Essentials of Marketing Analytics
- Length: 480 pages
- Edition: 1
- Language: English
- Publisher: McGraw-Hill Education
- Publication Date: 2021-02-18
- ISBN-10: 1264263600
- ISBN-13: 9781264263608
- Sales Rank: #2181701 (See Top 100 Books)
The starting point in learning marketing analytics is to understand the marketing problem. The second is asking the right business question. The data will help you tell the story.
We live in a global, highly competitive, rapidly changing world that is increasingly influenced by digital data, expanded analytical capabilities, information technology, social media and more. The era of Big Data has literally brought about huge amounts of data to review, analyze and solve. Today’s undergraduate and graduate students will need to have a keen understanding of not only the right types of questions to ask, but also the tools available to help answer them. Essentials of Marketing Analytics covers both, in a comprehensive, readable and flexible manner.
Coverage includes the most popular analytics software tools, such as Tableau and Python, as well as a variety of analytical techniques, including but not limited to social network analysis, automated machine learning, neural networking and more. Supported by a robust student and learning package via McGraw Hill Connect, Essentials of Marketing Analytics 1e is the most comprehensive, current, adaptable product on the market!
Cover page Title page Copyright page Dedication About The Authors Preface Connect Brief Table of Contents Contents PART 1 Overview of Marketing Analytics and Data Management 1 Introduction to Marketing Analytics 1.1 Introduction to Marketing Analytics Marketing Analytics Defined Analytics Levels and Their Impact on Competitive Advantage 1.2 Defining the Right Business Problems 1.3 Data Sources 1.4 Data Types Types of Data Data Measurement Metric Measurement Scales 1.5 Predictors versus Target Variable Types of Variables 1.6 Modeling Types: Supervised Learning versus Unsupervised Learning 1.7 The 7-Step Marketing Analytics Process Step 1: Business Problem Understanding Step 2: Data Understanding and Collection Step 3: Data Preparation and Feature Selection Step 4: Modeling Development Step 5: Model Evaluation and Interpretation Step 6: Model and Results Communication Step 7: Model Deployment 1.8 Setting Yourself Apart Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 2 Data Management 2.1 The Era of Big Data Is Here 2.2 Database Management Systems (DBMS) 2.3 Enterprise Data Architecture Traditional ETL ETL Using Hadoop A Closer Look at Data Storage 2.4 Data Quality 2.5 Data Understanding, Preparation, and Transformation Data Understanding Data Preparation Data Transformation CASE STUDY: AVOCADO TOAST: A RECIPE TO LEARN SQL Getting Started Understanding the Dataset Applying the Concepts Aggregation Build Your Own Supplier Table Add Data to Your Table Join the Two Tables (MERGE) Update the Data Delete Values Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications PART 2 Exploring and Visualizing Data Patterns 3 Exploratory Data Analysis Using Cognitive Analytics 3.1 The Importance of Exploratory Data Analysis 3.2 Defining Cognitive Analytics and Knowledge Discovery The Cognitive Analytics Technology that Won Jeopardy 3.3 Discovering Different Use Cases for Cognitive Analytics Cognitive Analytics to Interface with the Customer Cognitive Analytics to Support Internal Operations and Decision Making 3.4 Combining Internal and External Data Sources for Improved Insights CASE STUDY: A CLOSER LOOK AT ONLINE CUSTOMER EXPERIENCE Understanding the Business Problem Understanding the Dataset Applying the Concepts Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 4 Data Visualization 4.1 What Is Data Visualization? 4.2 Principles and Elements of Design for Data Visualization Principles of Design The Basic Elements of Design 4.3 Fundamental Considerations When Developing Data Visualizations Common Types of Charts and Graphs 4.4 So, What’s Your Story? CASE STUDY: TELECOMMUNICATIONS: OPTIMIZING CUSTOMER ACQUISITION Understanding the Business Problem Understanding the Dataset Data Preparation Applying the Concepts Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications PART 3 Analytical Methods for Supervised Learning 5 Regression Analysis 5.1 What Is Regression Modeling? Simple Linear Regression Multiple Linear Regression Evaluating the Ability of the Regression Model to Predict 5.2 The Predictive Regression Model 5.3 Predictive Regression Performance 5.4 Model Validation 5.5 Modeling Categorical Variables 5.6 Model Independent Variable Selection Detecting Multicollinearity Feature Selection CASE STUDY: NEED A RIDE? PREDICTING PRICES THAT CUSTOMERS ARE WILLING TO PAY FOR RIDESHARING SERVICES Understanding the Business Problem Understanding the Dataset Data Preparation Applying the Concepts Step 1: Preparing the Data for Modeling Step 2: Setting Up the Training Model and Cross Validation Step 3: Evaluating the Model Results Step 4: Applying the Model to New Dataset Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 6 Neural Networks 6.1 Introduction to Neural Networks 6.2 How Are Neural Networks Used in Practice? 6.3 What Are the Basic Elements of a Neural Network? 6.4 How Does a Neural Network Learn? What Does This Process Look Like in Action? How Does the Network Learn? When Does the Network Stop Learning? 