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

Mastering Marketing Data Science A Comprehensive Guide for Today's Marketers

"Within the realm of marketing, data science plays a critical role in unlocking valuable insights and driving strategic decision-making. This dynamic field encompasses a variety of key factors that collectively contribute to its power and effectiveness. These factors include the collection and...

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Detalles Bibliográficos
Autor principal: Brown, Iain (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Colección:Wiley and SAS Business Series
Materias:
Marketing > Data processing.
Data mining.
Marketing > Planning.
Consumer behavior.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820531006719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • 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
  • 1.4.1 Data Science
  • 1.4.2 Data Visualization
  • 1.4.3 Customer Segmentation
  • 1.4.4 Predictive Analytics
  • 1.4.5 Machine Learning
  • 1.4.6 Natural Language Processing
  • 1.4.7 Marketing Mix Modeling
  • 1.4.8 Big Data
  • 1.5 Structure of This Book
  • 1.6 Practical Example 1: Applying Data Science to Improve Cross-Selling in a Retail Bank Marketing Department
  • 1.6.1 Data Collection
  • 1.6.2 Data Preparation
  • 1.6.3 Customer Segmentation
  • 1.6.4 Product Recommendation Modeling
  • 1.6.5 Campaign Design
  • 1.6.6 A/B Testing and Evaluation
  • 1.6.7 Monitoring and Refinement
  • 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
  • 2.2.1 Traditional Data Sources
  • 2.2.2 The Emergence of Modern Data Sources
  • 2.2.3 Big Data and Its Impact on Marketing
  • 2.3 Data Collection Methods
  • 2.3.1 Surveys and Questionnaires
  • 2.3.2 Web Scraping
  • 2.3.3 Application Programming Interfaces
  • 2.3.4 Data Purchase
  • 2.3.5 Observational Data
  • 2.4 Data Preparation
  • 2.4.1 Data Cleaning
  • 2.4.2 Data Integration
  • 2.4.3 Data Transformation
  • 2.4.4 Data Reduction
  • 2.5 Practical Example: Collecting and Preparing Data for a Customer Churn Analysis
  • 2.6 Conclusion
  • 2.7 References
  • Chapter 3 Descriptive Analytics in Marketing
  • 3.1 Introduction
  • 3.2 Overview of Descriptive Analytics.
  • 3.2.1 The Role of Descriptive Analytics in Marketing
  • 3.2.2 Key Techniques in Descriptive Analytics
  • 3.2.3 The Importance of Data Visualization
  • 3.3 Descriptive Statistics for Marketing Data
  • 3.3.1 Measures of Central Tendency
  • 3.3.2 Measures of Dispersion
  • 3.3.3 Measures of Association
  • 3.3.4 Symmetry and Skewness
  • 3.4 Data Visualization Techniques
  • 3.4.1 Bar Charts and Histograms
  • 3.4.2 Line Charts and Time Series Plots
  • 3.4.3 Scatterplots and Bubble Charts
  • 3.4.4 Heat Maps and Geographic Maps
  • 3.4.5 Visualization Best Practices
  • 3.5 Exploratory Data Analysis in Marketing
  • 3.5.1 Data Distribution Analysis
  • 3.5.2 Correlation and Covariance
  • 3.5.3 Visual Exploratory Techniques
  • 3.6 Analyzing Marketing Campaign Performance
  • 3.6.1 Key Performance Indicators
  • 3.6.2 Techniques for Analyzing Marketing Campaign Performance
  • 3.6.3 The Role of Descriptive Analytics in Marketing Campaign Performance Analysis
  • 3.7 Practical Example: Descriptive Analytics for a Beverage Company's Social Media Marketing Campaign
  • 3.7.1 Data Collection and Preparation
  • 3.7.2 Selection of Key Performance Indicators
  • 3.7.3 Descriptive Analytics
  • 3.7.4 Segmentation Analysis
  • 3.7.5 Insights and Optimization
  • 3.8 Conclusion
  • 3.9 References
  • Chapter 4 Inferential Analytics and Hypothesis Testing
  • 4.1 Introduction
  • 4.2 Inferential Analytics in Marketing
  • 4.2.1 Overview of Inferential Analytics
  • 4.2.2 Basics of Probability
  • 4.2.3 Parametric Versus Nonparametric Tests
  • 4.2.4 Key Concepts in Inferential Analytics
  • 4.3 Confidence Intervals
  • 4.3.1 Estimating Population Mean
  • 4.3.2 Margin of Error and Level of Confidence
  • 4.3.3 Interpreting Confidence Intervals
  • 4.3.4 Practical Example: Confidence Interval in Marketing Campaign Evaluation
  • 4.4 A/B Testing in Marketing
  • 4.4.1 Basics and Importance.
