The most neglected price in your store
Brands agonise over product pricing and then set shipping at $4.99 because a competitor did. Yet shipping price is one of the highest-leverage numbers in your P&L: the parcel costs you the same regardless, so every dollar of shipping charge drops straight to contribution margin. On a store doing $50 orders with a few dollars of real profit per order, a $2 change in shipping price can move total profit more than a 10% change in product price, in either direction.
Conversion is the counterweight. Shipping cost is the most-cited reason for cart abandonment, and customers judge it against the order value: $7 shipping on a $150 cart is a shrug, while the same $7 on a $40 cart feels like a penalty. The useful question is what each price earns after the conversion it costs.
How the calculator works, and what it refuses to guess
Most shipping calculators pretend to know how price changes conversion. This one deliberately does not: you supply the conversion rate for each price, ideally from real tests, and the tool does the profit arithmetic:
sessions × CVR(P) × (AOV + P − COGS − carrier − CAC − other costs)
Every scenario is compared against your current setup, your price at your measured conversion rate, so "vs current" always means something real. (The original version of this tool compared everything against whichever row happened to be listed first, which changed the answer when you reordered rows. The rebuilt model pins the baseline explicitly.)
The most useful number it produces is the break-even conversion rate for each candidate price: the CVR at which that price exactly matches your current profit. Cheaper shipping must gain at least that much conversion; more expensive shipping can afford to lose down to it. That single number turns a vague fear ("raising shipping will kill conversion") into a testable claim: conversion would have to fall below 2.2% for $9.99 to lose.
A worked example: when free shipping is a trap
The example store has a $53 AOV and $52 of true per-order costs (product, carrier, ads, fees). Product economics alone leave about $1 per order; the $6.99 shipping charge is where the actual profit lives, taking each order to $7.99 and the month to about $240.
Free shipping converts best in the example (3.2% against 3.0%) and still collapses the month to $32, because each order now earns $1. To match the current setup, free shipping would need a 24% conversion rate, eight times reality. Going the other way, $9.99 shipping at a conservative 2.7% conversion earns about $297, which is 24% more profit, and stays ahead as long as conversion holds above 2.18%. On thin margins, shipping revenue carries the whole P&L.
Those are one store's numbers, not a universal law. With fat margins and a conversion-sensitive audience, free shipping genuinely wins. The tool exists so that your numbers decide, not the industry's favourite slogan.
Getting real conversion data per price
The model is only as good as the CVR column, so treat those cells as measurements to collect, not opinions to type:
- A/B test the checkout if your platform or a script allows shipping price experiments. This is the strongest evidence.
- Geo split: run the candidate price in one comparable region for 2–4 weeks and compare conversion against the rest.
- Before/after with guardrails: switch the price for a full business cycle, avoid promo periods and season changes, and demand a few hundred orders per side before you believe the delta.
- Sanity-check direction: conversion should generally fall as shipping rises. The tool warns you if your inputs claim otherwise; keep them only if a real test produced them.
Perception guardrails: the badges flag prices that are high relative to AOV for your product type: roughly, keep shipping under 10% of order value for standard products, up to 15% for small items only if you must, and 20 to 30% for bulky goods where the cost is visibly real. A price can win the spreadsheet and still corrode trust; the guardrails keep the maths honest about that.
Assumptions and limitations
- Flat prices only. Each scenario is one charge for every order. For "free over $X" offers, use the Free Shipping Threshold Calculator, since threshold economics are a different model.
- AOV held constant across prices. In reality, paid shipping slightly discourages small orders (nudging AOV up), a second-order effect this model skips.
- One carrier cost per order. Zone- and weight-based rate cards are averaged into a single number.
- Your CVR estimates carry the model. The break-even column exists precisely so you know how much estimation error each recommendation survives.
Change shipping price the way you'd change product price: with a test, a guardrail metric and a rollback plan. If you would like help designing that test, or a full pricing and offer review, talk to us.
