Hands-On Data Science for Marketing (Paperback)

Optimize your marketing strategies through analytics and machine learning

Key Features

• Understand how data science drives successful marketing campaigns
• Use machine learning for better customer engagement, retention, and product recommendations
• Extract insights from your data to optimize marketing strategies and increase profitability

Book Description

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.

What you will learn

• Learn how to compute and visualize marketing KPIs in Python and R
• Master what drives successful marketing campaigns with data science
• Use machine learning to predict customer engagement and lifetime value
• Make product recommendations that customers are most likely to buy
• Learn how to use A/B testing for better marketing decision making
• Implement machine learning to understand different customer segments