CHAPTER 1. INTRODUCTION
-- 1.1 The Subject of Algorithmic Marketing
-- 1.2 The Definition of Algorithmic Marketing
-- 1.3 Historical Backgrounds and Context
-- -- 1.3.1 Online Advertising: Services and Exchanges
-- -- 1.3.2 Airlines: Revenue Management
-- -- 1.3.3 Marketing Science
-- 1.4 Programmatic Services
-- 1.5 Who Should Read This Book?
-- 1.6 Summary

CHAPTER 2. REVIEW OF PREDICTIVE MODELING
-- 2.1 Descriptive, Predictive, and Prescriptive Analytics
-- 2.2 Economic Optimization
-- 2.3 Machine Learning
-- 2.4 Supervised Learning
-- -- 2.4.1 Parametric and Nonparametric Models
-- -- 2.4.2 Maximum Likelihood Estimation
-- -- 2.4.3 Linear Models
-- -- -- 2.4.3.1 Linear Regression
-- -- -- 2.4.3.2 Logistic Regression and Binary Classification
-- -- -- 2.4.3.3 Logistic Regression and Multinomial Classification
-- -- -- 2.4.3.4 Naive Bayes Classifier
-- -- 2.4.4 Nonlinear Models
-- -- -- 2.4.4.1 Feature Mapping and Kernel Methods
-- -- -- 2.4.4.2 Adaptive Basis and Decision Trees
-- 2.5 Representation Learning
-- -- 2.5.1 Principal Component Analysis
-- -- -- 2.5.1.1 Decorrelation
-- -- -- 2.5.1.2 Dimensionality Reduction
-- -- 2.5.2 Clustering
-- 2.6 More Specialized Models
-- -- 2.6.1 Consumer Choice Theory
-- -- -- 2.6.1.1 Multinomial Logit Model
-- -- -- 2.6.1.2 Estimation of the Multinomial Logit Model
-- -- 2.6.2 Survival Analysis
-- -- -- 2.6.2.1 Survival Function
-- -- -- 2.6.2.2 Hazard Function
-- -- -- 2.6.2.3 Survival Analysis Regression
-- -- 2.6.3 Auction Theory
-- 2.7 Summary

CHAPTER 3. PROMOTIONS AND ADVERTISEMENTS
-- 3.1 Environment
-- 3.2 Business Objectives
-- -- 3.2.1 Manufacturers and Retailers
-- -- 3.2.2 Costs
-- -- 3.2.3 Gains
-- 3.3 Targeting Pipeline
-- 3.4 Response Modeling and Measurement
-- -- 3.4.1 Response Modeling Framework
-- -- 3.4.2 Response Measurement
-- 3.5 Building Blocks: Targeting and LTV Models
-- -- 3.5.1 Data Collection
-- -- 3.5.2 Tiered Modeling
-- -- 3.5.3 RFM Modeling
-- -- 3.5.4 Propensity Modeling
-- -- -- 3.5.4.1 Look-alike Modeling
-- -- -- 3.5.4.2 Response and Uplift Modeling
-- -- 3.5.5 Segmentation and Persona-based Modeling
-- -- 3.5.6 Targeting by using Survival Analysis
-- -- 3.5.7 Lifetime Value Modeling
-- -- -- 3.5.7.1 Descriptive Analysis
-- -- -- 3.5.7.2 Markov Chain Models
-- -- -- 3.5.7.3 Regression Models
-- 3.6 Designing and Running Campaigns
-- -- 3.6.1 Customer Journeys
-- -- 3.6.2 Product Promotion Campaigns
-- -- -- 3.6.2.1 Targeting Process
-- -- -- 3.6.2.2 Budgeting and Capping
-- -- 3.8.4 Multi-Touch Attribution
-- 3.9 Measuring the Effectiveness
-- -- 3.9.1 Randomized Experiments
-- -- -- 3.9.1.1 Conversion Rate
-- -- -- 3.9.1.2 Uplift
-- -- 3.9.2 Observational Studies
-- -- -- 3.9.2.1 Model Specification
-- -- -- 3.9.2.2 Simulation
-- 3.10 Architecture of Targeting Systems
-- -- 3.10.1 Targeting Server
-- -- 3.10.2 Data Management Platform
-- -- 3.10.3 Analytics Platform
-- 3.11 Summary

