Free SaaS pricing model and LTV calculator

Use the saas pricing model calculator

What does this tool actually do?

This calculates your implied customer lifetime value from price and churn, then builds the tier structure that fits your chosen pricing model, with actual computed price points, not generic advice. It sets the anchor tier near 2.5 times your current price and the entry tier near 0.4 times, derives the maximum CAC your economics can support, and names the specific retention mechanic (free-to-paid conversion, trial conversion, usage expansion) that matches the model you picked.

It runs entirely in your browser. Nothing you type is sent to a server, no account is required, and there is no usage limit, because there is no cost per run to control.

Churn compounds, which is why monthly and annual numbers never agree

Multiplying monthly churn by twelve is the most common arithmetic error in SaaS planning. At 5 percent monthly churn you do not lose 60 percent of customers in a year, because retention compounds: 0.95 to the power of 12 is about 0.54, so you keep roughly 54 percent and lose 46. At 3 percent monthly you keep about 69 percent, and at 1 percent about 89. The shortcut overstates the loss while hiding the shape of the curve, which is the part that drives everything downstream.

The same relationship gives you average customer lifetime, which is simply one divided by the monthly churn rate. Five percent implies 20 months, three percent implies 33 months, two percent implies 50 months and one percent implies 100 months. That last figure should make you uncomfortable. It asserts an average relationship of more than eight years, which is a claim about the future dressed up as a calculation.

It also assumes churn is constant, which it never is. Cancellations cluster heavily in the first weeks after signup, then the rate falls as the surviving cohort becomes progressively more committed. A single blended rate therefore overstates the risk for long-tenured accounts and understates it for last month's cohort. If you have the data, plot retention by signup cohort and read the curve. If you do not, treat any lifetime figure beyond about 24 months as a scenario rather than a forecast.

Logo churn and revenue churn are different numbers

Logo churn counts accounts leaving. Revenue churn counts pounds leaving. They diverge as soon as your customers are not all the same size, which is immediately. Lose four percent of your accounts and one percent of your MRR and you have a small-account problem that self-corrects. Lose one percent of accounts and four percent of MRR and you have just lost your largest customer, which is a different meeting entirely.

Gross revenue churn cannot go below zero, because it only counts losses. Net revenue retention adds expansion (upgrades, seat growth, usage increases) and can therefore exceed 100 percent. Around 100 percent means the existing base holds itself flat. Between 110 and 120 is generally considered good, and the top decile of public SaaS companies sits meaningfully higher. Above 100 percent the business grows without adding a single new customer, which is why the metric moves valuations more than growth rate alone.

Contraction is the piece that hides in logo churn. A seat-based product loses revenue every time a customer freezes hiring, without a single cancellation. That means your revenue churn partly tracks your customers' headcount rather than their satisfaction with you, and in a downturn those two things separate sharply. Model contraction as its own line rather than folding it into churn, or you will misdiagnose a market problem as a product problem.

LTV, and why the three to one rule misleads

The workable formula is average revenue per account multiplied by gross margin, divided by monthly churn rate. At 50 pounds a month, 80 percent gross margin and 3 percent churn, that is 50 times 0.8 divided by 0.03, or about 1,333 pounds. Drop the gross margin term, as most spreadsheets quietly do, and the same customer appears to be worth 1,667. You have just inflated every downstream decision by a quarter.

Then comes the LTV to CAC ratio, quoted almost everywhere as a target of three to one. The number itself is not wrong, it is fragile, and the fragility is the useful part to understand. It is a ratio of one estimate built on an extrapolated churn rate to another estimate built on contested cost allocation, presented as a single figure precise enough to make decisions with. It breaks in five specific and predictable ways.

• It extrapolates past your evidence. A two-year-old company quoting a 50-month lifetime is asserting a number it has never observed. Cap the horizon at the age of your oldest meaningful cohort.
• Blended CAC hides everything. One channel at a payback of six months and another at forty average into something respectable and unactionable. Split by channel, and include salaries and agency fees, not just media spend.
• It says nothing about cash. LTV is a lifetime total; your bank balance is a monthly reality. CAC payback in months is CAC divided by monthly revenue times gross margin, and it is the number that governs whether growth is fundable.
• It averages across segments that behave nothing alike. An overall ratio of three can be one segment at eight subsidising another at 0.8, and the aggregate tells you to do more of both.
• It flatters stagnation. Because LTV rises as churn falls, a company that stops growing and retains well can post an excellent ratio while going nowhere. Read it alongside growth, never on its own.

What changing the pricing model actually changes

Choosing a model is choosing what your revenue tracks. Per-seat pricing ties growth to your customers' headcount, which is excellent while they hire and painful when they stop. Usage-based pricing ties it to their volume, so revenue expands and contracts on its own: strong net revenue retention in good conditions, unpredictable invoices that procurement teams dislike. Tiered packaging sells easily because the buyer can see which box they are in, but it caps expansion at the top of each tier. Flat rate is simplest and leaves the most money on the table.

Annual prepay deserves its own note because it distorts the metrics you are modelling. Moving a customer to annual billing gives them one cancellation decision a year instead of twelve, so measured churn falls without anything about the product improving. The standard trade is 15 to 20 percent off, often framed as two months free, in exchange for cash up front. The cash is real. The retention gain is partly real and partly an artefact of the billing cycle, and dissatisfaction now surfaces all at once at renewal.

Price increases behave better than most people expect, because software cost of goods barely moves with price, so a 10 percent rise flows almost entirely to gross profit. It also buys headroom: at a 10 percent higher price you could lose about 9 percent of your customers and still hold revenue flat, with fewer accounts to support. Model that breakeven loss explicitly before anyone argues about elasticity. None of this tells you what to charge, it tells you which lever moved the output.

Numbers worth knowing

Mistakes that quietly cost you results

Calculating annual churn as monthly churn times twelve

Retention compounds, so 5 percent monthly is about 46 percent annually rather than 60. The multiplication also implies churn stops mattering after month twelve, which quietly breaks every year-two projection built on it.

Quoting a customer lifetime longer than the company has existed

One divided by churn produces a number, not evidence. If your oldest cohort is 18 months old, cap the model there and treat anything beyond as a scenario, or you will fund acquisition against revenue nobody has ever collected.

Treating logo churn and revenue churn as interchangeable

They only agree if every customer pays the same. Model both, plus contraction as its own line, or a single large downgrade will read as a healthy month right up until the cash flow says otherwise.

Running one blended ARPA across every segment

A blended average of self-serve and enterprise describes no customer you have. Split the model by plan or segment first; the churn, margin and payback numbers usually differ enough to reverse the conclusion.

Modelling a price increase with volume held constant

Work out the breakeven loss instead. At a 10 percent rise you can lose about 9 percent of customers and hold revenue flat with lower support load, which turns an argument about elasticity into a number you can test.

What does the output look like?

This is the exact output the tool produces from the example inputs. It is generated by the same code that runs when you click the button, so what you see here is what you get.

Frequently asked questions

Related free tools

• Ad Angle Generator Paid ads
• Landing Page Message Match Auditor Paid ads

Some links on this site are affiliate links, which means Hacking Demand may earn a commission if you buy through them at no extra cost to you. This does not influence which tools are listed. The tools on this page are free and have no affiliate relationship of any kind.