Newsletter Value Calculator

Ask anyone who runs a newsletter what it earns and you will usually hear a figure for one send. That figure is real. It just answers the wrong question.

The question that matters is what the list is worth over time, and that is a different kind of number. A list is a group of people who arrive, stay for a while and leave. The money one of them brings over that whole stay is called their lifetime value, and the usual definition counts net profit over the entire future relationship, not revenue from a single month. It is a forecast rather than a record. Customer profitability looks back at what someone already produced, while lifetime value looks forward, which makes it more useful for decisions and much harder to pin down.

Lifetime value also sets a ceiling. It is the most you could sensibly spend to win one more subscriber, which is why it sits at the centre of every decision about paid growth. Spend above it and each new reader loses money, however fast the list grows.

Where the money comes from

A newsletter earns in a few distinct ways. Small businesses are told to list every revenue stream they have, and the federal small business agency names direct sales, membership fees and the sale of advertising space as typical examples. For a newsletter those map onto three lines: clicks that earn something, such as links to products that pay a commission; sponsorships, where an advertiser pays for space in an issue; and paid subscriptions, where some readers pay a recurring price. Each line behaves differently as the list changes, so it pays to keep them apart rather than quote one blended figure.

Click revenue is a chain of rates. Subscribers times sends gives emails delivered. An open rate and a click rate turn those into clicks, and revenue per click turns clicks into money. If you track clicks per recipient directly, the open rate drops out of the chain altogether, and one uncertain number goes with it.

Paid subscriptions are the line most directly tied to churn. A subscriber who leaves takes a recurring price with them, every month, for as long as they would have stayed.

Sponsorship sits in between. If a sponsor pays by the size of the audience, that line grows and shrinks with the list. If the fee is flat and fixed for a year, it does not, and a projection that scales it with the list will read too high.

Churn does most of the work

Churn is the share of a group that leaves in a period: the number lost divided by the number present at the start. A list that begins a month with 500 readers and loses 50 has a monthly churn of 10 percent. The same idea runs through telephone contracts, software subscriptions and streaming services, and it is a key input to every lifetime value model.

The simplest model treats each month as a fresh draw. A subscriber present now earns a margin this month, stays to the next with probability one minus the churn, earns again, and so on. Adding those expected margins gives a geometric series, and a geometric series whose ratio is below one has a finite sum: the first term divided by one minus the ratio. Here the first term is the monthly margin and the ratio is one minus churn, so the sum collapses to margin divided by churn. Its other face is the expected stay, which is 1 / churn. An annual churn of 25 percent means readers stay four years on average.

Take the example the calculator opens with, round numbers made up for the purpose. Ten thousand subscribers get four sends a month. Half open each send and a tenth of those click, at 0.20 a click: 400 a month. A sponsor pays 300. Two percent of readers pay 5 a month, another 1,000. Revenue is 1,700. The email platform charges 120 at this size, so the margin per subscriber is 1,580 divided by 10,000, or 0.158 a month. With 2 percent of the list leaving each month, the simple lifetime value is 0.158 divided by 0.02, which is 7.90.

Now halve the churn and the value doubles to 15.80. Double the churn and it halves. No revenue lever moves the result so directly. A 10 percent rise in revenue per subscriber lifts the lifetime value by a little more than 10 percent, because the platform cost does not rise with it. A cut in churn from 2 to 1 percent lifts it by 100 percent.

Monthly and annual churn are easy to mix up. A subscriber who stays a year survives twelve monthly draws, so a 2 percent monthly rate compounds to 21.53% a year, not 24 percent. Going the other way matters more. Treat an annual rate of 25 percent as if it were monthly and a list with no new readers keeps about 3 percent of itself after a year, instead of 75 percent.

Money that arrives later is worth less than money now, and the standard way to say how much less is a discount factor: one divided by one plus the rate, raised to the number of periods. Federal guidance for weighing costs and benefits uses exactly that factor; at 3.1 percent a year it values a benefit one year out at 0.9699 of the same benefit today. For a subscriber it gives the discounted lifetime value: margin times retention, divided by one plus the discount rate minus retention, where retention is one minus churn. In the example, 10 percent a year is 0.797 percent a month, and the discounted value is 5.54.

The gap between 7.90 and 5.54 has two causes, and only one of them is discounting. The discounted formula assumes the first margin arrives at the end of the first month, and only if the reader is still there. That timing alone removes one month’s margin, 0.158. The other 2.21 is the discount rate at work across the long tail of readers who stay for years. Discounting is often skipped in practice. Nominal figures are easier, and the standard account of lifetime value says plainly that they are biased slightly high, more so the further into the future the money is expected.

Paying for growth

Paid growth turns lifetime value into a decision. If winning a subscriber costs 1.50 and that subscriber is worth 5.54 discounted, the return on that spend is 5.54 minus 1.50, divided by 1.50: about 269 percent. Return on investment is defined exactly that way, as gain minus cost over cost. The definition is simple on purpose, which is also its weakness. It says nothing about time, and two campaigns with the same ratio can differ by years in how long the money takes to come back.

Payback answers the time question. It counts the periods until the cumulative cash flow turns positive, and it ignores the time value of money by design. For a paid subscriber the cash flow is the expected margin month by month, shrinking as churn thins the group that joined together. In the example the running total reaches 1.50 during the eleventh month, so payback takes 11 months. Ignore churn and the answer would be 1.50 divided by 0.158, about 9.5 months.

There is also a hard limit. If a subscriber costs more than margin divided by churn, they never pay back at all, however long you wait. At 2 percent churn and a 0.158 margin that ceiling is 7.90. A campaign that pays more than that per reader is buying a loss.

The list as a whole has its own arithmetic. New readers arrive at some steady pace while a fixed share leaves each month, so the list drifts toward the size where the two balance: new arrivals per month divided by churn. With 500 joining and 2 percent leaving, that size is 25,000. Growth slows as the list approaches it, not because the campaigns got worse but because there are more readers to lose.

Keeping the numbers honest

Every input to these formulas is a rate you measured yourself, and each one carries assumptions. Churn measured across a fast-growing list understates the churn of each signup month, because new arrivals dilute the departures. Following each signup group on its own gives the truer figure, and it is the only way to measure an average stay. The models also assume constant churn and constant margin. Real customers rarely behave that way, and the value they bring is often lopsided: many never come back after the first contact while a few are worth a great deal, so an average says little about any one reader.

Margin has to mean margin. A common mistake is to put revenue into a lifetime value formula, which can inflate the result several times over. Count what each reader brings in, less what it costs to keep them on the list.

Readers also have the right to leave. Commercial email in the United States must carry a working way to opt out, and the federal law gives a sender 10 business days to stop after a request. Churn is not a defect to engineer away. It is a cost of doing business, and the lever on it is a newsletter people want to keep.

The arithmetic should be exact as well. Keep money in whole minor units, the cents of a currency (the yen has none), and add those. JavaScript’s BigInt holds whole numbers far too large for an ordinary number, so a long projection never drifts by a cent.

What a calculator cannot do is tell you whether your numbers are good. It holds no typical rates and should not, because someone else’s average says nothing about your list. Feed it your own figures, measured by signup month where you can, and read the answer as the expected value of a simple model rather than a forecast of any month.

Revenue per send is a snapshot. Churn, payback and lifetime value are the film.