Book Image

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Book Image

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Table of Contents (12 chapters)
Data Science for Marketing Analytics
Preface

Generating Targeted Insights


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.

Selecting and Renaming Attributes

After we have explored our attributes, we might feel like the variation in the data for a certain attribute could be understood more clearly if it were focused on individually. As explained in detail in the previous chapter, we can select parts of data in pandas through the following methods:

  • [cols]: This method selects the columns to be displayed.

  • loc[label]: This method selects rows by label or Boolean condition.

  • loc[row_labels, cols]: This method selects rows in row_labels and their values in the cols columns.

  • iloc[location]: This method selects rows by integer location. It can be used to pass a list of row indices, slices, and so on.

For example, we can select Revenue, Quantity, and Gross Profit columns from the United States in the sales DataFrame, as follows:

sales...