If Customer Acquisition Cost answers "how much does a customer cost," Lifetime Value answers "how much is that customer actually worth." Knowing both is the only way to know whether your business model works. Knowing only one is worse than useless - it's actively misleading.

Definition

Customer Lifetime Value (LTV) - the total gross profit you expect to earn from a single customer across the full duration of the relationship - whether that's six months, three years, or a decade.

Formula
Simplest version:
LTV = Average gross profit per customer per period × Average customer lifetime (in periods)

Subscription business version:
LTV = (Average revenue per customer per month × Gross margin) ÷ Monthly churn rate
Worked example
A SaaS business charges $200/month with a 75% gross margin and a 4% monthly churn rate. LTV = ($200 × 0.75) ÷ 0.04 = $3,750. If their CAC is $1,200, the LTV:CAC ratio is 3.1:1 - right in the healthy zone.

Use gross profit, not revenue

A common shortcut: calculate LTV using revenue instead of gross profit. It's easier - you don't need to know your gross margin - and it produces a much bigger, more impressive number. It's also wrong.

Revenue overstates customer value because it ignores what it costs to serve them. A customer paying you $1,000 over their lifetime, where you spent $400 on hosting, support, payment processing, and fulfillment to serve them, is worth $600 to you - not $1,000. Multiply the gap across thousands of customers and revenue-based LTV will tell you to spend acquisition dollars you can't actually afford.

Estimating average customer lifetime

For subscription businesses, the math is straightforward: average customer lifetime ≈ 1 ÷ monthly churn rate. A 4% monthly churn rate implies 25 months average lifetime. A 2% monthly churn rate implies 50 months.

For non-subscription businesses (e-commerce, services, retail), it's a closer estimate: look at customers acquired 2+ years ago and measure the average time between their first and last purchase. Different cohorts give different answers; use a recent enough cohort to be relevant but old enough to have a full lifetime captured.

LTV is most useful paired with CAC

LTV in isolation is interesting trivia. LTV compared to Customer Acquisition Cost (CAC) is one of the cleanest signals of business health.

  • LTV:CAC below 1:1 - you lose money on every customer. Stop scaling and fix the model.
  • LTV:CAC 1:1 to 2:1 - acquisition barely pays back. Risky territory.
  • LTV:CAC around 3:1 - the conventional benchmark. Healthy.
  • LTV:CAC above 5:1 - strong economics. You may be under-investing in growth.

Three levers to improve LTV

1. Reduce churn

Even small churn improvements compound dramatically. Cutting monthly churn from 5% to 3% increases the implied lifetime from 20 months to 33 months - a 65% LTV improvement with everything else equal. Retention is the highest-leverage LTV lever for subscription businesses.

2. Increase average revenue per customer

Pricing, upsell, cross-sell, and feature expansion all push LTV up. The constraint: do they hurt retention? An aggressive upsell that pushes churn up can be net-negative on LTV even if revenue per customer rises.

3. Improve gross margin

Cheaper cost to serve flows directly to LTV. Migrating to cheaper infrastructure, automating support, and reducing payment processing fees are all gross margin improvements.

Common mistakes with LTV

1. Using revenue instead of gross profit

Already covered. The biggest single mistake.

2. Calculating LTV before you have enough retention data

Estimating LTV for a 6-month-old business with most customers still active is mostly guessing. Use industry benchmarks until you have at least 12-18 months of cohort data.

3. Treating LTV as constant

LTV changes as the business grows, customer mix shifts, and retention practices mature. A number you calculated 18 months ago is probably wrong now. Recalculate quarterly.

4. Ignoring the cohort effect

Customers acquired today behave differently from customers acquired three years ago. Older cohorts often have higher LTV (better targeting, better product). Average LTV across all cohorts can mask material shifts.

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