Mastering Marketing Data Science : A Comprehensive Guide for Today’s Marketers

Unlock the Power of Data: Transform Your Marketing Strategies with Data Science

Cover
Table of Contents
Wiley and SAS Business Series
Title Page
Copyright
Preface
NAVIGATING THE INTERSECTION OF MARKETING AND DATA SCIENCE
Acknowledgments
About the Author
CHAPTER 1: Introduction to Marketing Data Science
1.1 WHAT IS MARKETING DATA SCIENCE?
1.2 THE ROLE OF DATA SCIENCE IN MARKETING
1.3 MARKETING ANALYTICS VERSUS DATA SCIENCE
1.4 KEY CONCEPTS AND TERMINOLOGY
1.5 STRUCTURE OF THIS BOOK
1.6 PRACTICAL EXAMPLE 1: APPLYING DATA SCIENCE TO IMPROVE CROSS-SELLING IN A RETAIL BANK MARKETING DEPARTMENT
1.7 PRACTICAL EXAMPLE 2: THE IMPACT OF DATA SCIENCE ON A MARKETING CAMPAIGN
1.8 CONCLUSION
1.9 REFERENCES
CHAPTER 2: Data Collection and Preparation
2.1 INTRODUCTION
2.2 DATA SOURCES IN MARKETING: EVOLUTION AND THE EMERGENCE OF BIG DATA
2.3 DATA COLLECTION METHODS
2.4 DATA PREPARATION
2.5 PRACTICAL EXAMPLE: COLLECTING AND PREPARING DATA FOR A CUSTOMER CHURN ANALYSIS
2.6 CONCLUSION
2.7 REFERENCES
EXERCISE 2.1: DATA CLEANING AND TRANSFORMATION
EXERCISE 2.2: DATA AGGREGATION AND REDUCTION
CHAPTER 3: Descriptive Analytics in Marketing
3.1 INTRODUCTION
3.2 OVERVIEW OF DESCRIPTIVE ANALYTICS
3.3 DESCRIPTIVE STATISTICS FOR MARKETING DATA
3.4 DATA VISUALIZATION TECHNIQUES
3.5 EXPLORATORY DATA ANALYSIS IN MARKETING
3.6 ANALYZING MARKETING CAMPAIGN PERFORMANCE
3.7 PRACTICAL EXAMPLE: DESCRIPTIVE ANALYTICS FOR A BEVERAGE COMPANY'S SOCIAL MEDIA MARKETING CAMPAIGN
3.8 CONCLUSION
3.9 REFERENCES
EXERCISE 3.1: DESCRIPTIVE ANALYSIS OF MARKETING DATA
EXERCISE 3.2: DATA VISUALIZATION AND INTERPRETATION
CHAPTER 4: Inferential Analytics and Hypothesis Testing
4.1 INTRODUCTION
4.2 INFERENTIAL ANALYTICS IN MARKETING
4.3 CONFIDENCE INTERVALS
4.4 A/B TESTING IN MARKETING
4.5 HYPOTHESIS TESTING IN MARKETING
4.6 CUSTOMER SEGMENTATION AND PROCESSING
4.7 PRACTICAL EXAMPLES: INFERENTIAL ANALYTICS FOR CUSTOMER SEGMENTATION AND HYPOTHESIS TESTING FOR MARKETING CAMPAIGN PERFORMANCE
4.8 CONCLUSION
4.9 REFERENCES
EXERCISE 4.1: BAYESIAN INFERENCE FOR PERSONALIZED MARKETING
EXERCISE 4.2: A/B TESTING FOR MARKETING CAMPAIGN EVALUATION
CHAPTER 5: Predictive Analytics and Machine Learning
5.1 INTRODUCTION
5.2 PREDICTIVE ANALYTICS TECHNIQUES
5.3 MACHINE LEARNING TECHNIQUES
5.4 MODEL EVALUATION AND SELECTION
5.5 CHURN PREDICTION, CUSTOMER LIFETIME VALUE, AND PROPENSITY MODELING
5.6 MARKET BASKET ANALYSIS AND RECOMMENDER SYSTEMS
5.7 PRACTICAL EXAMPLES: PREDICTIVE ANALYTICS AND MACHINE LEARNING IN MARKETING
5.8 CONCLUSION
5.9 REFERENCES
EXERCISE 5.1: CHURN PREDICTION MODEL
EXERCISE 5.2: PREDICT WEEKLY SALES
CHAPTER 6: Natural Language Processing in Marketing
6.0 BEGINNER-FRIENDLY INTRODUCTION TO NATURAL LANGUAGE PROCESSING IN MARKETING
