In this chapter, we are going to expand the knowledge we gained from the previous chapter and the customer analytics exercise we conducted in Chapter 7, Exploratory Analysis for Customer Behavior. For successful and more intelligent marketing strategies, we cannot stop at analyzing customer data. With the advanced technology in data science and machine learning, we can now make intelligent guesses and estimates on customers' future behaviors, such as what types of customers are more likely to engage with marketing efforts, the amount of purchases that customers are likely to make, or which customers are likely to churn. These predictions or intelligent guesses that are built based on historical customer data can help you improve your marketing performance and further tailor your marketing strategies for different target audiences...
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Book Overview & Buying
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Table Of Contents
Hands-On Data Science for Marketing
By :
Hands-On Data Science for Marketing
By:
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)
Preface
Free Chapter
Section 1: Introduction and Environment Setup
Data Science and Marketing
Section 2: Descriptive Versus Explanatory Analysis
Key Performance Indicators and Visualizations
Drivers behind Marketing Engagement
From Engagement to Conversion
Section 3: Product Visibility and Marketing
Product Analytics
Recommending the Right Products
Section 4: Personalized Marketing
Exploratory Analysis for Customer Behavior
Predicting the Likelihood of Marketing Engagement
Customer Lifetime Value
Data-Driven Customer Segmentation
Retaining Customers
Section 5: Better Decision Making
A/B Testing for Better Marketing Strategy
What's Next?
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