Cold email ROI calculator

The chain of multiplications behind the projection

The model is a straight chain, each step feeding the next. Emails per day times working days per month gives your monthly send volume. That volume times the reply rate gives replies; replies times the positive share gives genuinely interested conversations; those positives times your close rate give deals; deals times average deal value give the projected monthly pipeline shown at the top.

Two details change the arithmetic. The positive-reply percentage is taken out of replies rather than out of sends, so it answers how many of the people who wrote back were actually interested. The close rate is then applied to those positive replies directly, so it has to cover everything between a warm response and signed paperwork. The currency toggle rewrites the deal value and the pipeline figure in dollars or rupees; it converts nothing, it only changes the symbol and the number formatting.

Which input moves the result most

Volume and deal value scale the result linearly — doubling either doubles the pipeline — but the three percentages multiply against each other, which is where the leverage hides. Lifting the reply rate and the positive share by a modest amount each compounds far more than adding another hundred sends a day, and it costs nothing in domain reputation. That is the argument for tightening targeting before buying more mailboxes.

Choosing inputs you can defend

Use figures pulled from your own last quarter wherever you have them, and be pessimistic where you do not. The two figures people most often overstate are the close rate, which quietly absorbs every deal that stalled in procurement, and daily volume, which assumes every mailbox sends its full allowance on every working day without pausing for a bounce spike or a holiday.

It helps to run the model twice — once with the numbers you hope for and once with the numbers you can prove — and to plan against the second.

What the projection deliberately leaves out

This is a projection built from figures you supply, not a forecast. It carries no cost side, so it shows pipeline rather than profit: mailboxes, domains, data and the hours spent answering replies all sit outside it. It also assumes a steady month and assumes every send arrives, when in practice bounces, spam placement and unsubscribes remove part of the volume before anyone reads a word. Treat the output as a sizing exercise, then re-run it on real results once a campaign has been live long enough to replace the guesses.

Can your mailboxes actually send the volume you entered?

The daily volume field is where optimistic models break first. Cold traffic is usually kept to a few dozen sends per mailbox per day, so 500 emails a day implies somewhere between ten and seventeen warmed mailboxes, each of which needs its own ramp before it carries full load. The warmup planner turns a volume goal into the mailbox count and the first day you reach it.

Autocloz's free plan includes 10 mailboxes, and paid plans remove the mailbox limit, so the constraint is warmup time rather than seats. Plug the reachable volume in here, not the volume you would like.

Working days deserve the same care. A month with public holidays, a team offsite or a domain that needed a week's pause after a bounce spike has fewer sending days than the calendar suggests. Enter the days your mailboxes will actually send on, and the pipeline figure stops overstating the month by a week's worth of volume.

How do you turn the projection into a test?

Pick the single percentage you believe you can move, run one campaign against a list you can describe in a sentence, and measure it. The benchmark calculator takes those raw counts and tells you which stage fell short. Once you have a real pipeline figure, set it against what the programme cost with the CAC and LTV calculator to see whether outbound pays for itself at your deal size.