6.1 INTRODUCTION TO NATURAL LANGUAGE PROCESSING
6.2 TEXT PREPROCESSING AND FEATURE EXTRACTION IN MARKETING NATURAL LANGUAGE PROCESSING
6.3 KEY NATURAL LANGUAGE PROCESSING TECHNIQUES FOR MARKETING
6.4 CHATBOTS AND VOICE ASSISTANTS IN MARKETING
6.5 PRACTICAL EXAMPLES OF NATURAL LANGUAGE PROCESSING IN MARKETING
6.6 CONCLUSION
6.7 REFERENCES
EXERCISE 6.1: SENTIMENT ANALYSIS
EXERCISE 6.2: TEXT CLASSIFICATION
CHAPTER 7: Social Media Analytics and Web Analytics
7.1 INTRODUCTION
7.2 SOCIAL NETWORK ANALYSIS
7.3 WEB ANALYTICS TOOLS AND METRICS
7.4 SOCIAL MEDIA LISTENING AND TRACKING
7.5 CONVERSION RATE OPTIMIZATION
7.6 CONCLUSION
7.7 REFERENCES
EXERCISE 7.1: SOCIAL NETWORK ANALYSIS (SNA) IN MARKETING
EXERCISE 7.2: WEB ANALYTICS FOR MARKETING INSIGHTS
CHAPTER 8: Marketing Mix Modeling and Attribution
8.1 INTRODUCTION
8.2 MARKETING MIX MODELING CONCEPTS
8.3 DATA-DRIVEN ATTRIBUTION MODELS
8.4 MULTI-TOUCH ATTRIBUTION
8.5 RETURN ON MARKETING INVESTMENT
8.6 CONCLUSION
8.7 REFERENCES
EXERCISE 8.1: MARKETING MIX MODELING (MMM)
EXERCISE 8.2: DATA-DRIVEN ATTRIBUTION
CHAPTER 9: Customer Journey Analytics
9.1 INTRODUCTION
9.2 CUSTOMER JOURNEY MAPPING
9.3 TOUCHPOINT ANALYSIS
9.4 CROSS-CHANNEL MARKETING OPTIMIZATION
9.5 PATH TO PURCHASE AND ATTRIBUTION ANALYSIS
9.6 CONCLUSION
9.7 REFERENCES
EXERCISE 9.1: CREATING A CUSTOMER JOURNEY MAP
EXERCISE 9.2: TOUCHPOINT EFFECTIVENESS ANALYSIS
CHAPTER 10: Experimental Design in Marketing
10.1 INTRODUCTION
10.2 DESIGN OF EXPERIMENTS
10.3 FRACTIONAL FACTORIAL DESIGNS
10.4 MULTI-ARMED BANDITS
10.5 ONLINE AND OFFLINE EXPERIMENTS
10.6 CONCLUSION
10.7 REFERENCES
EXERCISE 10.1: ANALYZING A SIMPLE A/B TEST
EXERCISE 10.2: FRACTIONAL FACTORIAL DESIGN IN AD OPTIMIZATION
CHAPTER 11: Big Data Technologies and Real-Time Analytics
11.1 INTRODUCTION
11.2 BIG DATA
11.3 DISTRIBUTED COMPUTING FRAMEWORKS
11.4 REAL-TIME ANALYTICS TOOLS AND TECHNIQUES
11.5 PERSONALIZATION AND REAL-TIME MARKETING
11.6 CONCLUSION
11.7 REFERENCES
CHAPTER 12: Generative Artificial Intelligence and Its Applications in Marketing
12.1 INTRODUCTION
12.2 UNDERSTANDING GENERATIVE ARTIFICIAL INTELLIGENCE: BASICS AND PRINCIPLES
12.3 IMPLEMENTING GENERATIVE ARTIFICIAL INTELLIGENCE IN CONTENT CREATION AND PERSONALIZATION
12.4 GENERATIVE ARTIFICIAL INTELLIGENCE IN PREDICTIVE ANALYTICS AND CUSTOMER BEHAVIOR MODELING
12.5 ETHICAL CONSIDERATIONS AND FUTURE PROSPECTS OF GENERATIVE ARTIFICIAL INTELLIGENCE IN MARKETING
12.6 CONCLUSION
12.7 REFERENCES
CHAPTER 13: Ethics, Privacy, and the Future of Marketing Data Science
13.1 INTRODUCTION
13.2 ETHICAL CONSIDERATIONS IN MARKETING DATA SCIENCE
13.3 DATA PRIVACY REGULATIONS
13.4 BIAS, FAIRNESS, AND TRANSPARENCY
13.5 EMERGING TRENDS AND THE FUTURE OF MARKETING DATA SCIENCE
13.6 CONCLUSION
13.7 REFERENCES
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