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

Introduction


In the previous chapter, we saw how to build plots using the built-in function of pandas, and learned how to estimate the mean, median, and other descriptive statistics about specific consumer or product groups.

In this chapter, we will learn about clustering, a form of unsupervised learning technique, and then begin a discussion of how to calculate the similarity between two data points. Next, we will discuss how to standardize data so that multiple data features can be used without one overwhelming the others. We will also go through how similarity can be calculated by computing the distance between data points. Finally, we will discuss k-means clustering, how to perform it, and how to explore the resulting groups.