What the model actually represents
Email revenue is not a rate applied to a list; it is a survival curve. Of everyone you send to, some messages never arrive, most of those that arrive are never opened, a minority of openers click, and a small share of clickers buy. Each stage multiplies the last, so the final order count is the product of four fractions — and because fractions multiply to smaller fractions, a campaign that looks healthy at every individual stage can still produce very few orders.
That structure is why the model is useful for diagnosis and not only for forecasting. If the send under-performs, the stage table tells you which multiplication failed. Poor delivery is an infrastructure problem: authentication, list hygiene, reputation. A weak open rate is a subject line, sender name and timing problem. A weak click-to-open rate is a body copy and offer problem — the reader wanted to look and did not want to act. A weak conversion rate is a landing page problem, and it is the one most likely to be inherited from outside the email team.
Two conventions matter enough to state explicitly. First, the click term here is click-to-open rate: unique clicks divided by unique opens. Many platforms also report a plain "click rate" against delivered messages, and the two differ by exactly the open rate. If you feed a delivered-based click rate into the CTOR field you will double-count the open stage and understate revenue by that factor. Second, ROI is computed against gross margin, not revenue, because the campaign is paid for out of what is left after cost of goods.
The model treats every order as a one-off. For businesses where an email-acquired customer buys repeatedly, the honest revenue figure is closer to customer lifetime value than to one order, and the campaign will look considerably better on that basis. Use the single-order version for a promotional send to an existing list and the lifetime version for acquisition, but never mix the two inside one comparison.
Each stage, and what moves it
Delivery rate is accepted messages divided by sent. It is governed by authentication (SPF, DKIM and a DMARC policy that aligns), by list hygiene, and by complaint rate. Below about 95% you have a technical problem rather than a creative one, and no amount of subject-line work will recover the revenue lost before the message arrives.
Open rate is unique opens divided by delivered. It is the least trustworthy number on this page. Open tracking works by loading a one-pixel image, and privacy features that pre-fetch remote images register an open whether or not a human ever looked. Rates inflated this way are not a reporting bug you can subtract out, because the inflation varies with the mailbox mix of your list. Treat open rate as a directional signal for subject lines within one list, never as a cross-company benchmark.
Click-to-open rate is the cleanest creative metric you have, precisely because both its numerator and denominator come from actual interactions with the message. When you test a body layout, a hero image or an offer, CTOR is the number to read.
Conversion rate is orders divided by clicks, measured on the destination page. It belongs to the landing experience, not to the email, which is why the same campaign sent to a product page and to a category page can produce different revenue from identical clicks. The conversion rate calculator handles the measurement side; the number that matters here is the one for this campaign's traffic, not your site average.
Revenue per email sent collapses the whole chain into one figure, and it is the right yardstick for comparing sends. A 20,000-message segment generating $0.28 per email is worth more attention than a 200,000-message blast generating $0.03, even though the second one produces more total revenue — because the first tells you a segment exists that is over nine times more responsive (0.28 ÷ 0.03 ≈ 9.3), and segments can be grown.
Worked example: a 100,000-message promotional send
You send a promotion to 100,000 subscribers. Delivery is 100% for simplicity, 20% open, 10% click-to-open, 5% of clickers buy, average order value is $100, the send cost $1,000 in ESP charges and creative, and the product carries a 100% gross margin.
- Delivered. 100,000 × 100% = 100,000.
- Opens. 100,000 × 20% = 20,000.
- Clicks. 20,000 × 10% = 2,000. Note this is 2% of delivered messages — the delivered-based click rate — which is what you would compare against your ESP's click-rate column.
- Orders. 2,000 × 5% = 100.
- Revenue. 100 × $100 = $10,000.
- Revenue per email sent. $10,000 ÷ 100,000 = $0.10.
- ROI. Gross margin is $10,000 × 100% = $10,000. ROI = ($10,000 − $1,000) ÷ $1,000 × 100 = 900%.
- Break-even. Each order contributes $100 × 100% = $100 of margin, so the send needs $1,000 ÷ $100 = 10 orders to cover itself. It produced 100.
Now change one stage. Raising click-to-open from 10% to 12% — a 20% relative improvement, since 12 ÷ 10 = 1.2 — raises clicks to 2,400, orders to 120 and revenue to $12,000, also 20% more. Every stage has that same proportional property, which is what makes revenue per email the fair comparison: it is the product of all four rates times order value, so improving any one rate by 20% moves it by 20%.
