Cohort LTV Calculator
The formula
What the cohort ltv tells you
The per-customer gross-profit sum, capped at a modeling horizon because no exponential churn curve deserves infinity.
Calculation: LTV = ARPC × margin × min(1 ÷ churn, horizon). Enter your inputs and every substitution is shown below the result.
Reading the result
The −1pt sensitivity line is where retention teams earn budgets: LTV is churn-leverage far more than price-leverage.
Rules of thumb: Horizon cap 48–60 months standard in subscription businesses
Limitations and best practice
Cohort averages hide diverging acquisition months — compute LTV per cohort, or the blended number will flatter the worst channels.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Why cap the horizon?
Should LTV use revenue or gross profit?
Source & methodology
Standard subscription LTV convention.
Cohort LTV: the number, the method and the caveats
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. Searchers land on a Cohort LTV page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
Like most useful measures, Cohort LTV is a summary, and summaries hide as much as they reveal. The companion outputs and the input table are here to reopen what the headline compressed.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for Cohort LTV.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here lifetime value = $900, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Monthly revenue churn first, at ≈11.1 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
Every input in Cohort LTV, and what it does to the answer
One formula, published in full: LTV = ARPC × margin × min(1 ÷ churn, horizon). Anything you can read, you can reproduce in a spreadsheet, and that reproducibility is the whole point of printing it rather than hiding it behind a button.
Avg revenue per customer / month ($) (example value 90) is a plain measured quantity — the unit shown in the label is the unit the formula expects. If the period is partial, decide whether you mean elapsed or planned time and keep that meaning across every field.
Gross margin (%) (example value 80) is a measured number in the unit named by the label; the formula does not convert for you. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Monthly revenue churn (%) (example value 8) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Model horizon cap (months) (example value 60) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If the period is partial, decide whether you mean elapsed or planned time and keep that meaning across every field.
Cohort LTV on the example inputs: the complete substitution
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Avg revenue per customer / month ($), Gross margin (%), Monthly revenue churn (%), Model horizon cap (months). The computed lifetime value comes out at $900, which the interpretation band labels “Customer life 12.5 months”.
- Life = min(1 ÷ 8%, 60) = 12.5 months
- LTV = 90 × 80% × life = $900
Uncapped expected life — 12.5 months. If your inputs change, this is usually the first companion figure to move.
Monthly gross profit per customer — $72.00. This is the number experienced users quote when the headline alone would mislead.
LTV if churn halved — $1,800. This is the number experienced users quote when the headline alone would mislead.
Value of −1 pt churn per customer — $129. If your inputs change, this is usually the first companion figure to move.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
Scenario table: Cohort LTV at ±10 % and ±20 % of the key input
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Cohort LTV, not an interpolation.
| Monthly revenue churn setting | Lifetime value |
|---|---|
| 80 % of the example value | $1,125 |
| 90 % of the example value | $1,000 |
| example inputs | $900 |
| 110 % of the example value | $818 |
| 120 % of the example value | $750 |
The corridor runs from $750 to $1,125 across that band — which is the range worth planning inside rather than the single $900 point estimate. Reproduce it in the form above by editing Monthly revenue churn alone; the substitution steps will follow your figures exactly.
What actually moves your Cohort LTV
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the lifetime value. For Cohort LTV the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Monthly revenue churn: ten percent up gives $818, ten percent down gives $1,000 — ≈11.1 % of the lifetime value either way
- Avg revenue per customer / month: ten percent up gives $990, ten percent down gives $810 — ≈10.0 % of the lifetime value either way
- Gross margin: ten percent up gives $990, ten percent down gives $810 — ≈10.0 % of the lifetime value either way
Monthly revenue churn is the dominant lever here: ten percent either way moves the result by about 11.1 %, from $818 to $1,000. That is where a careless figure does the most damage, so check it against a source, and treat Avg revenue per customer / month as the second priority (≈10.0 % per ten percent). The remaining fields can sit at safe defaults without materially changing the answer — knowing which is which is what separates a careful calculation from an anxious one.
Reading the result: bands, zones and thresholds
A number on its own is trivia; a number against a range is intelligence. The default case lands in the “Customer life 12.5 months” band, and bands are deliberately wide because real situations overlap. How much of that band is luck? On Monthly revenue churn alone ten percent either way covers $818 to $1,000. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Placing this figure in context: calculators of this kind differ less in arithmetic than in what they choose to ignore. This one keeps Avg revenue per customer / month ($) explicit so that the omissions are visible rather than hidden in a preset.
How people get Cohort LTV wrong
Cohort averages hide diverging acquisition months — compute LTV per cohort, or the blended number will flatter the worst channels. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Treating the example inputs as a recommendation. They are a demonstration of the mechanism — a decision-grade Cohort LTV begins the moment your own figures replace them.
One mistake is specific to this engine: assuming every input matters equally. The computation above shows Monthly revenue churn dominating — a careful figure there beats false precision everywhere else.
The subtlest error is answering the wrong question precisely. Confirm that lifetime value is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
Before you trust your Cohort LTV: a short checklist
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Check Monthly revenue churn before anything else: it is the measured dominant input, ≈11.1 % of Cohort LTV per ten percent.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Confirm the example values are plausible for your own situation before replacing them.
