How to Calculate SaaS LTV (And Why Most Companies Get It Wrong)
The standard LTV formula gives you one number that hides everything important. Here's how to calculate LTV properly for SaaS, with formulas, examples, and what actually matters for acquisition decisions.
Customer Lifetime Value (LTV) is the total revenue a customer generates before they churn. For SaaS companies, this determines maximum acceptable CAC, which channels to invest in, and whether the business model is sustainable.
When I first started working with SaaS companies on their paid acquisition, I'd ask "What's your LTV?" and get back one number: "$850" or "$1,200." Clean, confident, wrong. That single number was hiding everything that actually mattered.
Most companies calculate LTV using a simple formula that produces an average, which obscures the distribution of customer value. The problem? Your customers aren't average. Some churn in a month, others pay for years. Treating them all the same leads to catastrophically bad acquisition decisions.
The Standard Formula (And Why It Fails)
The formula everyone uses:
LTV = ARPU ÷ Monthly Churn Rate
Example: $50 ARPU, 5% monthly churn = $1,000 LTV
This assumes customers are homogeneous—they all behave roughly the same. In reality, SaaS customer behavior follows a power law distribution:
Customer Segment
% of Base
Lifetime
Value
Reality vs Average
Fast churners
40%
1-3 months
$50-150
85% below average
Medium
30%
6-12 months
$300-600
40% below average
Long-term
30%
18+ months
$900-1,800+
Up to 80% above average
That $1,000 "average" doesn't represent any actual customer. It's a statistical artifact that leads to bad decisions.
Three LTV Calculations You Actually Need
Use different methods for different purposes:
1. Historical LTV (Most Accurate)
What: Actual revenue from churned customers When: Analyzing past performance, comparing cohorts Formula: Sum of all payments from signup to churn
Example customer:
Months 1-6: $50/mo = $300
Months 7-12: $80/mo (upgraded) = $480
Total LTV: $780
Limitation: Only works for churned customers, doesn't predict current customer value.
2. Cohort LTV (Most Useful)
What: Track revenue from groups who started in the same period When: Understanding trends, measuring product changes Method: Track cumulative revenue month by month
Month
Cohort Size
Active
Revenue
Cumulative LTV
0
100
100
$5,000
$50
1
100
95
$4,800
$98
2
100
88
$4,500
$143
3
100
82
$4,200
$185
6
100
70
$3,700
$263
12
100
55
$3,000
$380
Why it matters: Shows how LTV evolves over time and reveals impact of product/pricing changes.
3. Predicted LTV (Most Practical)
What: Estimated future value based on early signals When: Setting CAC targets, real-time acquisition decisions Formula:
After analyzing dozens of SaaS companies, certain patterns consistently predict customer value:
Activation Quality
Activation Level
Definition
Retention Rate
Avg LTV Multiplier
High
Hit key milestones in week 1
85% at 6mo
2.5x
Medium
Partial activation
60% at 6mo
1.3x
Low
Minimal usage
25% at 6mo
0.4x
Customers who complete core workflows in their first session have dramatically higher LTV, but most formulas ignore activation entirely.
Payment Behavior
Annual prepay customers have 2-3x better retention than monthly payers, not just because of payment commitment but because they self-select as long-term users. Track this separately:
Churn isn't constant—it's highest in months 1-3 and decreases over time. Using one churn rate overestimates early-stage LTV and underestimates long-term customer value.
Use cohort analysis instead of simple formulas.
Mistake 3: Not Segmenting by Channel
Different channels produce different customer quality:
Blended LTV: $600 (misleading)
Actual:
- Organic: $800 LTV → Invest heavily in SEO
- Paid social: $400 LTV → Reduce budget or optimize targeting
Mistake 4: Ignoring Time Value
$600 earned over 12 months isn't the same as $600 earned over 36 months. Apply a discount rate for longer payback periods.
Making LTV Actionable
The goal isn't a perfect number—it's making better decisions:
Use LTV To:
Set channel budgets based on LTV by source
Define target customers by predicted LTV segments
Prioritize features that increase retention (raise LTV)
Optimize pricing for maximum lifetime value
Inform product development
Track These Metrics:
LTV by acquisition channel
LTV by customer segment
LTV trend over time (improving or declining?)
LTV:CAC ratio by channel
Payback period by channel
The Most Important Insight
Perfect LTV calculation matters less than understanding what drives LTV. Companies that track cohort retention curves and act on insights typically see 30-50% LTV improvements over 12-18 months.
Focus less on the formula, more on:
What makes customers stay?
Which early behaviors predict retention?
What features drive expansion?
How can we get more high-LTV customers?
The calculation gives you a baseline. Understanding the drivers lets you improve it.
What Changed My Thinking
Early in my career, I obsessed over getting the LTV calculation "perfect." We'd debate whether to use 75% or 80% margins, whether to include professional services revenue, how to handle one-time fees. These discussions felt important but missed the point entirely.
The real breakthrough came when I started segmenting LTV by acquisition source. One company I worked with had a blended LTV of $680—seemed reasonable. But when we broke it down:
Organic search: $920
Referrals: $1,100
Paid search: $520
Paid social: $380
They were spending 60% of their budget on the two lowest-LTV channels because "the CAC looked good." They weren't tracking what happened after month one.
The lesson: an approximate answer to the right question beats a precise answer to the wrong question. Stop optimizing the formula. Start understanding what actually drives customer value, and use that knowledge to make better acquisition decisions. Your LTV isn't static—it's a reflection of the customers you choose to acquire and the product experience you build for them.
