How to Calculate Lifetime Value of a Customer in B2B SaaS

Getting the lifetime value of a customer means multiplying their average revenue by their average lifespan. Right?

In B2B SaaS, that simple formula is often dangerously misleading. It ignores the factors that actually drive the business — gross margin, expansion revenue, and big differences between customer segments.

This guide gives you a practical, operator-friendly way to calculate LTV in a way that supports real decisions about growth, onboarding, and where to invest next.

Why Generic LTV Formulas Fail in B2B SaaS

Most SaaS leaders have seen the textbook shortcut:

Simple LTV = ARPA ÷ churn

It’s fast. It’s also too blunt for a modern SaaS model.

The problem with averages

Your business is not “one average customer.” It’s a set of segments with different:

• price points
• support costs
• expansion profiles
• churn behaviors

Consider a simple example:

• SMB segment: £50/month, ~12-month lifespan, low support
• Enterprise segment: £500/month, ~36-month lifespan, heavier onboarding/support

A blended LTV averages them into a number that is too high for SMB acquisition decisions and too low for enterprise investment decisions.

Common LTV pitfalls (and why they matter)

Mastering the Foundational SaaS LTV Formulas

Before you get fancy, learn the baseline formulas — and their limitations.

The simple LTV formula (quick, blunt)

Formula: Simple LTV = ARPA / Customer Churn Rate

Example:

• ARPA = £100/month
• monthly customer churn = 5%

Then:

£100 / 0.05 = £2,000

That’s top-line revenue. It treats all revenue as profit.

The gross-margin LTV formula (more realistic)

Formula: LTV (Gross Margin) = (ARPA * Gross Margin %) / Customer Churn Rate

If gross margin is 80%:

(£100 * 0.80) / 0.05 = £1,600

That difference is often the gap between “looks fine” and “actually unprofitable.”

Implementation: get a baseline you can trust

1. ARPA: total MRR ÷ number of paying customers.
2. Customer churn: customers cancelled last month ÷ customers at start of month.
3. Gross margin: (Revenue - COGS) / Revenue.
4. Compute baseline LTV: use the gross-margin formula and track it monthly.

If you need the churn/retention groundwork first, start here: Define Customer Retention.

Unlocking Deeper Insight with LTV Cohort Analysis

Basic formulas give you one blended number. Cohorts show you the trend.

Why cohorts reveal the real story

Cohort analysis groups customers by when they signed up (or by segment/channel). It lets you answer questions like:

• Did customers acquired after a new onboarding flow expand more?
• Did a pricing change increase revenue retention?
• Are recent cohorts reaching value milestones faster?

Build your first cohort table

Here’s a simplified example of cumulative revenue by cohort:

Then compute ARPC (Average Revenue Per Customer) per cohort at each month:

• Jan (Month 2): £12,800 ÷ 100 = £128 ARPC
• Feb (Month 2): £15,500 ÷ 120 = £129.17 ARPC
• Mar (Month 2): £13,000 ÷ 110 = £118.18 ARPC

Now you have a real question to investigate: why did March underperform?

A practical SQL starting point

Below is a simplified cohort query pattern. Adapt field names to your warehouse.

WITH user_cohorts AS (
  SELECT
    user_id,
    DATE_TRUNC('month', created_at) AS cohort_month
  FROM users
),

monthly_revenue AS (
  SELECT
    user_id,
    DATE_TRUNC('month', payment_date) AS revenue_month,
    SUM(amount) AS revenue
  FROM subscriptions
  GROUP BY 1, 2
)

SELECT
  c.cohort_month,
  EXTRACT(YEAR FROM r.revenue_month) * 12 + EXTRACT(MONTH FROM r.revenue_month)
  - (EXTRACT(YEAR FROM c.cohort_month) * 12 + EXTRACT(MONTH FROM c.cohort_month)) AS months_since_signup,
  COUNT(DISTINCT c.user_id) AS cohort_size,
  SUM(r.revenue) AS total_revenue
FROM user_cohorts c
JOIN monthly_revenue r ON c.user_id = r.user_id
GROUP BY 1, 2
ORDER BY 1, 2;

If you want to map this to growth actions, pair it with your onboarding milestones and activation definition (see: Unlocking Product Analytics for SaaS Growth).

