Every company has – or should have – a series of key performance indicators (KPIs) or, simply said, targets that they should follow. However, those KPIs cannot all be striking in a thousand different directions as that’s counterproductive. It’s important for them to be related to each other as this optimizes performance and helps us successfully reach our targets.
But how do we know whether these set KPIs are compatible?
In business analytics and business intelligence, it’s important to understand the correlation between two variables – these can be your business’ value drivers and expected outcome, for example. This is a key aspect of being successful with your analytical approach. And we can find out this relationship through a method called correlation analysis. In this article, we’ll go into more detail, discussing what it entails, how we apply it, and what the correlation analysis pros and cons are.
What Is Correlation Analysis?
Correlation analysis is a statistical technique which aims to establish whether a pair of variables is related. It is part of business analytics, alongside comparative and trend analysis.
In a business context, this technique can be used to understand which variables are influencing any particular outcome metric. For example, it may highlight the extent to which a price relates to quantities sold for a particular product, or whether job applicant scores are related to future employee performance.
It is important to emphasize that correlation establishes a statistical relationship, but does not prove causation. A good example is the relationship between the total revenue generated by arcades and the amount of Computer Science doctorates awarded in the US. These two variables are statistically related in some geographies, but what may be causing the relationship, in reality, is their mutual relation to another “unknown” variable – like recent advances in technology. What this means is that determining the actual causation may require further analysis.
In any case, without a doubt, one of the key outputs in correlation analysis is a correlational coefficient that indicates the strength of the relationship between two variables.
What Is Multiple Correlation Analysis?
There are also techniques to determine the statistical relationship between multiple variables and a single one. They can help determine which variables have the greatest impact on a particular outcome metric. For example, we could apply a multiple correlation analysis that uses price, season, and store placement in order to determine the collective relationship, as well as the variables’ individual relationships on product quantities in a store.
What Are the Advantages of Correlation Analysis?
There are a few good advantages of employing this technique into your business:
- Its low cost. The analysis can be applied using standard business tools such as Microsoft Excel. Alternatively, you can conduct more complex correlation analysis using open-source programming languages such as Python and R.
- It is relatively simple to understand. Learning the statistical principles to applying correlation analysis and interpreting the results is easily achievable for business professionals. After all, the correlation coefficient varies between -1 and 1, making it very intuitive to interpret.
- It gives you great insights. The technique provides information about the correlational relationship between variables and insight into the strength and the degree of certainty of those relationships, as well as other factors that can aid decision making.
What Are the Disadvantages of Correlation Analysis?
As with any other technique, there are some downsides to correlation analysis as well:
- It does not prove causation by itself. If analysts don’t apply it correctly, they run the risk of identifying variables that are related to one another, but do not cause each other. This, in turn, may lead to managers addressing the wrong levers of performance – like in the correlation example between arcade revenue and computer science doctorates. Surely, less people playing video games will not result in fewer Ph.D. graduates in computer science as well, right?
- It needs to start with a pre-defined set of variables. Managers need to select the variables that they will be testing ahead of time. More advanced data mining techniques, on the other hand, can help identify relations within a broader set.
- There are prerequisites to applying it in our analysis. For example, to obtain reliable results, the technique requires us to provide numerical data and a minimum number of observations – not ideal for larger-scale businesses that deal with huge quantities of data in various forms.
What Is the Verdict on Correlation Analysis?
While there are obvious disadvantages to this technique, they do not outweigh the advantages. Correlation analysis is great for finding the correlation between two variables, which can be incredibly helpful to companies who are looking to set quality KPIs and improve their businesses.
We can safely conclude that it is relatively simple, easy to understand, and extremely beneficial to a career in analytics – whether you’re just starting out in data science or looking for a career change. It could help you a great deal – provided that you have the right amount of data and that business managers have defined the variables well. Overall, correlation analysis can be quite advantageous for our business analytics goals.
