Data plays an indispensable role in the success of a business provided that the business draws the right conclusions from the data available to it. This is why it is essential to know the difference between correlation and causation to ensure that the business decisions are made based on hard facts, not the assumptions. We all know that making bold decisions based on assumptions can jeopardize business growth and might be challenging to bounce back to profit.
As Stephen Few has rightly 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 in giving a clear voice to your business data and therefore helps in making successful business decisions.
What is “Correlation”?
Correlation is a statistical term that pertains to the degree of relationship between two random variables. It tells us that there is a pattern between the variables, and they tend to move together.
For example, Two variables A and B are observed at the same time. It may be seen that when A increases, the value of B also increases. Here A and B are correlated, but it may not mean that B is increasing due to A.
It may happen that A is causing another variable C to increase, which may be causing B to increase. So, if increment in C is stopped, then even though A might continue to grow, B would not increase. And this is how we make sure A does not cause B, but somehow may be related.
Most of the times, Correlation is drawn from the data of events that happened in the past.
What is “Causation”?
Causation refers to the “cause-and-effect” relationship between two variables. In causation, we can establish a causal relationship between two variables. We can say that two variables A and B are related, they are happening at the same time, and A is causing B. Causation usually involves experimental studies and researches.
Correlation vs. Causation
In simple words, correlation refers to the relation between two random variables. It may happen just by coincidence and many times, 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.
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 the relationship as correlation, then, it may happen that they may have the same or similar cause, and knowing one’s cause helps identify the other’s cause faster.
And, many times if we know correlation and cause then its much easier to solve the given business case, because you know now the reason why your data is the way it is and can take better rewarding decisions or product pivots or sometimes the focus of the marketing to different demography.
Example 1: Increase in product scans, rise in loyalty programs participants
For one of the NeuroTags’ clients, we observed that when the number of scans increased, more people claimed the loyalty points.
When looked into the cause, we found that earlier the product code placement was in such a way that it was not catching the product buyer’s attention. After changing the NeuroTags code placement, it was promptly visible to the buyers, and they started to scan the code and also avail the loyalty points. So the cause was the placement of the tags for both the results.
Example 2: Extra salary, increase in road trips
We may come across data in which we observe that whenever people in neighborhood “A” receive yearly bonuses, the number of road trips taken by the same neighborhood also increase. Here an increase in salary 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 make the number of road trips to decrease.
A possible explanation may be that Neighbourhood A consists of travel enthusiasts who spend their bonus more on traveling. And Neighbourhood 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 and lesser time on the road.
So, the conclusion that bonus causes more trips or fewer trips is definitely wrong. Instead, there are many more parameters which cause the trips more or less, and it’s more important to understand these hidden parameters.
In marketing and product development, understanding the reason is considered very important. Many times the data, when not analyzed in a holistic way, may lead you to conclude something entirely wrong. If you always keep in mind that correlation does not imply causation, and your business decisions are based on thoroughly investigated facts, you should be able to achieve all 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.