How to actually Calculate Ltv without making it wrong
Lifetime value is the total revenue you can reasonably expect from a single customer over the entire relationship. The basic math is straightforward, which is exactly why most people get it wrong. They use a formula they found on a blog and then wonder why their financial model looks like a fantasy. The standard approach works like this: take your average monthly revenue per user, divide it by your monthly churn rate, and you get a rough LTV number. Or if you prefer the longer version, multiply gross margin percentage by average revenue per user and then divide by churn. Both get you to approximately the same place. A 5% monthly churn means you divide by 0.05, which is the same as multiplying your margin revenue by 20. That 20 is your estimated customer lifespan in months. Here is where things get real. I was building a SaaS model for a client a couple years back and their churn was anything but flat. New customers churned like crazy in month one, then stabilized, then churned again around month eight when their contracts came up for renewal. Running a simple divide-by-churn formula gave us an LTV that was about 40% too high because the formula assumed a constant churn rate that didn't exist. The fix was to build a month-by-month cohort survival curve, multiply each month's expected revenue by the probability of still being a customer that month, and sum it all up. That took a spreadsheet and about 20 minutes, but it gave us a number that actually matched reality instead of sounding optimistic.
You should also be thinking about discounting future cash flows. Money in month twelve is not the same as money in month one, and if you are comparing LTV across products with very different retention curves, the time value of money starts to matter. A 10% discount rate is standard in enterprise software. It brings future months down to present value and keeps your comparisons honest. There are two things most people miss. First, LTV without segmentation is basically a guessing game. Your enterprise customers, your free-tier users, and your mid-market accounts all have completely different LTV profiles. Running them through the same formula gives you a blended number that is useless for any decision making. Segment by acquisition channel at minimum. If you can, segment by cohort month because product changes and market conditions shift retention in ways that bleed into the aggregate. Second, most teams calculate LTV using revenue, not contribution margin. If your cost to serve a customer is 30% of revenue, then your actual profit LTV is 70% of what you just calculated. I have seen marketing teams spend more on acquisition than the gross profit a customer ever generates because nobody bothered to factor in server costs, support tickets, or payment processing fees. The gap between revenue LTV and profit LTV is usually where deals fall apart.
The formula breaks down completely in a few situations. Subscription businesses with long ramp-up periods where revenue starts low and grows over time will look terrible on a simple calculation. Platform businesses with network effects have LTV that depends on other customers existing, which makes isolated customer math meaningless. And if your business is pre-revenue or still figuring out pricing, LTV is just a placeholder dressed up as a number. In those cases, you are better off building a range scenario and being honest about it instead of presenting a single figure that implies more precision than the data supports. For most SaaS companies, a quick back-of-the-envelope LTV calculation is fine for early conversations. But if you are making hiring decisions, setting CAC targets, or pitching investors, you owe it to yourself to run the cohort-based model. It takes a little extra work upfront, maybe another hour in the spreadsheet, but it prevents you from making multi-quarter decisions based on a number that was never accurate to begin with.
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