A company is starting a marketing campaign. Sales has been increased. The team is happy, the campaign is declared a success and the budget for the next campaign increases.

What if sales had been going up prior to the campaign? What if there was a seasonal trend, a price change or a competitor is lowed out of stock? This is where businesses can get it wrong with the data. Two things can happen at the same time and yet not necessarily be caused by one another.

Businesses make decisions based on evidence and not assumptions, and knowing the difference between correlation and causation can help them in that process.

What is the difference ?

Correlation: When two variables tend to change together. One will increase and the other will either increase or decrease.

Causation: It means a change in one variable directly contributes to a change in another.

For example, a business may notice that higher advertising spending is associated with higher sales. That is a correlation. To claim that the advertising caused the sales increase, the business needs stronger evidence.

As the Australian Bureau of Statistics [1] explains, correlation describes a relationship between variables, while causation describes a cause-and-effect relationship. A correlation alone cannot establish that one variable caused the other.

Three Reasons Businesses Confuse Correlation With Causation

1. A third factor influences both variables

Let’s Say, a retailer sees ice cream and sunscreen sales increase during February and March. It would be crazy to think that people buy sunscreen because they buy ice cream. A third factor, warmer weather, can impact both.

The same problem occurs in business data. Sales may be going up for a company and advertising spending may be going up at the same time but seasonal demand may be going up for both.

This is often called a confounding variable: a factor that influences the relationship being observed. [2, 3, 4]

2. The direction of the relationship is unclear

A company sees its most loyal customers get more customer support. Does more support equal customer loyalty? Or are the loyal customers that use the product more often contacting the company more often? Either explanation may be correct. Correlation doesn’t tell us the direction of the relationship.

3. The pattern may be coincidental

When companies track enough metrics, some will look related just by chance. For example, a manager might look at monthly sales alongside how many social media posts were made and see both numbers went up. That is not proof that more posting gets more sales. Or it might be a coincidence or some other factor may be responsible for it.

The more patterns we look for, the more careful we must be to treat an interesting finding as meaningful.

How Businesses Can Test Whether Something Actually Works

A correlation is still useful. It can point analysts toward a question worth investigating. The next step is to test the explanation. Here are a few practical approaches:

1. Compare similar periods: Compare the same season or similar business conditions, not two unrelated months.

2. Check other factors: Review traffic, pricing, stock levels, promotions and shifts in customer behavior.

3. Use a control group: If possible, compare customers who received an intervention with similar customers who did not.

4. Run A/B test: Randomly split customers into different experiences and compare results. Random assignment minimizes the influence of other variables.

5. Look for consistent evidence: A pattern that is repeated under similar conditions is more convincing than a single observation.

For example, an online store could randomly show a discount to one group of eligible visitors while another group sees the regular price. If the groups are comparable and the test is properly designed, the difference in outcomes provides stronger evidence about the discount's effect.

A/B testing is not always possible, and its results still depend on good design. But it is often more informative than simply comparing sales before and after a campaign. [5]

Correlation Is a Starting Point, Not a Conclusion

A correlation coefficient, often written as “r”, measures the direction and strength of a linear relationship between two variables. And its value ranges from -1 to +1.

• Near +1: The variables tend to move together.

• Near -1: One tends to increase as the other decreases.

• Near 0: There is little or no linear relationship.

But even a strong correlation does not prove causation. It may be influenced by another variable, a coincidental pattern, or a relationship that works in the opposite direction [6]. This matters when presenting data to decision-makers. A chart can show that two metrics move together, but the explanation beside that chart must be supported by more than the visual pattern.

Instead of writing:

“The campaign increased sales by 24%.”

A more accurate statement might be:

“Sales increased by 24% during the campaign period. Further analysis is needed to estimate how much of the increase was caused by the campaign.”

That small difference in wording protects the business from turning an assumption into a strategy.

Conclusion

There are patterns in business data! Some show significant connections and others are a matter of timing, external circumstances and/or chance.

Correlation enables analysts to see what could be going on. Causation is key to the establishment of why it is happening. The difference is important because businesses don't just report numbers. These numbers serve as their clues to determine where to invest, what to change, and what strategies to repeat.

The next time two metrics move in concert, don't assume that there's a cause. What other factors might account for the pattern? What evidence would lend credence to the claim? What kind of tests could be carried out in the business to validate the claim?

A good data insight isn't one that merely explains what changed. Provides a business with a rationale for a change.