Marketing Causal Inference
| Analysis Type | Correlation | Causal Inference |
|---|---|---|
| Question | "What happened together?" | "What caused what?" |
| Example | Email opens correlate with sales | Emails caused 15% more sales |
| Method | Regression analysis | Holdout/control groups |
| Executive credibility | Low ("maybe") | High ("proven") |
💡TL;DR
Marketing Causal Inference proves that your actions caused results—not just that they happened together. Correlation shows "people who received emails also bought." Causal inference proves "emails caused them to buy." For SMBs, this distinction determines whether you're making data-driven decisions or data-justified guesses. Holdout testing is the primary method: compare outcomes between those who received treatment vs. those who didn't.
Definition
Statistical methods that establish causation (not just correlation) between marketing/sales actions and revenue outcomes using experimental design.
🏢What This Means for SMB Teams
Dashboards show correlation: "people who got emails also bought." Causal inference proves causation: "emails caused them to buy." This is the difference between hope and proof.
Detect /pricing revisits, email clicks, buying signals your CRM misses.
24/7 monitoring turns silent intent into revenue action.
📋Practical Example
A B2B company's dashboard showed 40% of customers who opened emails converted. They assumed emails worked. But when they ran a holdout test: 38% of non-email group also converted. The "40% correlation" was actually only 2% lift. They reallocated budget to channels with higher causal impact.
🔧Implementation Steps
- 1
Identify the question: What action's impact do you want to measure?
- 2
Design experiment: Randomize into treatment and control groups
- 3
Ensure clean separation: Control group gets NO treatment, not different treatment
- 4
Measure same outcomes for both groups over same time period
- 5
Calculate causal impact: Treatment outcome minus Control outcome = caused by action
❓Frequently Asked Questions
Why isn't correlation good enough for decision-making?
Because correlation can be misleading. High-intent buyers might have converted anyway—with or without your email. Correlation credits the email; causal inference tests whether it actually helped. Misattribution leads to wasted budget on ineffective actions.
We're too small for statistical significance. What do we do?
Run longer (60-90 days instead of 30), aggregate across similar actions, or accept directional findings. Even imperfect causal evidence is better than pure correlation. Start small, prove value, then invest in larger sample sizes.
⚡How Optifai Uses This
ROI Ledger uses holdout testing for causal inference. Weekly reports show causally-attributed revenue, not just correlated metrics.
Self-Improving ROI Ledger📚References
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Related Terms
Holdout Test for RevOps
A testing methodology where a percentage of accounts receive no AI actions (control group) to measure the true incremental revenue impact of automation.
ROI Ledger
A ledger system that tracks every AI action with a UUID and attributes actual revenue contribution using holdout testing.
Revenue Lift
The incremental revenue increase attributed to AI actions, measured against a holdout control group that received no AI intervention.