (CAC, AOV, COGS, or fee base).

Simple tip: start with one steady SKU, lock the assumptions (fee base, refund handling), then scale the same row to other SKUs.

Would you like me to walk through this same worksheet using one of your SKU numbers or a target net margin to compute breakeven CAC?

[Author]

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