Every company has – or should have – a set of key performance indicators (KPIs) or, more simply, targets that they must meet. However, those KPIs cannot all be pointing in the same direction because that would be counterproductive. It is critical that they are related to one another because this optimises performance and helps us achieve our goals.
But how do we know if these predefined KPIs are compatible?
In business analytics and business intelligence, it’s critical to understand the relationship between two variables, such as your company’s value drivers and expected outcome. This is a critical component of your analytical approach’s success. This relationship can be discovered using a technique known as correlation analysis.
What Exactly Is Correlation Analysis?
Correlation analysis is a statistical technique used to determine whether two variables are related. Along with comparative and trend analysis, it is a component of business analytics.
In a business setting, this technique can be used to determine which variables influence any given outcome metric. It may highlight, for example, the extent to which a price relates to the quantity sold for a specific product, or whether job applicant scores are related to future employee performance.
It is critical to note that correlation establishes a statistical relationship but does not establish causation. A good example is the relationship between total arcade revenue and the number of Computer Science doctorates awarded in the United States. These two variables are statistically related in some geographies, but what may be causing the relationship is their mutual relationship to another “unknown” variable, such as recent technological advances. This means that determining the actual cause may necessitate additional investigation.
In any case, a correlational coefficient, which indicates the strength of the relationship between two variables, is unquestionably one of the key outputs of correlation analysis.
What Is The Meaning Of Multiple Correlation Analysis?
There are also techniques for determining the statistical relationship between two or more variables. They can assist in determining which variables have the greatest influence on a specific outcome metric. For example, we could conduct a multiple correlation analysis using price, season, and store placement to determine the aggregate relationship as well as the individual relationships of the variables on product quantities in a store.
What Benefits Does Correlation Analysis Have?
There are several advantages to incorporating this strategy into your business:
It is inexpensive. Standard business tools, such as Microsoft Excel, can be used to conduct the analysis. Alternatively, you can use open-source programming languages like Python and R to perform more complex correlation analysis.
Alternatively, you can use open-source programming languages like Python and R to perform more complex correlation analysis.
It is relatively simple to grasp. For business professionals, learning statistical principles and applying correlation analysis and interpreting the results is simple. After all, the correlation coefficient ranges from -1 to 1, making it easy to interpret.
It provides valuable insights. The technique provides information about the correlational relationship between variables, as well as insight into the strength and certainty of those relationships, as well as other factors that can aid decision making.
What Are The Negative Consequences Of Correlation Analysis?
Correlation analysis has some drawbacks, just like any other technique:
It does not establish causation on its own. If analysts do not use it correctly, they risk identifying variables that are related but do not cause each other. As a result, managers may focus on the wrong performance levers, as illustrated by the correlation between arcade revenue and computer science doctorates. Surely, fewer people playing video games will not result in fewer computer science Ph.D. graduates, right?
It must begin with a pre-defined set of variables. Managers must plan ahead of time which variables will be tested. More advanced data mining techniques, on the other hand, can aid in the identification of relationships within a larger set.
There are some prerequisites for using it in our analysis. For example, in order to obtain reliable results, we must provide numerical data and a minimum number of observations – not ideal for larger-scale businesses that deal with massive amounts of data in various forms.
What Is The Final Word On Correlation Analysis?
While there are obvious drawbacks to this method, they do not outweigh the benefits. Correlation analysis is useful for determining the relationship between two variables, which can be extremely beneficial to businesses looking to set quality KPIs and improve their operations.
We can safely conclude that it is relatively simple, easy to grasp, and extremely effective.
We can safely conclude that it is relatively simple, easy to understand, and extremely beneficial to a career in analytics – whether you are new to data science or looking for a career change. It could be very useful if you have the right amount of data and business managers have properly defined the variables. Overall, correlation analysis can be very beneficial to our business analytics goals.