Using Predictive LTV to Shape Product and Growth Strategy

Predictive LTV connects early trial behavior to long-term outcomes. The goal is not “more tracking.” It’s:

• identify value-creating behaviors
• guide more users to them
• invest in segments that expand and retain

Identify high-value signals in the trial

Don’t score vanity metrics like logins. Score workflow proof.

• Social activation: invites teammates, creates shared workspaces.
• Integration signals: connects key tools (Slack, CRM, data source).
• Workflow completion: completes a core use case end-to-end.
• Milestone progress: finishes onboarding checklist steps that correlate with retention.

Implementation: a lightweight predictive model you can run this month

1. Pull your top 20% of customers by gross-margin LTV.
2. Analyze what they did in their first 7 days.
3. Choose 3–5 signals that show up repeatedly.
4. Build a simple score (points per signal).
5. Trigger nudges when high-potential users stall.

If you’re mapping interventions across the journey, this article helps: Mastering the B2B SaaS Customer Life Cycle.

Your Technical Blueprint for LTV Instrumentation

Any LTV model is only as good as the data you feed it.

A simple tracking plan (what to instrument)

Track milestones across the customer journey:

• Acquisition: Trial Started, Demo Requested, User Signed Up
• Activation: Checklist Completed, First Project Created, Teammate Invited
• Engagement: Feature X Used, Report Generated, Integration Connected
• Monetization: Subscription Upgraded, Add-on Purchased, Contract Renewed

For each event, define properties (plan, seats, MRR delta, segment) so you can build reliable LTV cohorts later.

Join product signals with revenue

To link early behavior to long-term value, you need a stable identifier across product and billing (e.g., account_id).

WITH activation_signals AS (
  SELECT
    account_id,
    MIN(CASE WHEN event_name = 'Teammate Invited' THEN timestamp END) AS first_invite_ts,
    MIN(CASE WHEN event_name = 'Integration Connected' THEN timestamp END) AS first_integration_ts
  FROM product_events
  WHERE event_name IN ('Teammate Invited', 'Integration Connected')
  GROUP BY 1
),

billing AS (
  SELECT
    account_id,
    plan_name,
    mrr,
    subscription_start_ts
  FROM billing_subscriptions
  WHERE status = 'active'
)

SELECT
  b.account_id,
  b.plan_name,
  b.mrr,
  a.first_invite_ts,
  a.first_integration_ts,
  TIMESTAMP_DIFF(a.first_invite_ts, b.subscription_start_ts, DAY) AS days_to_invite
FROM billing b
LEFT JOIN activation_signals a USING (account_id);

This is the bridge that turns “events” into a predictive model.

Common Questions About Calculating LTV in SaaS

How should we handle free trial users?

Free trial users have realized LTV of £0 until they convert. Exclude them from your primary LTV calculation.

Instead, track trial cohorts and build a predicted LTV model based on whether they hit key activation milestones.

What is a good LTV:CAC ratio?

A common benchmark is 3:1 or higher (in gross margin terms). Below that usually signals inefficient acquisition, weak retention/expansion, or pricing issues.

How often should we recalculate LTV?

At least monthly (or quarterly at minimum). Cohorts can shift quickly after changes to onboarding, pricing, or product value.

The EngageKit View: Turn LTV Into a System (Not a Spreadsheet)

Most teams can compute a number. Fewer teams can reliably increase it.

EngageKit helps you grow LTV by operationalizing the behaviors that drive it:

• Define the milestones that predict high LTV: activation, team signals, integrations, repeat workflows.
• Detect stalls early: find accounts that aren’t progressing toward value.
• Guide the next best step automatically: behavior-driven nudges in-app and via follow-up.

If you want a higher LTV, start by making your trial-to-value path consistent — and measurable.