Dropshipping ROI Calculator

The maths behind it

Return on investment here is profit divided by the money actually put at risk, which in this model is the advertising spend rather than inventory: ROI = net profit ÷ ad spend.

That is a different question from ROAS, which measures revenue against spend. ROI measures what you keep against what you risked.

ROI = (contribution − ad spend) ÷ ad spend, or equivalently (ROAS × contribution margin) − 1.

Putting numbers to it

$2,400 of ad spend producing 200 orders at $29.99: $5,998 of revenue, a 2.5 ROAS.

Contribution before advertising is 200 × $17.72 = $3,544. Subtract the $2,400 of spend and net profit is $1,144.

ROI is 1,144 ÷ 2,400 = 47.7%. Every dollar of advertising returned $1.48.

At a 2.0 ROAS the same arithmetic gives contribution of $2,835 against $2,400 of spend, an ROI of 18%. At 1.69 ROAS it is zero, which is the break-even established earlier from the other direction.

Where it is unreliable

ROI on ad spend alone ignores the fixed costs and the testing spend on failed products, both of which are real money at risk. Including them typically halves the figure.

It also treats the money as returning within the period, which the settlement delay contradicts. The cash comes back a week or more after it went out, and that gap has to be funded.

How to act on this

Calculate ROI across a full month including testing losses and fixed costs, not per winning campaign. The campaign figure describes an advertisement; the monthly figure describes the business.

Then compare it against the alternative uses of the same capital and effort. A 20% monthly ROI on $2,400 is $480, which is a real return and worth knowing in absolute terms as well as as a percentage.

Compounding, and why this model can scale quickly when it works

Because the capital cycle is short: spend today, receive in a week, spend again, a working campaign compounds far faster than an inventory business where capital is committed for months.

A 20% return on a weekly cycle, reinvested, is a rate of growth no inventory-holding business can match, which is the genuine attraction of the model and the reason successful operators scale so visibly.

The constraint is that the return rate falls as spend rises: audiences saturate, acquisition costs climb, and the ROI that held at $150 a day rarely holds at $1,500. Modelling scale on a constant ROI is the most common way growth projections in this model fail, and the honest version assumes the rate decays as the spend grows.

One further point on measurement: ROI should be calculated on cash actually returned rather than revenue recognised, given the settlement delay and the refunds that arrive later.

A month that looks like a 40% return on an accrual basis frequently looks like 25% once the refunds belonging to it have been processed, and the second figure is the one that funds the next month's advertising.

Return on investment calculated per order flatters the model, because the investment per order is small. The figure that matters is return on the advertising spend that produced the order, since that is where nearly all the money goes. A 300% return on a £4 cost of goods means very little when the click that generated it cost £11.