  • 4.4.2 Experimental Design for A/B Tests
  • 4.4.3 Setting Up A/B Tests: A Step-by-Step Guide
  • 4.4.4 Statistical Significance in A/B Tests
  • 4.4.5 Advanced A/B Testing Techniques
  • 4.4.6 Potential Pitfalls in A/B Testing
  • 4.4.7 Interpreting A/B Test Results
  • 4.5 Hypothesis Testing in Marketing
  • 4.5.1 Introduction to Hypothesis Testing
  • 4.5.2 Common Hypothesis Tests in Marketing
  • 4.5.3 Significance Levels and P-Values
  • 4.6 Customer Segmentation and Processing
  • 4.6.1 K-Means Clustering
  • 4.6.2 Hierarchical Clustering in Customer Segmentation
  • 4.6.3 Recency, Frequency, Monetary Analysis in Marketing
  • 4.7 Practical Examples: Inferential Analytics for Customer Segmentation and Hypothesis Testing for Marketing Campaign Performance
  • 4.7.1 Inferential Analytics for Customer Segmentation
  • 4.7.2 Hypothesis Testing for Marketing Campaign Performance
  • 4.8 Conclusion
  • 4.9 References
  • Chapter 5 Predictive Analytics and Machine Learning
  • 5.1 Introduction
  • 5.1.1 Overview of Predictive Analytics
  • 5.1.2 Machine Learning in Marketing
  • 5.1.3 Common Challenges in Predictive Analytics and Machine Learning in Marketing
  • 5.1.4 Misconceptions in Predictive Analytics and Machine Learning in Marketing
  • 5.2 Predictive Analytics Techniques
  • 5.2.1 Linear and Logistic Regression
  • 5.2.2 Time Series Forecasting
  • 5.3 Machine Learning Techniques
  • 5.3.1 Supervised Learning for Marketing
  • 5.3.2 Unsupervised Learning for Marketing
  • 5.3.3 Reinforcement Learning for Marketing
  • 5.4 Model Evaluation and Selection
  • 5.4.1 Model Accuracy, Precision, and Recall
  • 5.4.2 ROC Curves and AUC
  • 5.4.3 Cross-Validation Techniques
  • 5.4.4 Model Complexity and Overfitting
  • 5.5 Churn Prediction, Customer Lifetime Value, and Propensity Modeling
  • 5.5.1 Understanding Churn and Its Importance
  • 5.5.2 CLV Computation and Applications.
  • 5.5.3 Propensity Scoring and Its Marketing Applications
  • 5.6 Market Basket Analysis and Recommender Systems
  • 5.6.1 Principles of Association Rules
  • 5.6.2 Apriori Algorithm and Market Basket Analysis
  • 5.6.3 Collaborative Filtering in Recommender Systems
  • 5.6.4 Content-Based and Hybrid Recommendation Systems
  • 5.7 Practical Examples: Predictive Analytics and Machine Learning in Marketing
  • 5.7.1 Predicting Customer Churn with Logistic Regression
  • 5.7.2 Sales Forecasting with Time Series Models
  • 5.7.3 Customer Segmentation with Clustering
  • 5.7.4 Personalized Recommendation with Collaborative Filtering
  • 5.7.5 Dynamic Pricing with Reinforcement Learning
  • 5.8 Conclusion
  • 5.9 References
  • Chapter 6 Natural Language Processing in Marketing
  • 6.0 Beginner-Friendly Introduction to Natural Language Processing in Marketing
  • 6.0.1 What Is Natural Language Processing, and Why Should Marketers Care?
