Data plays an essential role in the success of a business provided that it is able to draw the right conclusions from its data. It is critical to differentiate between data correlation and causation so that business decisions are grounded on hard facts and minimally on assumptions. Making drastic decisions based on improper assumptions can send the business into jeopardy that is challenging to bounce back.
Stephen Few said, “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” Understanding the difference between correlation and causation helps us see clearer the connections among the data points, hear better what the data speaks, enabling us to make better business decisions.
What is “Correlation”?
Correlation is a statistical term that pertains to the degree of relationship between two random variables. It tells us how strong is a connection between them, reflecting how tightly or loosely they move together.
For example, variables A and B are observed at the same time. When A increases, B also increases. We say A and B are correlated. It may not mean that B is increasing due to A.
It could be that an increase in A is causing the increase in another variable C, which in turn is causing B to increase. So when C stops increasing, even though A might continue to grow, B would not grow. Therefore, A does not cause B, but B is related to A.
Most of the times, correlation is drawn from analyzing past data and past events.
What is “Causation”?
Causation refers to the “cause-and-effect” relationship that we can clearly establish a causal linkage between the two variables. Their changes are happening at the same time, and the change in one is causing the change in another. Causation usually involves experimental studies and researches.
Correlation vs. Causation
Simply put, correlation refers to the relation between two random variables. They may exhibit changes at the same time, but not necessarily that one is the cause of another. Many times coincidental changes are taken as the root cause that leads to bold and unrealistic claims.
Whereas causation refers to cause and effect of events, i.e., one event is the cause of the other event. Establishing causation is usually harder than establishing correlation.
For example, it may be observed when there is a rise in ice cream sales; more cases of theft are reported. Here, ice cream sales and theft cases are correlated. But the increase in ice cream sales does not cause theft cases.
The cause is – when the weather is warm, people consume more ice cream, hence the rise in sales. At the same time, summery weather is more favorable for people to stay out of their homes; consequently, thieves get more chances to steal with lesser chances of being caught.
What is the use of knowing the difference between Correlation and Causation in marketing and business?
Knowing the difference helps you prevent costly mistakes. If you are able to identify a relationship as a correlation, then, it may happen that they have the same or similar cause, and knowing one’s cause helps to identify the other’s changes much faster.
When we are able to identify and classify the correlations clearly, we’re equipped with the knowledge that enables us to reward and reinforce the correct decisions and strategy. Often the focus of marketing is different for different demographics. Business must know and pull the right trigger that encourages the anticipated result.
Example 1: Increase in product scans, rise in loyalty programs participants
For one of the NeuroTags’ clients, we observed that the number of scans increased, followed by more customers claiming the loyalty points.
While investigating the cause, we found that earlier, the product code placement was not catching the buyer’s attention. After tweaking the NeuroTags code placement to be more prominent, customers started to scan a lot more, and also claim loyalty points. Visibility of the tags was the cause for both the increase in scans and the increase in loyalty points claimed.
Example 2: Extra salary, increase in road trips
Observation says that whenever people in neighborhood “A” receive yearly raises and bonuses, the number of road trips taken by that same neighborhood increases. Here an increase in income and the number of road trips are correlated, but we can not conclude that the increase in pay is causing the rise in road trips.
It may happen that the yearly bonuses for neighborhood “B” arrive with the drop in the number of road trips in the same neighborhood.
A possible explanation may be that Neighborhood A consists of travel enthusiasts who spend their bonus more on trips. And Neighborhood B consists of people who love to play video games. They prefer to spend more on buying games and therefore more time playing video games, thus less time on the road.
So, the conclusion that bonus causes more or less trips is definitely wrong. There are other parameters that affect the number of road trips taken by a neighborhood. It’s important to identify these hidden parameters.
In marketing and product development, understanding the reasons is absolutely crucial to any success. Many times the data, when not analyzed in a holistic way, may lead you to a conclusion that is entirely off.
Keep in mind that correlation does not imply causation. Have your business decisions grounded on thoroughly investigated facts; you will achieve your growth goals.
We, at NeuroTags, provide our clients with the holistic view of their business data with the degree of correlation, which helps them to establish the causation and make successful business decisions.