Key features

• •The Subject of Algorithmic Marketing
• •The Definition of Algorithmic Marketing
• •Historical Backgrounds and Context
• •Programmatic Services
• •Who Should Read This Book?
• •Summary
• •Descriptive, Predictive, and Prescriptive Analytics
• •Economic Optimization
• •Machine Learning
• •Supervised Learning
• •Representation Learning
• •More Specialized Models
• •Summary
• •Environment
• •Business Objectives
• •Targeting Pipeline
• •Response Modeling and Measurement
• •Building Blocks: Targeting and LTV Models
• •Designing and Running Campaigns
• •Resource Allocation
• •Online Advertisements
• •Measuring the Effectiveness
• •Architecture of Targeting Systems
• •Summary
• •Environment
• •Business Objectives
• •Building Blocks: Matching and Ranking
• •Mixing Relevance Signals
• •Semantic Analysis
• •Search Methods for Merchandising
• •Relevance Tuning
• •Architecture of Merchandising Search Services
• •Summary
• •Environment
• •Business Objectives
• •Quality Evaluation
• •Overview of Recommendation Methods
• •Content-based Filtering
• •Introduction to Collaborative Filtering
• •Neighborhood-based Collaborative Filtering
• •Model-based Collaborative Filtering
• •Hybrid Methods
• •Contextual Recommendations
• •Non-Personalized Recommendations
• •Multiple Objective Optimization
• •Architecture of Recommender Systems
• •Summary
• •Environment
• •The Impact of Pricing
• •Price and Value
• •Price and Demand
• •Basic Price Structures
• •Demand Prediction
• •Price Optimization
• •Resource Allocation
• •Assortment Optimization
• •Architecture of Price Management Systems
• •Summary