CHAPTER 4. SEARCH
-- 4.1 Environment
-- 4.2 Business Objectives
-- -- 4.2.1 Relevance Metrics
-- -- 4.2.2 Merchandising Controls
-- -- 4.2.3 Service Quality Metrics
-- 4.3 Building Blocks: Matching and Ranking
-- -- 4.3.1 Token Matching
-- -- 4.3.2 Boolean Search and Phrase Search
-- -- 4.3.3 Normalization and Stemming
-- -- 4.3.4 Ranking and the Vector Space Model
-- -- 4.3.5 TFIDF Scoring Model
-- -- 4.3.6 Scoring with n-grams
-- 4.4 Mixing Relevance Signals
-- -- 4.4.1 Searching Multiple Fields
-- -- 4.4.2 Signal Engineering and Equalization
-- -- -- 4.4.2.1 One Strong Signal
-- -- -- 4.4.2.2 Strong Average Signal
-- -- -- 4.4.2.3 Fragmented Features and Signals
-- -- 4.4.3 Designing a Signal Mixing Pipeline
-- 4.5 Semantic Analysis
-- -- 4.5.1 Synonyms and Hierarchies
-- -- 4.5.2 Word Embedding
-- -- 4.5.3 Latent Semantic Analysis
-- -- 4.5.4 Probabilistic Topic Modeling
-- -- 4.5.5 Probabilistic Latent Semantic Analysis
-- -- -- 4.5.5.1 Latent Variable Model
-- -- -- 4.5.5.2 Matrix Factorization
-- -- -- 4.5.5.3 pLSA Properties
-- -- 4.5.6 Latent Dirichlet Allocation
-- -- 4.5.7 Word2Vec Model
-- 4.6 Search Methods for Merchandising
-- -- 4.6.1 Combinatorial Phrase Search
-- -- 4.6.2 Controlled Precision Reduction
-- -- 4.6.3 Nested Entities and Dynamic Grouping
-- 4.7 Relevance Tuning
-- -- 4.7.1 Learning to Rank
-- -- 4.7.2 Learning to Rank from Implicit Feedback
-- 4.8 Architecture of Merchandising Search Services
-- 4.9 Summary

