Data Science for Marketing Analytics: Achieve your marketing goals with the data analytics power of Python

In the previous chapter, we saw how to transform data and attributes obtained from raw sources into expected attributes and values through pandas. After structuring data into a tabular form, with each field containing the expected (correct and clean) values, we can say that this data is prepared for further analysis, which involves utilizing the prepared data to solve business problems. To ensure the best outcomes for a project, we need to be clear about the scope of the data, the questions we can address with it, and what problems we can solve with it before we can make any useful inference from the data.

To do that, not only do we need to understand the kind of data we have, but also the way some attributes are related to other attributes, what attributes are useful for us, and how they vary in the data provided. Performing this analysis on data and exploring ways we can use it, is not a straightforward task. We have to perform several initial exploratory tests on our data...

Given a structured marketing dataset, the first thing you should do is to try and build intuition for the data and create insights. It is also possible to make a call on whether a certain attribute is required for the analysis or not. The insights generated should instinctively agree with the values and there should be no doubts about the quality of the data, its interpretation, or its application for solving the business problems we are interested in. If some values don't make intuitive sense, we must dig deeper into the data, remove outliers, and understand why the attribute has those values. This is important in order to avoid inaccurate model creation, building a model on the wrong data, or the inefficient use of resources.

Before we start with the model creation, we should summarize the attributes in our data and objectively compare them with our business expectations. To quantify business expectations, we generally have target metrics whose relationships...

Once we have identified the KPIs for our analysis, we can proceed to make insights with respect to only those variables that affect the bottom line of the KPIs.

sales...

An important aspect of exploring data is to be able to represent the data visually. When data is represented visually, the underlying numbers and distribution become very easy to understand and differences become easy to spot.

Plots in Python are very similar to those in any other paradigm of traditional marketing analytics. We can directly make use of our previous understanding of plots and use them in Python. pandas supports inbuilt functions to visualize the data in them through the plot function. You can choose which ones are which via the kind parameter to the plot function. Some of the most commonly used ones, as used on sales.csv, are as follows:

You can specify which values to pass as the x and y axes by specifying the column names as x and y in the DataFrames.

In this chapter, we have explored the way data is processed and have figured out intuitive details about it. We can now perform advanced analysis and create visualizations to make processing easy to understand. We can use the knowledge that we have gained to solve further problems, explore hidden relationships, and do further analysis easily. Let's look at some applications of this with problems we can solve in the next chapter.