  • 6.0.2 Simple Analogy: The Recipe of Language
  • 6.0.3 Natural Language Processing in Everyday Marketing
  • 6.0.4 Let's Dive Deeper
  • 6.1 Introduction to Natural Language Processing
  • 6.1.1 Overview of Natural Language Processing
  • 6.1.2 Importance of Natural Language Processing in Marketing
  • 6.1.3 Components of Natural Language Processing: Syntax, Semantics, and Pragmatics
  • 6.1.4 Challenges in Natural Language Processing
  • 6.2 Text Preprocessing and Feature Extraction in Marketing Natural Language Processing
  • 6.2.1 Tokenization and Stemming
  • 6.2.2 Stop Word Removal
  • 6.2.3 Vectorization: Bag of Words and TF-IDF
  • 6.2.4 Word Embeddings: Word2Vec, GloVe
  • 6.3 Key Natural Language Processing Techniques for Marketing
  • 6.3.1 Text Analytics
  • 6.3.2 Sentiment Analysis
  • 6.3.3 Topic Modeling
  • 6.3.4 Named Entity Recognition
  • 6.3.5 Text Classification
  • 6.4 Chatbots and Voice Assistants in Marketing.
  • 6.4.1 Evolution and Importance of Chatbots
  • 6.4.2 Designing Effective Chatbots: Best Practices
  • 6.4.3 Voice Assistants and Voice Search
  • 6.5 Practical Examples of Natural Language Processing in Marketing
  • 6.5.1 Social Media Sentiment Analysis
  • 6.5.2 Chatbots for Customer Service
  • 6.5.3 Personalized Marketing Communications
  • 6.6 Conclusion
  • 6.7 References
  • Chapter 7 Social Media Analytics and Web Analytics
  • 7.1 Introduction
  • 7.2 Social Network Analysis
  • 7.2.1 Overview of Social Network Analysis
  • 7.2.2 Basics of Network Theory
  • 7.2.3 Metrics: Centrality, Density, and Clusters
  • 7.2.4 Influencer Identification and Engagement Strategies
  • 7.2.5 Community Detection in Social Networks
  • 7.2.6 Key Concepts in Social Network Analysis
  • 7.2.7 Practical Example: Social Network Analysis in Influencer Marketing
  • 7.3 Web Analytics Tools and Metrics
  • 7.3.1 Overview of Web Analytics Tools and Metrics
  • 7.3.2 Key Metrics: Page Views, Bounce Rate, and Conversion Rate
  • 7.3.3 Advanced Metrics: Funnel Analysis and Cohort Analysis
  • 7.3.4 Integration with Other Data Sources
  • 7.3.5 Key Concepts in Web Analytics
  • 7.3.6 Challenges in Interpreting Web Analytics Data
  • 7.3.7 Practical Example: Using Google Analytics for Customer Behavior Insights
  • 7.4 Social Media Listening and Tracking
  • 7.4.1 Overview of Social Media Listening and Tracking
  • 7.4.2 The Importance of Social Listening in Modern Marketing
  • 7.4.3 Analyzing Social Mentions and Share of Voice
  • 7.4.4 Crisis Management Through Social Listening
  • 7.4.5 Key Concepts in Social Media Listening and Tracking
  • 7.4.6 Practical Example: Social Media Listening for Brand Reputation Management
  • 7.5 Conversion Rate Optimization
  • 7.5.1 Overview of Conversion Rate Optimization
  • 7.5.2 A/B Testing for Landing Pages
  • 7.5.3 User Experience Best Practices for Conversions.
  • 7.5.4 Analyzing and Iterating Conversion Rate Optimization Strategies.

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