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.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction and Environment Setup
Section 1: Introduction and Environment Setup
2
Data Science and Marketing
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
3
Section 2: Descriptive Versus Explanatory Analysis
Section 2: Descriptive Versus Explanatory Analysis
4
Key Performance Indicators and Visualizations
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
5
Drivers behind Marketing Engagement
Drivers behind Marketing Engagement
Using regression analysis for explanatory analysis
Regression analysis with Python
Regression analysis with R
Summary
6
From Engagement to Conversion
From Engagement to Conversion
Decision trees
Decision trees and interpretations with Python
Decision trees and interpretations with R
Summary
7
Section 3: Product Visibility and Marketing
Section 3: Product Visibility and Marketing
8
Product Analytics
Product Analytics
The importance of product analytics
Product analytics using Python
Product analytics using R
Summary
9
Recommending the Right Products
Recommending the Right Products
Collaborative filtering and product recommendation
Building a product recommendation algorithm with Python
Building a product recommendation algorithm with R
Summary
10
Section 4: Personalized Marketing
Section 4: Personalized Marketing
11
Exploratory Analysis for Customer Behavior
Exploratory Analysis for Customer Behavior
Customer analytics – understanding customer behavior
Conducting customer analytics with Python
Conducting customer analytics with R
Summary
12
Predicting the Likelihood of Marketing Engagement
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
13
Customer Lifetime Value
Customer Lifetime Value
CLV
Evaluating regression models
Predicting the 3 month CLV with Python
Predicting the 3 month CLV with R
Summary
14
Data-Driven Customer Segmentation
Data-Driven Customer Segmentation
Customer segmentation
Clustering algorithms
Segmenting customers with Python
Segmenting customers with R
Summary
15
Retaining Customers
Retaining Customers
Customer churn and retention
Artificial neural networks
Predicting customer churn with Python
Predicting customer churn with R
Summary
16
Section 5: Better Decision Making
Section 5: Better Decision Making
17
A/B Testing for Better Marketing Strategy
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
18
What's Next?
What's Next?
Recap of the topics covered in this book
Real-life data science challenges
More machine learning models and packages
Summary
19
Other Books You May Enjoy
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Leave a review - let other readers know what you think

Evaluating regression models

We need to use a different set of metrics for evaluating regression models from those for classification model evaluations. This is because the prediction output of a regression model takes continuous values, meaning it can take any value and is not restricted to taking from a predefined set of values. On the other hand, as we have seen in Chapter 8, Predicting the Likelihood of Marketing Engagement, the prediction output of a classification model can only take a certain number of values. As was the case for the engagement prediction, our classification model from the previous chapter could only take two values—zero for no engagement and one for engagement. Because of this difference, we need to use different metrics to evaluate regression models.

In this section, we are going to discuss four commonly used methodologies to evaluate regression...

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