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Email Marketing & Outbound Calculators
Five calculators for the numbers email people argue about: what a campaign returns, how many cold emails one meeting actually costs, whether an A/B result is significant or noise, what a recurring meeting costs the company, and what time in the inbox is worth. All free, all in the browser, with the formula shown for each.
Runs entirely in your browser. Nothing you paste is uploaded, logged or stored.
- Revenue
- $1,814
- Profit
- $1,414
- ROI
- 354%
- Conversions
- 15
- Opens
- 4,200
- Clicks
- 504
- Revenue per recipient
- $0.18
- Cost per conversion
- $26.46
ROI = (revenue − cost) ÷ cost · revenue = list × open × click × conversion × AOV
Open rate has been unreliable since Apple Mail Privacy Protection began pre-fetching images in 2021. Treat it as directional; clicks and conversions are the honest inputs.
- Campaign ROI from list size, open, click and conversion rates
- Cold outbound: emails → replies → meetings → closed deals
- A/B significance with a real two-proportion z-test, and an underpowered warning
- Meeting cost, including the annualised cost of a recurring one
- Inbox time cost in hours, working weeks and money
- Every formula written out — no black boxes
How to use it
Four steps, about thirty seconds
- 01
Pick the calculator
Five tabs. Each one starts with sensible defaults so you can see the shape before entering your own numbers.
- 02
Enter your own figures
Use your real rates, not benchmarks. A calculator fed with industry averages tells you about the industry, not about you.
- 03
Read the derived numbers
The useful outputs are the ratios — emails per meeting, cost per conversion, hours per year — not the headline total.
- 04
Check the caveat
Each calculator states where its maths stops being trustworthy, including the sample size below which an A/B result means nothing.
The formulas
Every calculation, written out
No black boxes. Each formula is on screen beside its calculator, and all five are here so you can rebuild them in a spreadsheet if you would rather own the model.
| Calculator | Formula | The number that actually matters |
|---|---|---|
| Campaign ROI | (revenue − cost) ÷ cost | Revenue per recipient. It is comparable across campaigns of different sizes, which ROI on its own is not. |
| Cold outbound | sent × reply% × meeting% × close% | Emails per meeting. Volume is the lever you reach for; the ratio is the lever that compounds. |
| A/B significance | z = (p₂ − p₁) ÷ √(p̄(1−p̄)(1/n₁ + 1/n₂)) | Sample size needed. Decide it before the test, not after the result looks good. |
| Meeting cost | attendees × hours × hourly rate | The annualised figure. A weekly hour is fifty-two of them. |
| Inbox time | (emails × seconds) ÷ 3600 × working days | Working weeks per year. Hours are abstract; weeks are not. |
Inputs
Where these numbers go wrong
A calculator is only as honest as what you feed it, and three inputs are routinely wrong in the same direction.
Open rate. Since Apple Mail Privacy Protection started pre-fetching tracking pixels in 2021, a large share of “opens” are a machine, not a person. Apple Mail is the most-used client in many markets, so the inflation is not a rounding error. Use opens as a trend line, and let clicks and conversions carry the model.
Benchmarks instead of your own rates. Published averages describe an industry, not your list. If you do not know your reply rate, the output of the outbound calculator is a restatement of your assumption, not a forecast.
Hourly cost. For a meeting or inbox-time figure, use salary plus overhead — typically 1.25 to 1.4 times base — not take-home pay. The under-costed version is the one that makes a standing meeting look affordable.
Significance
The one calculator people misuse
The A/B calculator runs a two-proportion z-test, which is what every significance tool in this space uses. Three honest limits, none of which the interface can enforce for you.
It assumes you decided the sample size first. Checking the result repeatedly and stopping when it crosses 95% inflates false positives dramatically — that is why a subject line can “win” twice and then lose. Pick the sample size, run to it, then look.
Below about 30 conversions per arm, the maths drifts. The normal approximation needs a reasonable number of events. The calculator flags it rather than printing a confident number over thin data.
Significance is not importance. With a big enough list, a 0.1-point difference becomes statistically significant and remains commercially irrelevant. Read the absolute lift and ask whether you would change anything for it.
For the list quality underneath all of this, the email list cleaner removes the duplicates and dead addresses that quietly distort every rate above.
Questions
Frequently asked
Short answers, including the ones where the honest answer is “this tool can’t do that”.
(Revenue − Cost) ÷ Cost, where revenue is list size × open rate × click rate × conversion rate × average order value. The widely quoted '$36 back per $1' figure is a survey average across every industry — useful as a sanity check, useless as a forecast for your list.
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