How to judge the result
Judge ROI first and revenue second, because a large send can produce large revenue while losing money. The break-even order count on this page is the blunt version of the same test: if the campaign needs more orders than it can plausibly produce, redesign the offer rather than the subject line.
Then judge revenue per email sent against your own history for a comparable segment and offer. This metric is the reason to keep a record of every send in one table — over a year it produces a distribution you can position a new campaign inside, which is far more informative than any published average. Published email benchmarks are especially unreliable because open rates across the industry were structurally disrupted by mail privacy proxies, so figures gathered before and after that shift are not comparable at all.
Look at the stage table for the diagnosis. The stage with the largest absolute drop is not automatically the one to fix — the fair question is which stage is furthest below what you have achieved before on similar sends. A 20% open rate on a list that normally opens at 35% is a bigger opportunity than a 3% conversion rate that has never been higher than 3.2%.
Finally, weigh the cost of the send against its effect on the list. Frequency raises total revenue and raises unsubscribe and complaint rates at the same time, and the second effect is a permanent reduction in the asset you are measuring. A campaign that earns $3,000 today while costing you 2,000 subscribers has borrowed against future sends, and neither ROI nor revenue per email will show that. Track list attrition alongside these figures — the churn rate calculator applies the same arithmetic to subscribers as it does to customers.
Revenue per email sent, by stage rates (at $75 average order value)
| Open rate | CTOR | Conversion | Orders per 10,000 sent | Revenue per email |
|---|---|---|---|---|
| 20% | 10% | 2% | 4.0 | $0.030 |
| 25% | 12% | 2% | 6.0 | $0.045 |
| 30% | 12% | 3% | 10.8 | $0.081 |
| 35% | 15% | 3% | 15.75 | $0.118 |
| 40% | 15% | 4% | 24.0 | $0.180 |
| 45% | 18% | 5% | 40.5 | $0.304 |
| 50% | 20% | 5% | 50.0 | $0.375 |
The rows are illustrative combinations, not benchmarks. The point of the table is the leverage: the bottom row's rates are roughly 2.5, 2 and 2.5 times the top row's, and the revenue per email is 12.5 times larger, because the three factors multiply.
Mistakes that make the projection wrong
- Feeding a delivered-based click rate into the CTOR field. The two differ by exactly the open rate. Using a 2% delivered click rate where a 10% CTOR belongs understates revenue five-fold at a 20% open rate.
- Trusting open rate as a human signal. Privacy proxies pre-fetch tracking pixels, registering opens nobody made. Use opens to compare subject lines within one list and never as a benchmark against another company.
- Measuring ROI against revenue. The campaign is paid for out of gross margin. A 25%-margin retailer needs four dollars of revenue to cover one dollar of cost.
- Counting only the ESP fee as the cost. Creative, copy, design, offer discount and incentive cost all belong in the campaign cost, and the discount in particular reduces both order value and margin.
- Ignoring the unsubscribe cost. Every send permanently reduces the list. High-frequency programmes can post good per-campaign ROI while shrinking the asset that generates it.
- Applying a site-wide conversion rate. Email traffic converts differently from paid or organic traffic, and a promotional segment converts differently from a newsletter list.
Compliance, deliverability and the wider picture
Commercial email in the United States is governed by the CAN-SPAM Act, 15 U.S.C. §7701 et seq., enforced by the Federal Trade Commission. Its substantive requirements are concrete: no deceptive headers or subject lines, a clear identification of the message as an advertisement where applicable, a valid physical postal address, a working opt-out mechanism, and honouring opt-outs within ten business days. Note that CAN-SPAM does not require prior consent — but the mailbox providers do, in practice, because complaint rates drive filtering, and other jurisdictions do require it as law. If you mail into the EU or the UK, consent obligations under the GDPR and the ePrivacy rules apply instead, and they are stricter.
Deliverability is where compliance and arithmetic meet. SPF, DKIM and DMARC alignment are effectively mandatory at the major mailbox providers, and complaint rates above a fraction of a percent will move your mail to the spam folder, where the open stage of this model collapses. A model that assumes 98% delivery on a list you have not cleaned is optimistic in the first term of a five-term product, which propagates through every stage below it.
Email sits alongside other channels in the same revenue arithmetic. Where paid media buys reach and pays per click, email owns its audience and pays almost nothing per additional message, which is why its ROI figures are typically far higher and why the constraint is list size rather than budget. To size the reverse question — how many subscribers you would need to hit a revenue target — run the numbers through the traffic needed for revenue goal calculator, and to compare the channel's efficiency against paid, put both through marketing ROI on the same margin basis.