The Attribution Problem Finance Actually Understands - And How to Talk About It

"We can't prove that campaign caused the sales lift." Every marketer has heard this objection. But here's what nobody tells you: finance faces the exact same problem - and they've developed sophisticated frameworks for making decisions anyway. Let's steal them.

The attribution problem isn't unique to marketing. Finance professionals deal with correlation-vs-causation challenges constantly: Does this economic indicator predict stock returns? Does company culture drive performance? Does R&D spending create value?

They don't throw up their hands and say "we can't know anything." They use statistical tools to quantify relationships, acknowledge limitations, and make probabilistic decisions. You can do the same.

What Correlation Actually Means

Correlation measures whether two things move together. That's it. Not whether one causes the other - just whether they're related.

The correlation coefficient (r) ranges from -1 to +1:

Value	Meaning	Marketing Example
+1.0	Perfect positive	Spend up → Revenue up (always)
+0.7 to +0.9	Strong positive	Email sends & conversions
+0.3 to +0.7	Moderate positive	Brand spend & consideration
0	No relationship	Print ads & app downloads
-1.0	Perfect negative	Price up → Volume down (always)

Crucially, correlation doesn't tell you direction of causation. If brand spend and sales are correlated at 0.6, it could mean:

1. Brand spend drives sales (what you hope)
2. Sales drive brand spend (you spend more when things are good)
3. Something else drives both (seasonality, economy)

💡 Key Insight: Finance knows correlation isn't causation - they learned it on day one. What impresses them isn't claiming causation anyway; it's showing you understand the limitation and have methods to address it.

Spurious Correlations: The Credibility Killer

Before we talk about using correlation properly, let's talk about using it improperly—because this is where marketers destroy their credibility.

A spurious correlation is a statistical relationship that exists in data but has no meaningful connection. Famous examples:

• Nicholas Cage films correlate with swimming pool drownings (r = 0.67)
• Margarine consumption correlates with divorce rates in Maine (r = 0.99)
• Cheese consumption correlates with death by bedsheet tangling (r = 0.95)

Absurd? Yes. But marketing is full of equally spurious correlations that sound more plausible:

• "Our social engagement correlates with sales!" (Both spike during product launches)
• "Email open rates predict revenue!" (Both track with business hours/weekdays)
• "Our brand awareness grew with market share!" (Both grew with the economy)

The CFO has seen enough spurious correlations to be skeptical of all correlations. Your job is to show why yours are different.

How Finance Uses Correlation (Despite Its Limits)

Finance doesn't avoid correlation because it's imperfect. They use it constantly - but with guardrails:

1. They Look for Theoretical Basis

Before running the numbers, finance asks: "Is there a plausible reason these should be related?" A correlation between ad spend and sales has theoretical basis. A correlation between ad spend and the CEO's golf handicap does not.

Apply this: Start with the logic. "We believe brand advertising creates mental availability, which influences purchase decisions. Here's the correlation that supports that theory."

2. They Test for Timing (Lead/Lag)

If A causes B, A should generally happen before B. Finance looks at whether changes in one variable precede changes in another.

Apply this: "Brand spend in month 1 correlates 0.65 with consideration lift in month 2, suggesting a leading indicator relationship. Same-month correlation is only 0.4."

3. They Control for Confounders

A confounder is a third variable that affects both things you're measuring. Seasonality is the classic marketing confounder - both ad spend and sales peak in Q4.

Apply this: "After controlling for seasonality and promotional periods, the correlation between brand spend and sales remains 0.52 - lower than the raw 0.68, but still meaningful."

4. They Look for Dose-Response

If the relationship is real, more input should create more output. Does doubling spend double the effect? If there's no dose-response pattern, the relationship may be spurious.

Apply this: "Spend buckets show dose-response: at $50K/month we see 3% consideration lift; at $100K/month, 5.5%; at $150K/month, 7.2%."

🎯 The Pattern: Each of these techniques doesn't prove causation. But together, they build a "weight of evidence" case that's far more credible than a single correlation number.

Incrementality: Correlation's Sophisticated Cousin

Modern marketers talk a lot about "incrementality." Let's be clear: incrementality testing is just a method for getting closer to causation.

The core idea: compare outcomes for a group exposed to marketing versus a similar group not exposed. The difference is the "incremental" effect.

Finance recognizes this as a quasi-experimental design - not as rigorous as a true randomized experiment, but much stronger than simple correlation.

Method	How It Works	Causal Strength
Geo-holdout	Market X gets ads, similar market Y doesn't	Strong
Matched market	Statistical matching of test/control groups	Strong
Spend variation	Natural spend fluctuations analyzed for impact	Moderate
Time-series	Campaign on/off periods compared	Moderate
Correlation only	Spend and outcomes move together	Weak

Board-ready language: "Our geo-holdout tests show 12% incremental sales lift in test markets, statistically significant at p<0.05. This moves us from correlation to demonstrated causal impact."

Channel Correlation: The Portfolio View

There's another way finance uses correlation that's even more relevant to marketing: portfolio diversification.

In investing, you want assets that aren't perfectly correlated. When stocks drop, you want bonds to hold steady (or rise). The same logic applies to marketing channels.

Consider two scenarios:

Scenario A: Facebook and Instagram performance have 0.9 correlation. When one is up, the other is up. When one crashes (algorithm change, CPM spike), both crash.

Scenario B: Facebook and Direct Mail have 0.2 correlation. They move independently. When Facebook crashes, direct mail keeps humming.

A marketing portfolio heavy on correlated channels is concentrated risk. A portfolio with uncorrelated channels is diversified risk.

🎯 Strategic Implication: This is why "all-in on Facebook" strategies scare CFOs - they're the marketing equivalent of putting your entire 401(k) in one stock. Show your channel correlations and the diversification benefit of your mix.

A Marketing Correlation Matrix

	Paid Social	Search	Email	TV	Direct
Paid Social	1.00	0.75	0.45	0.30	0.15
Search	0.75	1.00	0.50	0.40	0.20
Email	0.45	0.50	1.00	0.25	0.35
TV	0.30	0.40	0.25	1.00	0.10
Direct Mail	0.15	0.20	0.35	0.10	1.00

Reading this: Paid Social and Search are highly correlated (0.75) - risk is concentrated. Direct Mail has low correlation with everything - it provides diversification. A portfolio heavy on digital with some direct mail is more resilient than all-digital.

The Big Picture: Honest Uncertainty Builds Credibility

Here's the counterintuitive truth: acknowledging uncertainty makes you more credible, not less.

When you say "this campaign drove a 15% sales lift," the CFO immediately thinks about all the reasons that might not be true. When you say "our geo-holdout test showed 12-18% incremental lift, controlling for seasonality, with 95% confidence," you've preempted the objections.

The language of correlation, causation, and uncertainty is finance's native tongue. Speaking it shows you understand that marketing measurement is hard - and that you're doing the work anyway.

That's more impressive than any perfectly round number you could invent.

Quick Reference: From Correlation to Credibility

Weak Claim	Strong Claim
"Brand spend drives sales."	"Brand spend correlates 0.6 with sales, with spend leading by 4-6 weeks."
"This campaign generated $2M."	"Geo-test shows $1.8-2.2M incremental, p<0.05, controlling for seasonality."
"We're diversified across channels."	"Average channel correlation is 0.35; no single channel >30% of budget."
"Social engagement predicts sales."	"After removing shared seasonality, engagement-sales correlation is 0.25."

This article is part of the "Finance for the Boardroom-Ready CMO" series.

Based on concepts from the CFA Level 1 curriculum, translated for marketing leaders.