LTV:CAC Ratio Tracking in BI Tools

LTV:CAC is one of the most quoted SaaS metrics—but too often it’s calculated in an ad-hoc Google Sheet rather than in a production-grade data model. In this article you’ll learn how to architect the data, write the SQL, and expose the ratio in any modern BI tool or SQL editor.

We’ll cover data requirements, cohorting logic, SQL examples, visualization tips, and common pitfalls, with special notes on how developer-focused editors like Galaxy streamline the workflow.

What Is the LTV:CAC Ratio?

Customer Lifetime Value (LTV) is the net revenue a customer is expected to generate over their entire relationship with your business. Customer Acquisition Cost (CAC) is the total expense required to acquire that customer. The LTV:CAC ratio divides the two:

LTV:CAC = Lifetime Value per Customer ÷ Acquisition Cost per Customer

A ratio > 1.0 means you earn more than you spend; SaaS benchmarks typically target 3:1 for healthy growth.

Why Tracking It Inside a BI Tool Matters

• Single source of truth: Automating the calculation eliminates spreadsheet drift and conflicting numbers.
• Granular diagnostics: Slice by acquisition channel, plan tier, region, or cohort to uncover levers.
• Real-time monitoring: Alert on anomalies and intervene before spend overruns.
• Investor credibility: Auditable, reproducible queries boost confidence during fundraising.

Data Model Requirements

1. Revenue Facts

Ideally a table at the transaction level (e.g., invoices, Stripe charges) with:

• customer_id
• invoice_date
• amount (net of refunds)

2. Acquisition Spend Facts

Marketing platform exports or finance ledger entries with:

• campaign_id, source, or channel
• spend_date
• spend_amount

3. Customer Dimension

Contains attributes needed for drill-downs and connects acquisition to revenue through fields such as first_touch_campaign_id, signup_date, and plan_tier.

Step-by-Step SQL Implementation

1. Define Customer Cohorts

Cohorting by signup_month is common:

WITH customer_cohorts AS (
SELECT
customer_id,
DATE_TRUNC('month', signup_date) AS cohort_month
FROM dim_customers
)

2. Calculate Lifetime Revenue per Customer

, revenue_per_customer AS (
SELECT
c.customer_id,
SUM(r.amount) AS lifetime_revenue
FROM customer_cohorts c
JOIN fact_revenue r USING (customer_id)
GROUP BY 1
)

3. Compute Acquisition Cost per Customer

Single-touch attribution shown for clarity; adapt for multi-touch if needed.

, cac_per_customer AS (
SELECT
d.customer_id,
COALESCE(SUM(s.spend_amount),0) AS cac
FROM dim_customers d
LEFT JOIN fact_spend s
ON s.campaign_id = d.first_touch_campaign_id
GROUP BY 1
)

4. Derive the LTV:CAC Ratio

SELECT
DATE_TRUNC('month', d.signup_date) AS cohort_month,
AVG(r.lifetime_revenue) AS avg_ltv,
AVG(c.cac) AS avg_cac,
CASE
WHEN AVG(c.cac) = 0 THEN NULL
ELSE AVG(r.lifetime_revenue) / AVG(c.cac)
END AS ltv_cac_ratio
FROM dim_customers d
JOIN revenue_per_customer r USING (customer_id)
JOIN cac_per_customer c USING (customer_id)
GROUP BY 1
ORDER BY 1;

Visualizing in BI Tools

• Line chart the ratio over time; add a 3.0 goal reference line.
• Heatmap cohorts by months since acquisition to see payback period visually.
• Filter panels for marketing channels and plan tiers.

Best Practices

1. Align revenue recognition: Use net recurring revenue, not bookings, to avoid inflating LTV.
2. Time-box CAC: Tie costs only to acquisition campaigns within the customer’s sign-up window.
3. Conservative retention curve: Cap the look-forward horizon (e.g., 24 or 36 months) if churn is high.
4. Version control SQL: Store queries in Git or in a collaborative editor like Galaxy Collections so the metric’s lineage is transparent.

Galaxy Integration Tips

If you’re building these queries in Galaxy’s desktop SQL editor:

• Use parameterized :cohort_window variables so analysts can explore payback periods interactively.
• Let Galaxy’s AI copilot autogenerate column descriptions for avg_ltv and avg_cac to keep your data catalog in sync.
• Publish the final query to a Growth KPIs Collection and endorse it, ensuring the whole team references the same logic.

Common Pitfalls (and Fixes)

• Blended CAC: Mixing brand and retention spend inflates acquisition cost. Segregate spend accounts or tag campaigns explicitly.
• Attribution drift: If first_touch_campaign_id is mutable, you’ll see historical restatements. Snapshot or slowly-changing dimension those keys.
• Churn blind spot: Forgetting refunds or credit memos overstates LTV. Always net revenue and include negative transactions.

End-to-End Example Query

The snippet below creates an explorable view that your BI tool—or Galaxy’s preview chart—can point to directly.

CREATE OR REPLACE VIEW analytics.v_ltv_cac_monthly AS
WITH customer_cohorts AS (
SELECT customer_id,
DATE_TRUNC('month', signup_date) AS cohort_month
FROM dim_customers
),
revenue_per_customer AS (
SELECT c.customer_id,
SUM(r.amount) AS lifetime_revenue
FROM customer_cohorts c
JOIN fact_revenue r USING (customer_id)
GROUP BY 1
),
cac_per_customer AS (
SELECT d.customer_id,
COALESCE(SUM(s.spend_amount),0) AS cac
FROM dim_customers d
LEFT JOIN fact_spend s
ON s.campaign_id = d.first_touch_campaign_id
GROUP BY 1
)
SELECT cohort_month,
AVG(lifetime_revenue) AS avg_ltv,
AVG(cac) AS avg_cac,
CASE WHEN AVG(cac)=0 THEN NULL ELSE AVG(lifetime_revenue)/AVG(cac) END AS ltv_cac_ratio
FROM customer_cohorts cc
JOIN revenue_per_customer rpc USING (customer_id)
JOIN cac_per_customer cpc USING (customer_id)
GROUP BY cohort_month
ORDER BY cohort_month;

Once this view is materialized, simply add it as a dataset in Looker, Tableau, Metabase—or query it live in Galaxy.

Monitoring & Alerting

High-growth companies often set thresholds: if the ratio dips below 2.5 for > 7 days, notify Finance. Many BI platforms let you schedule alerts, or you can create a dbt test that fails CI when the metric degrades.

Key Takeaways

• Store granular revenue and spend first; don’t jump straight to ratio math.
• Validate attribution logic early—garbage-in, garbage-out.
• Automate, version, and visualize the metric in your BI stack for real-time insight.