Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations

Contents
Acknowledgements
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
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
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
		3.6.3 Multistage Promotion Campaigns
		3.6.4 Retention Campaigns
		3.6.5 Replenishment Campaigns
	3.7 Resource Allocation
		3.7.1 Allocation by Channel
		3.7.2 Allocation by Objective
	3.8 Online Advertisements
		3.8.1 Environment
		3.8.2 Objectives and Attribution
		3.8.3 Targeting for the CPA-LT Model
			3.8.3.1 Brand Proximity
			3.8.3.2 Ad Response Modeling
			3.8.3.3 Inventory Quality and Bidding
		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
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
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
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