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Hands-On Data Science for Marketing

By : Yoon Hyup Hwang

4.1 (14)

Buy this Book

Hands-On Data Science for Marketing

4.1 (14)

By: Yoon Hyup Hwang

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Section 1: Introduction and Environment Setup

Data Science and Marketing

Technical requirements

Trends in marketing

Applications of data science in marketing

Setting up the Python environment

Setting up the R environment

Summary

Section 2: Descriptive Versus Explanatory Analysis

Key Performance Indicators and Visualizations

KPIs to measure performances of different marketing efforts

Computing and visualizing KPIs using Python

Computing and visualizing KPIs using R

Summary

Drivers behind Marketing Engagement

Using regression analysis for explanatory analysis

Regression analysis with Python

Regression analysis with R

Summary

From Engagement to Conversion

Decision trees

Decision trees and interpretations with Python

Decision trees and interpretations with R

Summary

Section 3: Product Visibility and Marketing

Product Analytics

The importance of product analytics

Product analytics using Python

Product analytics using R

Summary

Recommending the Right Products

Collaborative filtering and product recommendation

Building a product recommendation algorithm with Python

Building a product recommendation algorithm with R

Summary

Section 4: Personalized Marketing

Exploratory Analysis for Customer Behavior

Customer analytics – understanding customer behavior

Conducting customer analytics with Python

Conducting customer analytics with R

Summary

Predicting the Likelihood of Marketing Engagement

Predictive analytics in marketing

Evaluating classification models

Predicting the likelihood of marketing engagement with Python

Predicting the likelihood of marketing engagement with R

Summary

Customer Lifetime Value

CLV

Evaluating regression models

Predicting the 3 month CLV with Python

Predicting the 3 month CLV with R

Summary

Data-Driven Customer Segmentation

Customer segmentation

Clustering algorithms

Segmenting customers with Python

Segmenting customers with R

Summary

Retaining Customers

Customer churn and retention

Artificial neural networks

Predicting customer churn with Python

Predicting customer churn with R

Summary

Section 5: Better Decision Making

A/B Testing for Better Marketing Strategy

A/B testing for marketing

Statistical hypothesis testing

Evaluating A/B testing results with Python

Evaluating A/B testing results with R

Summary

What's Next?

Recap of the topics covered in this book

Real-life data science challenges

More machine learning models and packages

Summary

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Regression analysis with Python

In this section, you will learn how to use the statsmodels package in Python to conduct regression analysis. For those readers that would like to use R instead of Python, for this exercise, you can skip to the next section. We will start this section by looking at the data more closely, using the pandas and matplotlib packages, and then we will discuss how to build regression models and interpret the results by using the statsmodels library.

For this exercise, we will be using one of the publicly available datasets from IBM Watson, which can be found at https://www.ibm.com/communities/analytics/watson-analytics-blog/marketing-customer-value-analysis/. You can follow the link and download the data file in a CSV format. In order to load this data into your Jupyter Notebook, you can run the following code:

import matplotlib.pyplot as plt
import pandas...

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Hands-On Data Science for Marketing

By : Yoon Hyup Hwang

Hands-On Data Science for Marketing

By: Yoon Hyup Hwang

Overview of this book

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