Why Do Marketing Professionals Need To Understand Statistics?

You do not need to be a math “nerd” to understand the importance of analytics. As marketers, we have many tools at our disposal and although it can create anxiety for even the most seasoned professionals, “hard-core statistics” are probably one of the most reliable KPIs.

To that end, learning to utilize the insights derived from regression statistics is no longer a specialty field, but should be understood by any marketing professional in a leadership position. Regression sounds undeniably “mysterious” and to many, mathematics, let alone statistics, can seem out of reach, but it does not need to be that way.

What are we really talking about here?

A regression analysis is used in statistics to figure out if there is a relationship, or “correlation,” between TWO variables.

As marketers, how often are we asked to measure only TWO variables at any given time? That is why we must go a step further and understand a subcategory, known as multiple regression analysis. This model is both an exciting and powerful tool because multiple regression analysis does not limit us only to comparing two variables to one another. If you have more than one independent variable (or “predictor”) affecting your data, you will definitely want to know which one of them is individually influencing trends so that you can isolate it and ultimately use these trends to scale your campaigns and create the greatest ROI.

To do this, you need to run a multiple regression analysis. More precisely, a multiple regression analysis helps determine if a set of dependent variables has an influence on performance. Think of it like most of the more linear tests you run, where you are looking to test the individual regression of two or more independent variables on the same dependent variable. For example…

In a linear regression analysis, your Y-axis = PPC and your X-axis = Time.

In a multiple regression analysis your Y-axis = PPC, X¹ = Time, X² = Budget, and X³ = Number of Articles.

Just Breathe: How to Interpret Multiple Regression Analysis…

In the first example (linear regression), we are simply testing to see if TIME impacts the success of your PPC campaign. In the second example, we have options, as we are able to measure the relationship between time, budget and number of articles and understand the impact of each one (Y) accordingly. So, when analyzing the success of any PPC campaign, the trick is to begin with one linear regression test between Y and X¹ and then, run multiple regression tests to find your x², and x³.

Relevance to online marketing

Regression models are used to understand the customer journey using web analytics and other data to support Omni-channel marketing. Marketing has become a data-driven arena and as a result, we must all feel more comfortable pulling from our statistics knowledge bank. Multiple regression analysis is a powerful leaping off point into the nearly infinite world of statistically driven KPIs and allows for a more robust and multi-faceted means by which to gain insight into different marketing metrics and their true relationships.