Cohort analysis is the most rigorous way to understand customer retention.
Instead of tracking an aggregate churn rate, you follow a specific group of
customers (those who started in January, for example) and measure how many
are still active over time.
What Cohort Analysis Reveals
Aggregate churn hides critical signals. A company growing 20% monthly with
5% monthly churn looks like a 5% churn business — but cohort analysis reveals
that older cohorts retain much better (or worse) than newer ones.
Cohort signals to watch:
- Month 1–3 churn spike: Usually an onboarding issue
- Month 6–12 churn spike: Often a value realization problem
- Stable churn after month 12: "Power user" retention floor — the core audience
LTV from Cohort Churn Rate
For a constant monthly churn rate:
LTV = Monthly Revenue per Customer ÷ Monthly Churn Rate
At $99/month and 2% monthly churn: LTV = $99 ÷ 0.02 = $4,950
At $99/month and 5% monthly churn: LTV = $99 ÷ 0.05 = $1,980
Cutting churn in half doubles LTV — the single most powerful lever in subscription
economics.
Cohort Half-Life
The cohort half-life is the number of months until 50% of the original cohort
has churned:
At 2% monthly churn: half-life ≈ 34 months
At 5% monthly churn: half-life ≈ 14 months
At 10% monthly churn: half-life ≈ 7 months
Benchmarks by Company Type
Company Type
Monthly Churn
Annual Churn
LTV at $100/mo
Enterprise SaaS
0.5–1%
6–11%
$10,000–$20,000
Mid-market SaaS
1–2%
12–22%
$5,000–$10,000
SMB SaaS
2–5%
22–46%
$2,000–$5,000
Consumer apps
5–15%
46–80%
$700–$2,000
Frequently asked questions
What is a good monthly churn rate for SaaS?
Below 1% monthly churn (under 12% annual) is considered good for SMB SaaS.
Enterprise SaaS should target 0.5–0.75% monthly (6–9% annual). Consumer apps
with 3–5% monthly churn can be viable if CAC is low and ARPU high enough.
How do I do a full cohort analysis?
Group customers by the month they first subscribed. For each cohort, track
what percentage remains active at months 1, 3, 6, 12, 24. Plot these as a
retention curve. Compare curves across cohorts — improving retention curves
over time means your product and onboarding are getting better.
How to Do Cohort Analysis for SaaS: Step-by-Step Guide
A step-by-step guide to building a cohort retention matrix for SaaS — what to track, how to read retention curves, and what patterns to act on.
Cohort analysis is the single most useful analytical framework for subscription
businesses. It takes 20–30 minutes to set up and can reveal problems (and wins)
that aggregate metrics hide.
Step 1: Define Your Cohort
The most common cohort definition: customers grouped by the month they first
subscribed (or activated, or made their first purchase).
Other useful cohort definitions:
- Acquisition channel (paid search, referral, organic)
- Pricing plan (Starter vs Pro vs Enterprise)
- Company size (1–10, 11–50, 51–200 employees)
- Onboarding path (self-serve vs sales-assisted)
Step 2: Build the Retention Matrix
Create a table where:
- Rows = cohort start month (Jan 2024, Feb 2024, etc.)
- Columns = months since start (Month 0, Month 1, Month 2, etc.)
- Cell value = % of original cohort still active
Example:
Cohort
M0
M1
M2
M3
M6
M12
Jan 2024
100%
82%
74%
70%
65%
60%
Feb 2024
100%
85%
78%
73%
—
—
The diagonal shows the current state of each cohort (Feb 2024 cohort is at M2).
Step 3: Identify the Patterns
Healthy pattern: Steep early drop, then flattening curve.
Most churn in months 1–2, then survivors are sticky. The tail asymptote is your
"power user" baseline.
Problematic pattern: Linear decay that never flattens.
Churn continues at the same rate month after month — no core of power users.
Improving cohorts: Newer cohorts have higher retention at the same time point
than older ones — your product is getting better.
Worsening cohorts: Newer cohorts churn faster — check for product regressions,
channel mix change (acquiring lower-quality customers), or pricing changes.
Monthly Churn Rate Benchmarks for SaaS: What's Good, Bad, and Typical?
SaaS monthly churn rate benchmarks by company type, ARR, and customer segment — what's acceptable and what signals a retention problem.
Monthly churn rate is the percentage of subscribers who cancel each month.
What's acceptable varies dramatically by customer segment and ARPU.
Benchmarks by Customer Segment
Segment
Monthly Churn
Annual Churn
Notes
Enterprise ($5k+ ACV)
0.5–1.0%
6–12%
Multi-year contracts; churn is often 0 until renewal
Mid-market ($1k–5k ACV)
1.0–2.0%
11–22%
Quarterly reviews; churn often tied to budget cycles
SMB (<$1k ACV)
2.0–5.0%
22–46%
Higher turnover; product must be self-evidently valuable
Consumer subscription
5–15%
46–80%
Low switching cost; loyalty built through habit/content
Benchmarks by ARR Stage
According to various benchmark reports (ProfitWell, SaaStr):
- <$1M ARR: 10–20% annual churn is common (small sample, less reliable)
- $1M–$10M ARR: Target <10% annual, <15% is acceptable
- $10M–$50M ARR: Target <8% annual
- $50M+ ARR: Public SaaS median ~7–8% gross annual churn
- Best-in-class: Veeva, Salesforce, and enterprise platforms under 4% annual
Gross Churn vs Net Churn
Gross churn: Revenue lost from cancellations only
Net churn (NDR/NRR): Revenue lost from cancellations MINUS revenue gained from upgrades
A company can have 8% gross churn but negative net churn (105%+ NRR) if expansion
revenue from upgrades exceeds cancellation revenue. Both metrics matter.
What Causes Above-Average Churn?
Product-market fit gaps: Customers don't fully solve their problem
Pricing plan mismatch: Wrong tier for the customer's actual usage
Onboarding failures: Customers never reach the "aha moment"
Support gaps: Customers with unresolved issues churn 3–5× faster
Budget pressure: SMBs cancel discretionary spend in downturns first
Investors use margin-adjusted LTV because you can only reinvest the margin,
not the total revenue.
When the Formula Breaks Down
The constant-churn formula has three simplifying assumptions:
Constant churn: In reality, cohorts often have higher early churn
(months 1–3) and lower later churn. The actual LTV is higher than the formula
suggests for sticky products.
No expansion revenue: If customers upgrade over time, the formula
understates LTV. Use average revenue per account trend to adjust.
Discrete time: The formula uses monthly periods; more accurate models
use continuous time (e^−churn_rate × t) for instantaneous rates.
For most planning purposes, the simple formula is accurate enough. The margins
of error (±20%) are smaller than the uncertainty in any other LTV input.