6.5 Key Reminders When Using Neural Networks CASE STUDY: AIRLINE INDUSTRY: UNDERSTANDING CUSTOMER SATISFACTION Understanding the Business Problem Understanding the Dataset Preparing the Data Applying the Concepts Stage 1: Preparing the Data for Modeling Stage 2: Setting Up the Training Model and Cross Validation Stage 3: Evaluating the Model Results Stage 4: Applying the Model to a New Dataset Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 7 Automated Machine Learning 7.1 What Is Automated Machine Learning (AutoML)? What Questions Might Arise? 7.2 AutoML in Marketing Which Companies Are Actively Using AutoML? 7.3 What Are Key Steps in the Automated Machine Learning Process? Data Preparation Model Building Creating Ensemble Models Advanced Ensemble Methods Model Recommendation CASE STUDY: LOAN DATA: UNDERSTANDING WHEN AND HOW TO SUPPORT FISCAL RESPONSIBILITY IN CUSTOMERS Understanding the Business Problem Understanding the Dataset Uploading the Data Examining the Features Defining the Target Variable Running the Model Evaluating the Model Results Applying the Model to predict new cases Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications PART 4 Analytical Methods for Unsupervised Learning 8 Cluster Analysis 8.1 What Is Cluster Analysis? 8.2 How Is Cluster Analysis Used in Practice? 8.3 How Does a Cluster Analysis Function? 8.4 What Are the Types of Cluster Analysis? K-Means Clustering K-Means Issues to Remember Hierarchical Clustering Hierarchical Clustering Issues to Remember CASE STUDY: ONLINE PERFUME AND COSMETIC SALES: UNDERSTANDING CUSTOMER SEGMENTATION THROUGH CLUSTER ANALYSIS Understanding the Business Problem Understanding the Dataset Applying the Concepts Opening Python Using Anaconda Preparing the Python Environment Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 9 Market Basket Analysis 9.1 What Is Market Basket Analysis? 9.2 How Is Market Basket Analysis Used in Practice? 9.3 Association Rules: How Does a Market Basket Analysis Identify Product Relationships? 9.4 Special Topics in Market Basket Analysis CASE STUDY: ONLINE DEPARTMENT STORE: UNDERSTANDING CUSTOMER PURCHASE PATTERNS Understanding the Business Problem Understanding the Dataset Data Preparation Applying the Concepts Loading Data Preparing the Data Running FP-Growth Creating Association Rules Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications PART 5 Emerging Analytical Approaches 10 Natural Language Processing 10.1 What Is Natural Language Processing? 10.2 How Is Natural Language Processing Used in Practice? Optimize Inventory and Engage Customers in Marketing Campaigns Produce New Products to Meet Customer Needs Simplify Guest Travel to Improve Hospitality Create a Better Experience for Customers Add Unique Features to Products Improve Customer Service Facilitate Customer Ordering Strengthen Customer Relationships 10.3 How Is Text Analytics Applied? Step 1: Text Acquisition and Aggregation Step 2: Text Preprocessing Tokenization Stemming Lemmatization Stop Words Removal N-Grams Bag of Words Term-Document Matrix Step 3: Text Exploration Frequency Bar Chart Word Clouds Step 4: Text Modeling 10.4 Special Topics in Text Analytics CASE STUDY: SPECIALTY FOOD ONLINE REVIEW: UNDERSTANDING CUSTOMER SENTIMENTS Understanding the Business Problem Understanding the Dataset Data Preparation Applying the Concepts Opening Python Using Anaconda Preparing the Python Environment Text Preprocessing Topic Modeling Sentiment Analysis Using TextBlob Sentiment Analysis Using Vader Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 11 Social Network Analysis 11.1 What Is Social Network Analysis? 11.2 Social Network Analysis in Practice 11.3 How Does a Social Network Analysis Function? Network Measures Measures of Centrality Network Structures 11.4 Link Prediction Using Social Network Analysis CASE STUDY: AUTO INDUSTRY: UNDERSTANDING NETWORK INFLUENCERS Understanding the Business Problem Understanding the Dataset Data Preparation Applying the Concepts Step 1: Getting Started with Polinode Step 2: Uploading Data to Polinode Step 3: Viewing the Network Graph Step 4: Measuring Network Properties Step 5: Updating Nodes Graph View Step 6: Running a Network Report and Downloading Results Insights Learned from Applying the Concepts Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications 12 Fundamentals of Digital Marketing Analytics 12.1 What Are the Basics of Digital Marketing? What Is Owned Digital Media? What Is Paid Digital Media? What Is Earned Digital Media? How Is Digital Marketing Used? 12.2 Digital Marketing Analytics in Practice Owned Digital Marketing Media Paid Digital Marketing Media Earned Digital Marketing Media 12.3 Digital Marketing Analytics Measures Audience Analysis Acquisition Analysis Behavior Analysis Conversion Analysis A/B Testing Multivariate Testing Multichannel Attribution 12.4 How Does A/B Testing Work? CASE STUDY: E-COMMERCE: THE GOOGLE ONLINE MERCHANDISE STORE Understanding the Business Problem Understanding the Dataset Applying the Concepts Getting Started with Google Analytics Step 1: Accessing the Demo Account Step 2: Reviewing the Main Dashboard Step 3: Reviewing the Reports Insights Learned from Applying the Concepts A Final Note Summary of Learning Objectives and Key Terms Discussion and Review Questions Critical Thinking and Marketing Applications Glossary Index
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