Uncover Relationship in Exploratory Data Analysis(EDA)

Every analysis starts with getting to know our data. This includes (but is not limited to): understanding the way the data is structured, removing unneeded columns, addressing nulls, running summary statistics and exploring possible relationships by creating segments or groups, and finally conducting visual exploration by plotting the data.

Using Python you can define a loop that runs the through the different segments of our population and calculates the conversion rate for each. Our segments can be anything from age groups, engagement level, or any other segment of interest. Python allows you to quickly explore conversion on a more granular level, something every successful marketing campaign needs. Without Python you will find yourself spending a significant amount of time repeating similar analyses.

Every marketing campaign has specific goals, and majority of campaigns will focus on a conversion metric. Let’s say you’re sending an email blast letting users know about a time limited discount for your product. To properly analyze the impact of your campaign you’ll need to define conversion metrics, such as the conversion rate of users who redeemed the discount of those targeted with the email, as well as the conversion rate of users who continued to use the product after redeeming the discount.

Your campaign will likely convert some segments of users more than others, so you’ll want to repeat the analysis on diverse segments of the population to identify differences in impact.