Customer Lifetime Value (CLV)

What is Customer Lifetime Value?

Customer Lifetime Value (CLV) is the estimated total revenue a typical customer generates before they churn. It combines two inputs: how much a customer pays per month (ARPA) and how long they stay (the inverse of your churn rate). CLV sets the ceiling for how much you can spend acquiring a customer and still be profitable.

The standard rule: CLV should be at least 3× your customer acquisition cost (CAC). A CLV of $3,000 with a $1,500 CAC means you’re barely breaking even on each customer. A CLV of $3,000 with a $500 CAC means you have room to invest in growth.

CLV improves two ways: increase what customers pay (higher ARPA through upsells or pricing) or reduce how fast they leave (lower churn through better onboarding and retention). Reducing churn has a compounding effect — cutting monthly churn from 5% to 2.5% doubles CLV.

The CLV formula

Worked example

Current month: Your company has $10,000 in MRR across 200 paying subscriptions, with a 5% monthly customer churn rate.

Step 1 — Calculate ARPA

Each paying customer contributes $50 per month on average.

Step 2 — Calculate CLV

The average customer is expected to generate $1,000 in total revenue before churning. That’s equivalent to a 20-month average customer lifetime ($50 × 20 = $1,000).

What halving churn does

If you reduce monthly churn from 5% to 2.5% with the same ARPA:

CLV doubles from $1,000 to $2,000. Halving churn doubles the customer lifetime from 20 to 40 months, doubling the total revenue per customer.

How it’s computed

CLV is a Tier 2 metric — it depends on two other metrics rather than raw subscription data. North Metric computes it last in the dependency chain to ensure both inputs are fresh.

Why paid-only subscriptions?

The ARPA calculation uses only paying subscriptions (plan amount > $0). Including free-tier users would dilute the average, producing a lower ARPA and therefore a lower CLV that doesn’t reflect what paying customers are actually worth.

The churn rate conversion

Customer churn rate is stored as a percentage (e.g., 5.0 for 5%). The CLV formula divides by 100 to convert it to a decimal (0.05) before dividing. Without this conversion, CLV would be 100× too low — the most common manual calculation error.

The relationship to NRR

CLV and NRR are connected through churn. Lower churn drives both higher CLV (longer customer lifetime) and higher NRR (more revenue retained each period). A company improving its NRR from 95% to 105% is simultaneously extending customer lifetimes and unlocking expansion revenue — both of which increase CLV.

Snapshot, not predictive

North Metric’s CLV is a point-in-time snapshot. It takes current ARPA and current churn rate and projects forward assuming both remain constant. It does not model expansion revenue over time, cohort-level survival curves, or seasonal variation. For early-stage companies with volatile churn, the value can swing significantly month to month.

CLV vs ARPA

CLV and ARPA are closely related but answer different questions. ARPA tells you what a customer pays per month; CLV tells you their total expected value. A $100 ARPA with 10% monthly churn ($1,000 CLV) is less valuable than a $50 ARPA with 1% monthly churn ($5,000 CLV).

Common CLV mistakes

1. Forgetting to convert churn to a decimal. Using 5 instead of 0.05 gives CLV = $10 instead of $1,000 — a 100× error. Always divide the churn percentage by 100 before plugging it into the formula.
2. Including free users in ARPA. Dividing total MRR by all users (including free-tier) deflates ARPA and produces a CLV that underestimates paying customers’ value. Use paying subscriptions only.
3. Treating CLV as a guarantee. CLV assumes current ARPA and churn stay constant. It’s a snapshot projection, not a prediction. A company with rapidly improving churn will exceed its current CLV; one with worsening churn will fall short.
4. Ignoring the CLV:CAC ratio. A $10,000 CLV sounds impressive until you learn CAC is $8,000. CLV in isolation is meaningless — always evaluate it relative to your cost of acquisition. The threshold is 3:1 or higher.

SaaS CLV benchmarks

CLV benchmarks are segmented by MRR tier. Higher is better. CLV naturally increases with company maturity — larger companies tend to have higher ARPA and lower churn, both of which compound into higher lifetime value. These benchmarks are derived from ARPA and monthly churn data across 1,400+ Stripe-connected SaaS companies.

Frequently asked questions

What is a good CLV for a SaaS company?

There is no universal “good” CLV because it depends on your price point and market. The meaningful benchmark is the CLV:CAC ratio — CLV should be at least 3× your customer acquisition cost. A $500 CLV is strong if your CAC is $100, and weak if your CAC is $400. Median CLV ranges from $615 at early stage to $40,000 at growth stage.

How do you calculate CLV from monthly data?

Divide your Average Revenue Per Account (ARPA) by your monthly customer churn rate expressed as a decimal. If ARPA is $80 and monthly churn is 4%, CLV = $80 ÷ 0.04 = $2,000. The churn rate must be in decimal form — divide the percentage by 100 first.

Why does CLV show as unavailable when churn is zero?

CLV divides ARPA by the churn rate. When churn is zero, the formula would divide by zero, producing an infinite result. Rather than displaying a misleading number, North Metric returns CLV as unavailable. Zero churn implies infinite customer lifetime — mathematically correct but not actionable as a business metric.

What’s the difference between CLV and LTV?

CLV and LTV (Lifetime Value) are the same metric. “CLV” and “LTV” are used interchangeably across the SaaS industry. Some teams use “CLTV” as a third abbreviation. All three refer to the total expected revenue from a customer over their lifetime. North Metric uses CLV as the canonical label.

Does CLV account for expansion revenue?

Indirectly. CLV uses current ARPA, which already reflects any upsells or expansions that have happened. But it assumes ARPA stays constant going forward — it does not project future expansion. A cohort-based model would capture that, but requires years of data. The ARPA ÷ churn formula is a practical starting point for most SaaS companies.