- Confirm the answer against one independent method or published benchmark for Cohort LTV.
Step by step: getting a trustworthy Cohort LTV out of this tool
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — gather before you type. Cohort LTV depends on avg revenue per customer / month ($), gross margin (%), monthly revenue churn (%), model horizon cap (months). Collect them from a source document — a statement, a report, an invoice, a log — rather than from memory, because recalled inputs are the single largest cause of wrong results. One decimal place more than your data supports is one decimal place of fiction.
Step 2 — baseline before exploration. Get one defensible Lifetime value on screen before you start nudging numbers. Without a baseline, every variation looks interesting and none of them means anything. One decimal place more than your data supports is one decimal place of fiction.
Step 3 — change one input at a time. Nudge a single field by ten percent, watch the headline and its companions move, then undo it. Two or three fields will dominate — the sensitivity table above already ranks them for this engine. The point of transparency is that a reader can disagree with a specific line rather than with you.
Step 4 — stress before you commit. A decision that survives a ten-percent squeeze on its two biggest inputs needs no luck to be sound. One that does not survive is a decision about hope, not about arithmetic. One decimal place more than your data supports is one decimal place of fiction.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the $900 output. Six months from now the context is what turns this figure into a trend. Estimates improve most when the worst case is written down in ink.
From calculation to decision: where Cohort LTV earns its keep
Numbers earn their keep in conversations, and Cohort LTV is no exception. In Business & Entrepreneurship discussions around Startup Metrics, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. Rounding early is the quietest way to introduce a permanent error.
Professionals in Startup Metrics use this style of calculation as a screening filter before spending real money or effort: a quick Cohort LTV check eliminates weak options in minutes, and the survivors earn the deeper, slower analysis. Write the figure down before moving on; memory flatters whichever number you saw last.
Treat Cohort LTV like a smoke alarm rather than a painting — its value is in being tested periodically, not in being admired once. The input you reach for first is usually the one you measured longest ago.
Quick answers: what searchers ask about Cohort LTV
What affects Cohort LTV the most?
Cohort LTV is driven above all by Monthly revenue churn. Perturbing only that field by ten percent either way produced $818 against the $900 baseline — a ≈11.1 % response, while Avg revenue per customer / month manages ≈10.0 %.
Which matters more: avg revenue per customer / month or monthly revenue churn?
By the numbers, Monthly revenue churn: a ten-percent change in it moves the result ≈11.1 %, versus ≈10.0 % for Avg revenue per customer / month. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Cohort LTV?
There is no honest universal average for Cohort LTV, but there is a reference point: the defaults on this page produce $900 (Customer life 12.5 months). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate Cohort LTV by hand?
Use the stated model — LTV = ARPC × margin × min(1 ÷ churn, horizon) — and substitute in the order the tool prints (it starts from Life = min(1 ÷ 8%, 60) = 12.5 months). On the example inputs the hand route lands on $900, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Cohort LTV if monthly revenue churn changes by 10 %?
On these inputs a ±10 % change in monthly revenue churn takes Cohort LTV to $818 against the $900 example — around 11.1 % of the headline. The same test on every other field is in the sensitivity section.
Is the Cohort LTV result of $900 good or bad?
Good and bad are comparisons, so the honest test is three questions: where does your own Cohort LTV sit against the $900 reference above, how far would a plausible input error move it (≈11.1 % per ten percent on Monthly revenue churn), and what does the alternative option compute to?
Is Cohort LTV accurate?
The arithmetic is exact: LTV = ARPC × margin × min(1 ÷ churn, horizon) is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the $900 result as the correct answer to the question you typed — and check the assumptions section before acting on it.
How do I calculate Cohort LTV manually?
Write down the inputs, then follow the printed steps in order — LTV = ARPC × margin × min(1 ÷ churn, horizon). A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
Why cap the horizon?
The 1/churn tail assumes exponential loyalty; 60 months keeps LTV defensible in diligence.
Should LTV use revenue or gross profit?
Gross profit — revenue-based LTV is the metric that most often lies in a pitch deck.
Does this Cohort LTV tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Business & Entrepreneurship figures and the reason you can re-run this page as often as you like.
What the labels on this page mean
- Uncapped expected life
- 12.5 months in the default Cohort LTV calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Monthly gross profit per customer
- $72.00 for the Cohort LTV worked case; it sits next to the main result because it carries context the headline leaves out.
- LTV if churn halved
- $1,800 for the Cohort LTV worked case; it sits next to the main result because it carries context the headline leaves out.
- Lifetime value
- The primary output of this tool: $900 for the default inputs, computed exactly from LTV = ARPC × margin × min(1 ÷ churn, horizon).
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Customer life 12.5 months”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Cohort LTV moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
Everything above runs on one stated formula, applied without hidden coefficients: LTV = ARPC × margin × min(1 ÷ churn, horizon). Source of record: Standard subscription LTV convention. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. Nothing is smoothed, weighted or adjusted behind the scenes; where judgement enters, this page says so and names the input responsible.
What to do with your Cohort LTV next
Save the number together with its inputs and today’s date; a Cohort LTV figure without context is unusable the moment an input changes.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.