Book Image

Data Science for Marketing Analytics - Second Edition

By : Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali
Book Image

Data Science for Marketing Analytics - Second Edition

By: Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali

Overview of this book

Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Table of Contents (11 chapters)
Preface

Choosing the Number of Clusters

While performing segmentation in the previous chapter, we specified the number of clusters to the k-means algorithm. In practice, though, we don't typically know the number of clusters to expect in the data. While an analyst or business team may have some intuition that may be very different from the 'natural' clusters that are available in the data. For instance, a business may have an intuition that there are generally three types of customers. But an analysis of the data may point to five distinct groups of customers. Recall that the features that we choose and the scale of those features also play an important role in defining 'similarity' between customers.

There is, hence, a need to understand the different ways we can choose the 'right' number of clusters. In this chapter, we will discuss three approaches. First, we will learn about simple visual inspection, which has the advantages of being easy and intuitive...