How to Calculate LTV When You Have Limited Data
You don't need years of customer history to calculate a useful LTV. Here are the formulas that work at different stages — and the assumptions you need to state explicitly.
The most common reason early-stage teams skip LTV calculations is that they feel they do not have enough data. This is almost always the wrong call. An LTV estimate built on twelve months of customer data and explicit assumptions is far more useful than no LTV estimate at all — because it forces you to think carefully about retention, it makes your assumptions visible and challengeable, and it gives you a number to refine as more data arrives.
The goal at the early stage is not a precise LTV — it is a directionally correct one with documented assumptions. The precision comes over time. The discipline of calculating it starts on day one.
The two formulas
Average lifetime = 1 ÷ 0.03 = 33 months.
LTV = ₹60 × 0.75 × 33 = ₹1,485
LTV = ₹45 × (0.82 + 0.71 + 0.64 + 0.59 + 0.55…) summed to your expected horizon.
At 24 months: LTV ≈ ₹45 × 17.4 months effective ≈ ₹783
An LTV estimate with explicit assumptions is far more useful than no LTV at all. The discipline of calculating it — not the precision of the result — is what changes how a team makes decisions.
The three assumptions that change everything
Churn rate. The most sensitive input in any LTV calculation. A shift from 3% to 5% monthly churn reduces average customer lifetime from 33 months to 20 months — a 40% reduction in LTV. When you have limited data, model LTV at three churn assumptions (optimistic, central, pessimistic) and present the range rather than a single number. This prevents the common mistake of anchoring to a number that was derived from two months of data.
Gross margin. Using revenue instead of gross profit overstates LTV significantly for businesses with meaningful delivery costs. A SaaS business with 80% gross margins and a services business with 40% gross margins have very different economics even at identical revenue levels. If your gross margin is below 50%, your LTV is much lower than a revenue-based calculation suggests — and your CAC tolerance is correspondingly lower.
The time horizon. LTV is technically the sum of all future gross profit from a customer, discounted to present value. In practice, most teams cap their LTV at a three to five year horizon, because projections beyond that introduce more uncertainty than insight. Be explicit about your horizon. An LTV calculated over twenty-four months and one calculated over sixty months for the same customer can differ by three to five times — and both are technically "correct."
Use Simple LTV when you have less than eighteen months of customer history, when you are doing a quick directional calculation, or when your audience (investors, leadership) needs a number they can sanity-check quickly. Use Predictive LTV when you have enough cohort data to see how your retention curve actually flattens — typically after eighteen months — and when you are making precise decisions about CAC ceilings or payback period targets. The Simple formula will overstate LTV if your retention curve does not flatten (i.e., if customers keep churning at a constant rate indefinitely rather than stabilizing).
The number you actually use for decisions
LTV by itself is not directly actionable. The number you use for actual decisions is the LTV:CAC ratio and the payback period. If your Simple LTV is ₹1,485 and your CAC is ₹400, your LTV:CAC ratio is 3.7:1 — healthy. Your payback period is CAC divided by monthly gross profit per customer: ₹400 ÷ (₹60 × 0.75) = ₹400 ÷ ₹45 = approximately 9 months — excellent.
Both numbers should be recalculated every quarter as your ARPU, churn rate, and CAC evolve. The trend matters as much as the current value: a 3:1 ratio that has improved from 2:1 over six months tells a very different story from a 3:1 ratio that has declined from 5:1.
If your customers upgrade over time — moving from lower to higher tiers, adding seats, buying add-ons — your simple LTV formula understates the true value of a customer because ARPU at acquisition is lower than ARPU at Month 18. Adjust by using average ARPU across the customer lifetime rather than acquisition ARPU, or add a separate "expansion revenue" term. Businesses with strong net revenue retention (above 110%) will dramatically understate LTV if they use a static ARPU assumption.
LTV calculations require inputs from multiple parts of the business — revenue data from finance, churn data from product or CS, gross margin data from operations. In FabricLoop, teams doing quarterly LTV reviews often use a shared group note that captures the inputs, the calculation, the assumptions, and the resulting LTV:CAC ratio alongside the prior quarter's figures. When the assumptions are documented in the same place as the output, the "which churn rate did we use?" conversation does not happen three months later. The calculation becomes auditable and improvable rather than a black box that changes every time someone recalculates it.