Book Image

Hands-On Data Science for Marketing

By : Yoon Hyup Hwang
Book Image

Hands-On Data Science for Marketing

By: Yoon Hyup Hwang

Overview of this book

Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Introduction and Environment Setup
3
Section 2: Descriptive Versus Explanatory Analysis
7
Section 3: Product Visibility and Marketing
10
Section 4: Personalized Marketing
16
Section 5: Better Decision Making

Summary

In this chapter, we have learned about customer churn and retention. We have discussed the reasons why customer churn hurts businesses. More specifically, we have learned how retaining existing customers is much less expensive than acquiring new customers. We have shown some of the common reasons why customers leave a company, such as poor customer service, not finding enough value in products or services, lack of communications, and lack of customer loyalty. In order to understand why customers leave, we could conduct surveys or analyze customer data to understand their needs and pain points better. We have also discussed how we can train ANN models to identify those customers who are at risk of churning. Through programming exercises, we have learned how to use the keras library to build and train ANN models in Python and R.

In the following chapter, we are going to...