Book description
Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results
Key Features
 Study new techniques for marketing analytics
 Explore uses of machine learning to power your marketing analyses
 Work through each stage of data analytics with the help of multiple examples and exercises
Book Description
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.
The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.
By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
What you will learn
 Analyze and visualize data in Python using pandas and Matplotlib
 Study clustering techniques, such as hierarchical and kmeans clustering
 Create customer segments based on manipulated data
 Predict customer lifetime value using linear regression
 Use classification algorithms to understand customer choice
 Optimize classification algorithms to extract maximal information
Who this book is for
Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
Publisher resources
Table of contents
 Preface
 Chapter 1

Data Preparation and Cleaning
 Introduction
 Data Models and Structured Data
 pandas

Data Manipulation
 Selecting and Filtering in pandas
 Creating Test DataFrames in Python
 Adding and Removing Attributes and Observations
 Exercise 3: Creating and Modifying Test DataFrames
 Combining Data
 Handling Missing Data
 Exercise 4: Combining DataFrames and Handling Missing Values
 Applying Functions and Operations on DataFrames
 Grouping Data
 Exercise 5: Applying Data Transformations
 Activity 1: Addressing Data Spilling
 Summary
 Chapter 2
 Data Exploration and Visualization
 Chapter 3
 Unsupervised Learning: Customer Segmentation
 Chapter 4

Choosing the Best Segmentation Approach
 Introduction

Choosing the Number of Clusters
 Simple Visual Inspection
 Exercise 14: Choosing the Number of Clusters Based on Visual Inspection
 The Elbow Method with Sum of Squared Errors
 Exercise 15: Determining the Number of Clusters Using the Elbow Method
 Activity 5: Determining Clusters for HighEnd Clothing Customer Data Using the Elbow Method with the Sum of Squared Errors
 Different Methods of Clustering
 Evaluating Clustering
 Summary
 Chapter 5

Predicting Customer Revenue Using Linear Regression
 Introduction
 Understanding Regression

Feature Engineering for Regression
 Feature Creation
 Data Cleaning
 Exercise 20: Creating Features for Transaction Data
 Assessing Features Using Visualizations and Correlations
 Exercise 21: Examining Relationships between Predictors and Outcome
 Activity 8: Examining Relationships Between Storefront Locations and Features about Their Area
 Performing and Interpreting Linear Regression
 Summary
 Chapter 6
 Other Regression Techniques and Tools for Evaluation
 Chapter 7

Supervised Learning: Predicting Customer Churn
 Introduction
 Classification Problems
 Understanding Logistic Regression

Creating a Data Science Pipeline
 Obtaining the Data
 Exercise 28: Obtaining the Data
 Scrubbing the Data
 Exercise 29: Imputing Missing Values
 Exercise 30: Renaming Columns and Changing the Data Type
 Exploring the Data
 Statistical Overview
 Correlation
 Exercise 31: Obtaining the Statistical Overview and Correlation Plot
 Visualizing the Data
 Exercise 32: Performing Exploratory Data Analysis (EDA)
 Activity 13: Performing OSE of OSEMN
 Modeling the Data
 Summary
 Chapter 8
 FineTuning Classification Algorithms
 Chapter 9
 Modeling Customer Choice

Appendix
 Chapter 1: Data Preparation and Cleaning
 Chapter 2: Data Exploration and Visualization
 Chapter 3: Unsupervised Learning: Customer Segmentation
 Chapter 4: Choosing the Best Segmentation Approach
 Chapter 5: Predicting Customer Revenue Using Linear Regression
 Chapter 6: Other Regression Techniques and Tools for Evaluation
 Chapter 7: Supervised Learning: Predicting Customer Churn
 Chapter 8: FineTuning Classification Algorithms
 Chapter 9: Modeling Customer Choice
Product information
 Title: Data Science for Marketing Analytics
 Author(s):
 Release date: March 2019
 Publisher(s): Packt Publishing
 ISBN: 9781789959413
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