CHAPTER 5. RECOMMENDATIONS
-- 5.1 Environment
-- -- 5.1.1 Properties of Customer Ratings
-- 5.2 Business Objectives
-- 5.3 Quality Evaluation
-- -- 5.3.1 Prediction Accuracy
-- -- 5.3.2 Ranking Accuracy
-- -- 5.3.3 Novelty
-- -- 5.3.4 Serendipity
-- -- 5.3.5 Diversity
-- -- 5.3.6 Coverage
-- -- 5.3.7 The Role of Experimentation
-- 5.4 Overview of Recommendation Methods
-- 5.5 Content-based Filtering
-- -- 5.5.1 Nearest Neighbor Approach
-- -- 5.5.2 Naive Bayes Classifier
-- -- 5.5.3 Feature Engineering for Content Filtering
-- 5.6 Introduction to Collaborative Filtering
-- -- 5.6.1 Baseline Estimates
-- 5.7 Neighborhood-based Collaborative Filtering
-- -- 5.7.1 User-based Collaborative Filtering
-- -- 5.7.2 Item-based Collaborative Filtering
-- -- 5.7.3 Comparison of User-based and Item-based Methods
-- -- 5.7.4 Neighborhood Methods as a Regression Problem
-- -- -- 5.7.4.1 Item-based Regression
-- -- -- 5.7.4.2 User-based Regression
-- -- -- 5.7.4.3 Fusing Item-based and User-based Models
-- 5.8 Model-based Collaborative Filtering
-- -- 5.8.1 Adapting Regression Models to Rating Prediction
-- -- 5.8.2 Naive Bayes Collaborative Filtering
-- -- 5.8.3 Latent Factor Models
-- -- -- 5.8.3.1 Unconstrained Factorization
-- -- -- 5.8.3.2 Constrained Factorization
-- -- -- 5.8.3.3 Advanced Latent Factor Models
-- 5.9 Hybrid Methods
-- -- 5.9.1 Switching
-- -- 5.9.2 Blending
-- -- -- 5.9.2.1 Blending with Incremental Model Training
-- -- -- 5.9.2.2 Blending with Residual Training
-- -- -- 5.9.2.3 Feature-weighted Blending
-- -- 5.9.3 Feature Augmentation
-- -- 5.9.4 Presentation Options for Hybrid Recommendations
-- 5.10 Contextual Recommendations
-- -- 5.10.1 Multidimensional Framework
-- -- 5.10.2 Context-Aware Recommendation Techniques
-- -- 5.10.3 Time-Aware Recommendation Models
-- -- -- 5.10.3.1 Baseline Estimates with Temporal Dynamics
-- -- -- 5.10.3.2 Neighborhood Model with Time Decay
-- -- -- 5.10.3.3 Latent Factor Model with Temporal Dynamics
-- 5.11 Non-Personalized Recommendations
-- -- 5.11.1 Types of Non-Personalized Recommendations
-- -- 5.11.2 Recommendations by Using Association Rules
-- 5.12 Multiple Objective Optimization
-- 5.13 Architecture of Recommender Systems
-- 5.14 Summary

CHAPTER 6. PRICING AND ASSORTMENT
-- 6.1 Environment
-- 6.2 The Impact of Pricing
-- 6.3 Price and Value
-- -- 6.3.1 Price Boundaries
-- -- 6.3.2 Perceived Value
-- 6.4 Price and Demand
-- -- 6.4.1 Linear Demand Curve
-- -- 6.4.2 Constant-Elasticity Demand Curve
-- -- 6.4.3 Logit Demand Curve
-- 6.5 Basic Price Structures
-- -- 6.5.1 Unit Price
-- -- 6.5.2 Market Segmentation
-- -- 6.5.3 Multipart Pricing
-- -- 6.5.4 Bundling
-- 6.6 Demand Prediction
-- -- 6.6.1 Demand Model for Assortment Optimization
-- -- 6.6.2 Demand Model for Seasonal Sales
-- -- -- 6.6.2.1 Demand Data Preparation
-- -- -- 6.6.2.2 Model Specification
-- -- 6.6.3 Demand Prediction with Stockouts
-- 6.7 Price Optimization
-- -- 6.7.1 Price Differentiation
-- -- -- 6.7.1.1 Differentiation with Demand Shifting
-- -- -- 6.7.1.2 Differentiation with Constrained Supply
-- -- 6.7.2 Dynamic Pricing
-- -- -- 6.7.2.1 Markdowns and Clearance Sales
-- -- -- 6.7.2.2 Markdown Price Optimization
-- -- -- 6.7.2.3 Price Optimization for Competing Products
-- -- 6.7.3 Personalized Discounts
-- 6.8 Resource Allocation
-- -- 6.8.1 Environment
-- -- 6.8.2 Allocation with Two Classes
-- -- 6.8.3 Allocation with Multiple Classes
-- -- 6.8.4 Heuristics for Multiple Classes
-- -- -- 6.8.4.1 EMSRa
-- -- -- 6.8.4.2 EMSRb
-- 6.9 Assortment Optimization
-- -- 6.9.1 Store-Layout Optimization
-- -- 6.9.2 Category Management
-- 6.10 Architecture of Price Management Systems
-- 6.11 Summary

A APPENDIX: Dirichlet Distribution

INDEX

BIBLIOGRAPHY

BACK TO BOOK